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40 Detailed Artificial Intelligence Case Studies [2024]

In this dynamic era of technological advancements, Artificial Intelligence (AI) emerges as a pivotal force, reshaping the way industries operate and charting new courses for business innovation. This article presents an in-depth exploration of 40 diverse and compelling AI case studies from across the globe. Each case study offers a deep dive into the challenges faced by companies, the AI-driven solutions implemented, their substantial impacts, and the valuable lessons learned. From healthcare and finance to transportation and retail, these stories highlight AI’s transformative power in solving complex problems, optimizing processes, and driving growth, offering insightful glimpses into the potential and versatility of AI in shaping our world.

Related: How to Become an AI Thought Leader?

1. IBM Watson Health: Revolutionizing Patient Care with AI

Task/Conflict: The healthcare industry faces challenges in handling vast amounts of patient data, accurately diagnosing diseases, and creating effective treatment plans. IBM Watson Health aimed to address these issues by harnessing AI to process and analyze complex medical information, thus improving the accuracy and efficiency of patient care.

Solution: Utilizing the cognitive computing capabilities of IBM Watson, this solution involves analyzing large volumes of medical records, research papers, and clinical trial data. The system uses natural language processing to understand and process medical jargon, making sense of unstructured data to aid medical professionals in diagnosing and treating patients.

Overall Impact:

• Enhanced accuracy in patient diagnosis and treatment recommendations.
• Significant improvement in personalized healthcare services.

Key Learnings:

• AI can complement medical professionals’ expertise, leading to better healthcare outcomes.
• The integration of AI in healthcare can lead to significant advancements in personalized medicine.

2. Google DeepMind’s AlphaFold: Unraveling the Mysteries of Protein Folding

Task/Conflict: The scientific community has long grappled with the protein folding problem – understanding how a protein’s amino acid sequence determines its 3D structure. Solving this problem is crucial for drug discovery and understanding diseases at a molecular level, yet it remained a formidable challenge due to the complexity of biological structures.

Solution: AlphaFold, developed by Google DeepMind, is an AI model trained on vast datasets of known protein structures. It assesses the distances and angles between amino acids to predict how a protein folds, outperforming existing methods in terms of speed and accuracy. This breakthrough represents a major advancement in computational biology.

• Significant acceleration in drug discovery and disease understanding.
• Set a new benchmark for computational methods in biology.
• AI’s predictive power can solve complex biological problems.
• The application of AI in scientific research can lead to groundbreaking discoveries.

3. Amazon: Transforming Supply Chain Management through AI

Task/Conflict: Managing a global supply chain involves complex challenges like predicting product demand, optimizing inventory levels, and streamlining logistics. Amazon faced the task of efficiently managing its massive inventory while minimizing costs and meeting customer demands promptly.

Solution: Amazon employs sophisticated AI algorithms for predictive inventory management, which forecast product demand based on various factors like buying trends, seasonality, and market changes. This system allows for real-time adjustments, adapting swiftly to changing market dynamics.

• Reduced operational costs through efficient inventory management.
• Improved customer satisfaction with timely deliveries and availability.
• AI can significantly enhance supply chain efficiency and responsiveness.
• Predictive analytics in inventory management leads to reduced waste and cost savings.

4. Tesla’s Autonomous Vehicles: Driving the Future of Transportation

Task/Conflict: The development of autonomous vehicles represents a major technological and safety challenge. Tesla aimed to create self-driving cars that are not only reliable and safe but also capable of navigating complex traffic conditions without human intervention.

Solution: Tesla’s solution involves advanced AI and machine learning algorithms that process data from various sensors and cameras to understand and navigate the driving environment. Continuous learning from real-world driving data allows the system to improve over time, making autonomous driving safer and more efficient.

• Leadership in the autonomous vehicle sector, enhancing road safety.
• Continuous improvements in self-driving technology through AI-driven data analysis.
• Continuous data analysis is key to advancing autonomous driving technologies.
• AI can significantly improve road safety and driving efficiency.

Related: High-Paying AI Career Options

5. Zara: Fashioning the Future with AI in Retail

Task/Conflict: In the fast-paced fashion industry, predicting trends and managing inventory efficiently are critical for success. Zara faced the challenge of quickly adapting to changing fashion trends while avoiding overstock and meeting consumer demand.

Solution: Zara employs AI algorithms to analyze fashion trends, customer preferences, and sales data. The AI system also assists in managing inventory, ensuring that popular items are restocked promptly and that stores are not overburdened with unsold products. This approach optimizes both production and distribution.

• Increased sales and profitability through optimized inventory.
• Enhanced customer satisfaction by aligning products with current trends.
• AI can accurately predict consumer behavior and trends.
• Effective inventory management through AI can significantly impact business success.

6. Netflix: Personalizing Entertainment with AI

Task/Conflict: In the competitive streaming industry, providing a personalized user experience is key to retaining subscribers. Netflix needed to recommend relevant content to each user from its vast library, ensuring that users remained engaged and satisfied.

Solution: Netflix developed an advanced AI-driven recommendation engine that analyzes individual viewing habits, ratings, and preferences. This personalized approach keeps users engaged, as they are more likely to find content that interests them, enhancing their overall viewing experience.

• Increased viewer engagement and longer watch times.
• Higher subscription retention rates due to personalized content.
• Personalized recommendations significantly enhance user experience.
• AI-driven content curation is essential for success in digital entertainment.

7. Airbus: Elevating Aircraft Maintenance with AI

Task/Conflict: Aircraft maintenance is crucial for ensuring flight safety and operational efficiency. Airbus faced the challenge of predicting maintenance needs to prevent equipment failures and reduce downtime, which is critical in the aviation industry.

Solution: Airbus implemented AI algorithms for predictive maintenance, analyzing data from aircraft sensors to identify potential issues before they lead to failures. This system assesses the condition of various components, predicting when maintenance is needed. The solution not only enhances safety but also optimizes maintenance schedules, reducing unnecessary inspections and downtime.

• Decreased maintenance costs and reduced aircraft downtime.
• Improved safety with proactive maintenance measures.
• AI can predict and prevent potential equipment failures.
• Predictive maintenance is essential for operational efficiency and safety in aviation.

8. American Express: Securing Transactions with AI

Task/Conflict: Credit card fraud is a significant issue in the financial sector, leading to substantial losses and undermining customer trust. American Express needed an efficient way to detect and prevent fraudulent transactions in real-time.

Solution: American Express utilizes machine learning models to analyze transaction data. These models identify unusual patterns and behaviors indicative of fraud. By constant learning from refined data, the system becomes increasingly accurate in detecting fraudulent activities, providing real-time alerts and preventing unauthorized transactions.

• Minimized financial losses due to reduced fraudulent activities.
• Enhanced customer trust and security in financial transactions.
• Machine learning is highly effective in fraud detection.
• Real-time data analysis is crucial for preventing financial fraud.

Related: Is AI a Good Career Option for Women?

9. Stitch Fix: Tailoring the Future of Fashion Retail

Task/Conflict: In the competitive fashion retail industry, providing a personalized shopping experience is key to customer satisfaction and business growth. Stitch Fix aimed to offer customized clothing selections to each customer, based on their unique preferences and style.

Solution: Stitch Fix uses AI and algorithms analyze customer feedback, style preferences, and purchase history to recommend clothing items. This personalized approach is complemented by human stylists, ensuring that each customer receives a tailored selection that aligns with their individual style.

• Increased customer satisfaction through personalized styling services.
• Business growth driven by a unique, AI-enhanced shopping experience.
• AI combined with human judgment can create highly effective personalization.
• Tailoring customer experiences using AI leads to increased loyalty and business success.

10. Baidu: Breaking Language Barriers with Voice Recognition

Task/Conflict: Voice recognition technology faces the challenge of accurately understanding and processing speech in various languages and accents. Baidu aimed to enhance its voice recognition capabilities to provide more accurate and user-friendly interactions in multiple languages.

Solution: Baidu employs deep learning algorithms for voice and speech recognition, training its system on a diverse range of languages and dialects. This approach allows for more accurate recognition of speech patterns, enabling the technology to understand and respond to voice commands more effectively. The system continuously improves as it processes more voice data, making technology more accessible to users worldwide.

• Enhanced user interaction with technology in multiple languages.
• Reduced language barriers in voice-activated services and devices.
• AI can effectively bridge language gaps in technology.
• Continuous learning from diverse data sets is key to improving voice recognition.

11. JP Morgan: Revolutionizing Legal Document Analysis with AI

Task/Conflict: Analyzing legal documents, such as contracts, is a time-consuming and error-prone process. JP Morgan sought to streamline this process, reducing the time and effort required while increasing accuracy.

Solution: JP Morgan implemented an AI-powered tool, COIN (Contract Intelligence), to analyze legal documents quickly and accurately. COIN uses NLP to interpret and extract relevant information from contracts, significantly reducing the time required for document review.

• Dramatic reduction in time required for legal document analysis.
• Increased accuracy and reduced human error in contract interpretation.
• AI can efficiently handle large volumes of data, offering speed and accuracy.
• Automation in legal processes can significantly enhance operational efficiency.

12. Microsoft: AI for Accessibility

Task/Conflict: People with disabilities often face challenges in accessing technology. Microsoft aimed to create AI-driven tools to enhance accessibility, especially for individuals with visual, hearing, or cognitive impairments.

Solution: Microsoft developed a range of AI-powered tools including applications for voice recognition, visual assistance, and cognitive support, making technology more accessible and user-friendly. For instance, Seeing AI, an app developed by Microsoft, helps visually impaired users to understand their surroundings by describing people, texts, and objects.

• Improved accessibility and independence for people with disabilities.
• Creation of more inclusive technology solutions.
• AI can significantly contribute to making technology accessible for all.
• Developing inclusive technology is essential for societal progress.

Related: How to get an Internship in AI?

13. Alibaba’s City Brain: Revolutionizing Urban Traffic Management

Task/Conflict: Urban traffic congestion is a major challenge in many cities, leading to inefficiencies and environmental concerns. Alibaba’s City Brain project aimed to address this issue by using AI to optimize traffic flow and improve public transportation in urban areas.

Solution: City Brain uses AI to analyze real-time data from traffic cameras, sensors, and GPS systems. It processes this information to predict traffic patterns and optimize traffic light timing, reducing congestion. The system also provides data-driven insights for urban planning and emergency response coordination, enhancing overall city management.

• Significant reduction in traffic congestion and improved urban transportation.
• Enhanced efficiency in city management and emergency response.
• AI can effectively manage complex urban systems.
• Data-driven solutions are key to improving urban living conditions.

14. Deep 6 AI: Accelerating Clinical Trials with Artificial Intelligence

Task/Conflict: Recruiting suitable patients for clinical trials is often a slow and cumbersome process, hindering medical research. Deep 6 AI sought to accelerate this process by quickly identifying eligible participants from a vast pool of patient data.

Solution: Deep 6 AI employs AI to sift through extensive medical records, identifying potential trial participants based on specific criteria. The system analyzes structured and unstructured data, including doctor’s notes and diagnostic reports, to find matches for clinical trials. This approach significantly speeds up the recruitment process, enabling faster trial completions and advancements in medical research.

• Quicker recruitment for clinical trials, leading to faster research progress.
• Enhanced efficiency in medical research and development.
• AI can streamline the patient selection process for clinical trials.
• Efficient recruitment is crucial for the advancement of medical research.

15. NVIDIA: Revolutionizing Gaming Graphics with AI

Task/Conflict: Enhancing the realism and performance of gaming graphics is a continuous challenge in the gaming industry. NVIDIA aimed to revolutionize gaming visuals by leveraging AI to create more realistic and immersive gaming experiences.

Solution: NVIDIA’s AI-driven graphic processing technologies, such as ray tracing and deep learning super sampling (DLSS), provide highly realistic and detailed graphics. These technologies use AI to render images more efficiently, improving game performance without compromising on visual quality. This innovation sets new standards in gaming graphics, making games more lifelike and engaging.

• Elevated gaming experiences with state-of-the-art graphics.
• Set new industry standards for graphic realism and performance.
• AI can significantly enhance creative industries, like gaming.
• Balancing performance and visual quality is key to gaming innovation.

16. Palantir: Mastering Data Integration and Analysis with AI

Task/Conflict: Integrating and analyzing large-scale, diverse datasets is a complex task, essential for informed decision-making in various sectors. Palantir Technologies faced the challenge of making sense of vast amounts of data to provide actionable insights for businesses and governments.

Solution: Palantir developed AI-powered platforms that integrate data from multiple sources, providing a comprehensive view of complex systems. These platforms use machine learning to analyze data, uncover patterns, and predict outcomes, assisting in strategic decision-making. This solution enables users to make informed decisions in real-time, based on a holistic understanding of their data.

• Enhanced decision-making capabilities in complex environments.
• Greater insights and efficiency in data analysis across sectors.
• Effective data integration is crucial for comprehensive analysis.
• AI-driven insights are essential for strategic decision-making.

Related: Surprising AI Facts & Statistics

17. Blue River Technology: Sowing the Seeds of AI in Agriculture

Task/Conflict: The agriculture industry faces challenges in increasing efficiency and sustainability while minimizing environmental impact. Blue River Technology aimed to enhance agricultural practices by using AI to make farming more precise and efficient.

Solution: Blue River Technology developed AI-driven agricultural robots that perform tasks like precise planting and weed control. These robots use ML to identify plants and make real-time decisions, such as applying herbicides only to weeds. This targeted approach reduces chemical usage and promotes sustainable farming practices, leading to better crop yields and environmental conservation.

• Significant reduction in chemical usage in farming.
• Increased crop yields through precision agriculture.
• AI can contribute significantly to sustainable agricultural practices.
• Precision farming is key to balancing productivity and environmental conservation.

18. Salesforce: Enhancing Customer Relationship Management with AI

Task/Conflict: In the realm of customer relationship management (CRM), personalizing interactions and gaining insights into customer behavior are crucial for business success. Salesforce aimed to enhance CRM capabilities by integrating AI to provide personalized customer experiences and actionable insights.

Solution: Salesforce incorporates AI-powered tools into its CRM platform, enabling businesses to personalize customer interactions, automate responses, and predict customer needs. These tools analyze customer data, providing insights that help businesses tailor their strategies and communications. The AI integration not only improves customer engagement but also streamlines sales and marketing efforts.

• Improved customer engagement and satisfaction.
• Increased business growth through tailored marketing and sales strategies.
• AI-driven personalization is key to successful customer relationship management.
• Leveraging AI for data insights can significantly impact business growth.

19. OpenAI: Transforming Natural Language Processing

Task/Conflict: OpenAI aimed to advance NLP by developing models capable of generating coherent and contextually relevant text, opening new possibilities in AI-human interaction.

Solution: OpenAI developed the Generative Pre-trained Transformer (GPT) models, which use deep learning to generate text that closely mimics human language. These models are trained on vast datasets, enabling them to understand context and generate responses in a conversational and coherent manner.

• Pioneered advancements in natural language understanding and generation.
• Expanded the possibilities for AI applications in communication.
• AI’s ability to mimic human language has vast potential applications.
• Advancements in NLP are crucial for improving AI-human interactions.

20. Siemens: Pioneering Industrial Automation with AI

Task/Conflict: Industrial automation seeks to improve productivity and efficiency in manufacturing processes. Siemens faced the challenge of optimizing these processes using AI to reduce downtime and enhance output quality.

Solution: Siemens employs AI-driven solutions for predictive maintenance and process optimization to reduce downtime in industrial settings. Additionally, AI optimizes manufacturing processes, ensuring quality and efficiency.

• Increased productivity and reduced downtime in industrial operations.
• Enhanced quality and efficiency in manufacturing processes.
• AI is a key driver in the advancement of industrial automation.
• Predictive analytics are crucial for maintaining efficiency in manufacturing.

Related: Top Books for Learning AI

21. Ford: Driving Safety Innovation with AI

Task/Conflict: Enhancing automotive safety and providing effective driver assistance systems are critical challenges in the auto industry. Ford aimed to leverage AI to improve vehicle safety features and assist drivers in real-time decision-making.

Solution: Ford integrated AI into its advanced driver assistance systems (ADAS) to provide features like adaptive cruise control, lane-keeping assistance, and collision avoidance. These systems use sensors and cameras to gather data, which AI processes to make split-second decisions that enhance driver safety and vehicle performance.

• Improved safety features in vehicles, minimizing accidents and improving driver confidence.
• Enhanced driving experience with intelligent assistance features.
• AI can highly enhance safety in the automotive industry.
• Real-time data processing and decision-making are essential for effective driver assistance systems.

22. HSBC: Enhancing Banking Security with AI

Task/Conflict: As financial transactions increasingly move online, banks face heightened risks of fraud and cybersecurity threats. HSBC needed to bolster its protective measures to secure user data and prevent scam.

Solution: HSBC employed AI-driven security systems to observe transactions and identify suspicious activities. The AI models analyze patterns in customer behavior and flag anomalies that could indicate fraudulent actions, allowing for immediate intervention. This helps in minimizing the risk of financial losses and protects customer trust.

• Strengthened security measures and reduced incidence of fraud.
• Maintained high levels of customer trust and satisfaction.
• AI is critical in enhancing security in the banking sector.
• Proactive fraud detection can prevent significant financial losses.

23. Unilever: Optimizing Supply Chain with AI

Task/Conflict: Managing a global supply chain involves complexities related to logistics, demand forecasting, and sustainability practices. Unilever sought to enhance its supply chain efficiency while promoting sustainability.

Solution: Unilever implemented AI to optimize its supply chain operations, from raw material sourcing to distribution. AI algorithms analyze data to forecast demand, improve inventory levels, and minimize waste. Additionally, AI helps in selecting sustainable practices and suppliers, aligning with Unilever’s commitment to environmental responsibility.

• Enhanced efficiency and reduced costs in supply chain operations.
• Better sustainability practices, reducing environmental impact.
• AI can highly optimize supply chain management.
• Integrating AI with sustainability initiatives can lead to environmentally responsible operations.

24. Spotify: Personalizing Music Experience with AI

Task/Conflict: In the competitive music streaming industry, providing a personalized listening experience is crucial for user engagement and retention. Spotify needed to tailor music recommendations to individual tastes and preferences.

Solution: Spotify utilizes AI-driven algorithms to analyze user listening habits, preferences, and contextual data to recommend music tracks and playlists. This personalization ensures that users are continually engaged and discover new music that aligns with their tastes, enhancing their overall listening experience.

• Increased customer engagement and time spent on the platform.
• Higher user satisfaction and subscription retention rates.
• Personalized content delivery is key to user retention in digital entertainment.
• AI-driven recommendations significantly enhance user experience.

Related: How can AI be used in Instagram Marketing?

25. Walmart: Revolutionizing Retail with AI

Task/Conflict: Retail giants like Walmart face challenges in inventory management and providing a high-quality customer service experience. Walmart aimed to use AI to optimize these areas and enhance overall operational efficacy.

Solution: Walmart deployed AI technologies across its stores to manage inventory levels effectively and enhance customer service. AI systems predict product demand to optimize stock levels, while AI-driven robots assist in inventory management and customer service, such as guiding customers in stores and handling queries.

• Improved inventory management, reducing overstock and shortages.
• Enhanced customer service experience in stores.
• AI can streamline retail operations significantly.
• Enhanced customer service through AI leads to better customer satisfaction.

26. Roche: Innovating Drug Discovery with AI

Task/Conflict: The pharmaceutical industry faces significant challenges in drug discovery, requiring vast investments of time and resources. Roche aimed to utilize AI to streamline the drug development process and enhance the discovery of new therapeutics.

Solution: Roche implemented AI to analyze medical data and simulate drug interactions, speeding up the drug discovery process. AI models predict the effectiveness of compounds and identify potential candidates for further testing, significantly minimizing the time and cost related with traditional drug development procedures.

• Accelerated drug discovery processes, bringing new treatments to market faster.
• Reduced costs and increased efficiency in pharmaceutical research.
• AI can greatly accelerate the drug discovery process.
• Cost-effective and efficient drug development is possible with AI integration.

27. IKEA: Enhancing Customer Experience with AI

Task/Conflict: In the competitive home furnishings market, enhancing the customer shopping experience is crucial for success. IKEA aimed to use AI to provide innovative design tools and improve customer interaction.

Solution: IKEA introduced AI-powered tools such as virtual reality apps that allow consumers to visualize furniture before buying. These tools help customers make more informed decisions and enhance their shopping experience. Additionally, AI chatbots assist with customer service inquiries, providing timely and effective support.

• Improved customer decision-making and satisfaction with interactive tools.
• Enhanced efficiency in customer service.
• AI can transform the retail experience by providing innovative customer interaction tools.
• Effective customer support through AI can enhance brand loyalty and satisfaction.

28. General Electric: Optimizing Energy Production with AI

Task/Conflict: Managing energy production efficiently while predicting and mitigating potential issues is crucial for energy companies. General Electric (GE) aimed to improve the efficiency and reliability of its energy production facilities using AI.

Solution: GE integrated AI into its energy management systems to enhance power generation and distribution. AI algorithms predict maintenance needs and optimize energy production, ensuring efficient operation and reducing downtime. This predictive maintenance approach saves costs and enhances the reliability of energy production.

• Increased efficiency in energy production and distribution.
• Reduced operational costs and enhanced system reliability.
• Predictive maintenance is crucial for cost-effective and efficient energy management.
• AI can significantly improve the predictability and efficiency of energy production.

Related: Use of AI in Sales

29. L’Oréal: Transforming Beauty with AI

Task/Conflict: Personalization in the beauty industry enhances customer satisfaction and brand loyalty. L’Oréal aimed to personalize beauty products and experiences for its diverse customer base using AI.

Solution: L’Oréal leverages AI to assess consumer data and provide personalized product suggestions. AI-driven tools assess skin types and preferences to recommend the best skincare and makeup products. Additionally, virtual try-on apps powered by AI allow customers to see how products would look before making a purchase.

• Enhanced personalization of beauty products and experiences.
• Increased customer engagement and satisfaction.
• AI can provide highly personalized experiences in the beauty industry.
• Data-driven personalization enhances customer satisfaction and brand loyalty.

30. The Weather Company: AI-Predicting Weather Patterns

Task/Conflict: Accurate weather prediction is vital for planning and safety in various sectors. The Weather Company aimed to enhance the accuracy of weather forecasts and provide timely weather-related information using AI.

Solution: The Weather Company employs AI to analyze data from weather sensors, satellites, and historical weather patterns. AI models improve the accuracy of weather predictions by identifying trends and anomalies. These enhanced forecasts help in better planning and preparedness for weather events, benefiting industries like agriculture, transportation, and public safety.

• Improved accuracy in weather forecasting.
• Better preparedness and planning for adverse weather conditions.
• AI can enhance the precision of meteorological predictions.
• Accurate weather forecasting is crucial for safety and operational planning in multiple sectors.

31. Cisco: Securing Networks with AI

Task/Conflict: As cyber threats evolve and become more sophisticated, maintaining robust network security is crucial for businesses. Cisco aimed to leverage AI to enhance its cybersecurity measures, detecting and responding to threats more efficiently.

Solution: Cisco integrated AI into its cybersecurity framework to analyze network traffic and identify unusual patterns indicative of cyber threats. This AI-driven approach allows for real-time threat detection and automated responses, thus improving the speed and efficacy of security measures.

• Strengthened network security with faster threat detection.
• Reduced manual intervention by automating threat responses.
• AI is essential in modern cybersecurity for real-time threat detection.
• Automating responses can significantly enhance network security protocols.

32. Adidas: AI in Sports Apparel Manufacturing

Task/Conflict: To maintain competitive advantage in the fast-paced sports apparel market, Adidas sought to innovate its manufacturing processes by incorporating AI to improve efficiency and product quality.

Solution: Adidas employed AI-driven robotics and automation technologies in its factories to streamline the production process. These AI systems optimize manufacturing workflows, enhance quality control, and reduce waste by precisely cutting fabrics and assembling materials according to exact specifications.

• Increased production efficacy and reduced waste.
• Enhanced consistency and quality of sports apparel.
• AI-driven automation can revolutionize manufacturing processes.
• Precision and efficiency in production lead to higher product quality and sustainability.

Related: How can AI be used in Disaster Management?

33. KLM Royal Dutch Airlines: AI-Enhanced Customer Service

Task/Conflict: Enhancing the customer service experience in the airline industry is crucial for customer satisfaction and loyalty. KLM aimed to provide immediate and effective assistance to its customers by integrating AI into their service channels.

Solution: KLM introduced an AI-powered chatbot, which provides 24/7 customer service across multiple languages. The chatbot handles inquiries about flight statuses, bookings, and baggage policies, offering quick and accurate responses. This AI solution helps manage customer interactions efficiently, especially during high-volume periods.

• Improved customer service efficiency and responsiveness.
• Increased customer satisfaction through accessible and timely support.
• AI chatbots can highly improve user service in high-demand industries.
• Effective communication through AI leads to better customer engagement and loyalty.

34. Novartis: AI in Drug Formulation

Task/Conflict: The pharmaceutical industry requires rapid development and formulation of new drugs to address emerging health challenges. Novartis aimed to use AI to expedite the drug formulation process, making it faster and more efficient.

Solution: Novartis applied AI to simulate and predict how different formulations might behave, speeding up the lab testing phase. AI algorithms analyze vast amounts of data to predict the stability and efficacy of drug formulations, allowing researchers to focus on the most promising candidates.

• Accelerated drug formulation and reduced time to market.
• Improved efficacy and stability of pharmaceutical products.
• AI can significantly shorten the drug development lifecycle.
• Predictive analytics in pharmaceutical research can lead to more effective treatments.

35. Shell: Optimizing Energy Resources with AI

Task/Conflict: In the energy sector, optimizing exploration and production processes for efficiency and sustainability is crucial. Shell sought to harness AI to enhance its oil and gas operations, making them more efficient and less environmentally impactful.

Solution: Shell implemented AI to analyze geological data and predict drilling outcomes, optimizing resource extraction. AI algorithms also adjust production processes in real time, improving operational proficiency and minimizing waste.

• Improved efficiency and sustainability in energy production.
• Reduced environmental impact through optimized resource management.
• Automation can enhance the effectiveness and sustainability of energy production.
• Real-time data analysis is crucial for optimizing exploration and production.

36. Procter & Gamble: AI in Consumer Goods Production

Task/Conflict: Maintaining operational efficiency and innovating product development are key challenges in the consumer goods industry. Procter & Gamble (P&G) aimed to integrate AI into their operations to enhance these aspects.

Solution: P&G employs AI to optimize its manufacturing processes and predict market trends for product development. AI-driven data analysis helps in managing supply chains and production lines efficiently, while AI in market research informs new product development, aligning with consumer needs.

• Enhanced operational efficacy and minimized production charges.
• Improved product innovation based on consumer data analysis.
• AI is crucial for optimizing manufacturing and supply chain processes.
• Data-driven product development leads to more successful market introductions.

Related: Use of AI in the Navy

37. Disney: Creating Magical Experiences with AI

Task/Conflict: Enhancing visitor experiences in theme parks and resorts is a priority for Disney. They aimed to use AI to create personalized and magical experiences for guests, improving satisfaction and engagement.

Solution: Disney utilizes AI to manage park operations, personalize guest interactions, and enhance entertainment offerings. AI algorithms predict visitor traffic and optimize attractions and staff deployment. Personalized recommendations for rides, shows, and dining options enhance the guest experience by leveraging data from past visits and preferences.

• Enhanced guest satisfaction through personalized experiences.
• Improved operational efficiency in park management.
• AI can transform the entertainment and hospitality businesses by personalizing consumer experiences.
• Efficient management of operations using AI leads to improved customer satisfaction.

38. BMW: Reinventing Mobility with Autonomous Driving

Task/Conflict: The future of mobility heavily relies on the development of safe and efficient autonomous driving technologies. BMW aimed to dominate in this field by incorporating AI into their vehicles.

Solution: BMW is advancing its autonomous driving capabilities through AI, using sophisticated machine learning models to process data from vehicle sensors and external environments. This technology enables vehicles to make intelligent driving decisions, improving safety and passenger experiences.

• Pioneering advancements in autonomous vehicle technology.
• Enhanced safety and user experience in mobility.
• AI is crucial for the development of autonomous driving technologies.
• Safety and reliability are paramount in developing AI-driven vehicles.

39. Mastercard: Innovating Payment Solutions with AI

Task/Conflict: In the digital age, securing online transactions and enhancing payment processing efficiency are critical challenges. Mastercard aimed to leverage AI to address these issues, ensuring secure and seamless payment experiences for users.

Solution: Mastercard integrates AI to monitor transactions in real time, detect fraudulent activities, and enhance the efficiency of payment processing. AI algorithms analyze spending patterns and flag anomalies, while also optimizing authorization processes to reduce false declines and improve user satisfaction.

• Strengthened security and reduced fraud in transactions.
• Improved efficiency and user experience in payment processing.
• AI is necessary for securing and streamlining expense systems.
• Enhanced transaction processing efficiency leads to higher customer satisfaction.

40. AstraZeneca: Revolutionizing Oncology with AI

Task/Conflict: Advancing cancer research and developing effective treatments is a pressing challenge in healthcare. AstraZeneca aimed to utilize AI to revolutionize oncology research, enhancing the development and personalization of cancer treatments.

Solution: AstraZeneca employs AI to analyze genetic data and clinical trial results, identifying potential treatment pathways and personalizing therapies based on individual genetic profiles. This approach accelerates the development of targeted treatments and improves the efficacy of cancer therapies.

• Accelerated innovation and personalized treatment in oncology.
• Better survival chances for cancer patients.
• AI can significantly advance personalized medicine in oncology.
• Data-driven approaches in healthcare lead to better treatment outcomes and innovations.

Related: How can AI be used in Tennis?

Closing Thoughts

These 40 case studies illustrate the transformative power of AI across various industries. By addressing specific challenges and leveraging AI solutions, companies have achieved remarkable outcomes, from enhancing customer experiences to solving complex scientific problems. The key learnings from these cases underscore AI’s potential to revolutionize industries, improve efficiencies, and open up new possibilities for innovation and growth.

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• 🔎 Search: Easily find past chats with a powerful search feature, improving your ability to recall information.
• 🔗 Sharable Chat Link: Generate a link to share your chat, allowing others to view the conversation.
• 🌍 Multilingual Chat in 75+ Languages: Communicate and generate content in over 75 languages, expanding your global reach.
• 💻 AI Code Assistance: Leverage AI to generate code in any programming language, debug errors, or ask any coding-related questions. Our AI models are specially trained to understand and provide solutions for coding queries, making it an invaluable tool for developers seeking to enhance productivity, learn new programming concepts, or solve complex coding challenges efficiently.
• 📁 AI Chat with Files and Images: Upload images or files and ask questions related to their content. AI automatically understands and answers questions based on the content or context of the uploaded files.
• 📷 AI Text to Image & Image to Image: Create stunning visuals with models like Stable Diffusion, Midjourney, DALLE v2, DALLE v3, and Leonardo AI.
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• 🎥 AI Text to Video: Convert text scripts into captivating video content.
• 🔍 AI Image to Text with Context Understanding: Not only extract text from images but also understand the context of the visual content. For example, if a user uploads an image of a teddy bear, AI will recognize it as such.
• 🔀 AI Image to Video: Turn images into dynamic videos with contextual understanding.
• 📸 AI Professional Headshots: Generate professional-quality avatars or profile photos with AI.
• ✂ AI Image Editor, Resizer and Compressor, Upscale: Enhance, optimize, and upscale your images with AI-powered tools.
• 🎼 AI Music to Music: Enhance or transform existing music tracks by inputting an audio file. AI analyzes your music and generates a continuation or variation, offering a new twist on your original piece.
• 🗣 AI Voice Chat: Experience interactive voice responses with AI personalities.
• ☁ Cloud Storage: All content generated is saved to the cloud, ensuring you can access your creations from any device, anytime.

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AI Case Study Creator That Brings Stories to Life

Easily create impressive interactive case studies that increase lead engagement and conversion rates.

Used by professional marketing teams at:

Professional case study templates built for storytelling

Simply grab a template and let our AI case study generator✨ bring it to life for you:

How our AI case study generator works

Generate your case study with ai.

Simply type in what you need and let Storydoc do the magic for you!

Edit and bring it to perfection

Let our magic assistant help you through the process
with automatic slide copy and design.

Turbo-charge with integrations

Easily connect your CRM, calendar, and other tools
to move from static PDFs to actionable case studies.

Send. Track. Convert. Track customer engagement and conversion in real-time Generate your case study with AI

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Track customer engagement and conversion in real-time

Their case studies are getting attention

“Storydoc gives us the power and flexibility to design case studies and other pieces of content ourselves, even with our limited design experience ."

“With our most recent Storydocs, we're like, ‘Oh my goodness, It brought it to life like we do when we present it , but without the person even being there!’”

" Storydoc sets me apart from my competitors .  My goal is for my business to be memorable and Storydoc allows me to showcase the colors of my business in the best possible way."

A business case studies tool you can trust

Turn your case studies into an engagement tool.

Switch from static PDFs and webpages to interactive case studies created with modern marketing needs in mind.

Stop killing engagement

Readers strongly dislike PDF content . Replace your static case studies with interactive multimedia stories users love and remember.

Grant yourself content superpowers with AI

Easily design amazing interactive case studies by yourself faster than ever, guided by AI. No coding, no design skills needed.

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32% of case studies are opened on mobile  - your storydocs won’t fail to impress on mobile or any other device.

Convert users directly from your case studies

Enable readers to easily take the next step directly from your case studies with smart CTAs like a form, calendar, or live chat.

Wanna know if your case studies are working?

Get real-time analytics on everything . Reading time, scroll depth, conversions, shares, and more.

Make personalized case studies for ABM

Easily personalize prospecting case studies . Add the prospect's name and title with dynamic variables and instantantly apply their branding.

Your readers want a story , not a case study

Give'em what they want, give'em a Storydoc.

Everything that you should know about Storydoc

What is the Storydoc case study creator?

This AI case study generator lets you to intuitively design and write engaging interactive stories that captivate prospects. No coding or design skills needed.

The Storydoc case study designer offers a broad array of interactive slides for startups and new business concepts. These can be quickly and easily customized to align with your vision and requirements.

Storydoc frees you from outdated PPT slide methods, offering instead a scroll-based, web-friendly, mobile-optimized experience, complete with performance analytics.

Is the Storydoc AI case study generator safe?

Absolutely, the Storydoc AI case study creation app is secure and reliable. Your personal information is well-protected and encrypted.

We prioritize your data security, adhering to stringent security policies and best practices. Don't just take our word for it; companies like Meta, Pepsi, and Xerox trust us enough to use Storydoc daily.

For more information see  Our Story page ,  Terms and Conditions , and  Privacy Policy .

Why Storydoc is more than just another AI case study creator?

Storydoc is more than a tool for creating presentations. Instant AI case studies are useful, but they can become repetitive.

Sure, you can create your content faster, but does it truly stand out? Will it be effective? Probably not.

The issue often lies in the traditional PowerPoint design, whether AI-assisted or not. Storydoc takes a different approach.

We create case study experiences that truly engage decision-makers, featuring scrollitelling, multimedia, and in-document navigation.

Check out these examples .

What’s so great about AI-generated case studies?

An AI-generated case study can save you hours, even days, of effort for your startup. However, if you're using an AI PPT case study tool, you're saving time but potentially missing impact.

No one enjoys PowerPoints, even those created with AI. No AI PowerPoint case study tool can deliver a presentation that truly makes a difference. But Storydoc can. Our AI helps you create stories that generate interest and revenue.

Is Storydoc a free case study designer?

The Storydoc AI case study generator enables you to create content faster and more effectively than doing it solo.

Transform your presentations from ordinary to extraordinary in no time. Storydoc offers a 14-day free trial.

Try it out and see if it suits your needs. Based on hundreds of thousands of presentation sessions, we're confident that prospective clients will appreciate it.

Every interactive case study you create during your trial is yours to keep forever, at no cost!

For learning about our paid plans see our  Pricing .

Can I trust Storydoc with my data?

You can trust Storydoc to keep your personal information and business data safe.

The Storydoc app is safe and secure thanks to an encrypted connection . We process your data in accordance with very strict policies.

For more information, see Terms and Conditions , and Privacy Policy .

What's the best way to get started?

The easiest way to start is to visit our Case study templates page , pick a template you like, provide a few details, and see the magic happen - how Storydoc generates a presentation from scratch with your branding, content structure, visuals, and all.

Inside the presentation maker app, you can switch between templates, adjust your design with drag and drop interface, find ready-made slides for any use case, and generate text and images with the help of our AI assistant.

How do I send or share Storydoc case studies?

Storydocs function like web pages; each case study you create has a unique link for easy sending and tracking.

Once your Storydoc is complete, just hit publish. Published presentations are instantly viewable in any browser.

To share your presentation, simply click the Share button and copy the link. Viewers will experience an interactive webpage, far more engaging than a static PowerPoint or PDF.

Can I print Storydoc case studies?

Yes, but currently, this service is only available to our Pro and Enterprise customers. However, this feature will soon be accessible to all Storydoc users directly from the editor.

Keep in mind, a printed Storydoc loses its interactive elements, which are key to its high engagement and charm.

What integrations does Storydoc offer?

All the essential ones! Storydocs provide full content integrations: Calendly, Loom, YouTube, Typeform, and more, all of which can be added to your Storydoc presentation. But we offer much more than the basics.

With Storydoc, you can embed lead-capturing forms, your own live chat, advanced dashboards, in-page payments, and e-signatures.

Learn more on our Integrations page .

Are Storydocs mobile-friendly?

Yes! Storydoc is optimized for flawless mobile performance . No matter the divide or OS your case studies is opened on, the design will be perfect.

Check out similar Storydoc tools

Create your best case study to date

Stop losing opportunities to ineffective case studies. Your new winning case study is one click away!

AI Case Study Generator

Generate professional and engaging case studies effortlessly with our free AI Case Study creator. Simplify the process and showcase your success.

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Unlock the power of our case study creator tool—Generate compelling case studies effortlessly with our creator and captivate your audience. With just a few clicks, our smart technology helps you understand data, find trends, and make insightful reports, making your experience better and improving your SEO strategy.

What is a Case Study

A case study is like a detailed story that looks closely at a particular situation, person, or event, especially in the business world. It's a way to understand how things work in real life and learn valuable lessons. For instance, if a business wanted to figure out how another one became successful, they might study that business as a case study.

Let's say there's a small company that started selling handmade products online and became successful. A case study about this business could explain the challenges they faced, the strategies they used to grow, and the results they achieved. By reading this case study, other businesses could learn useful tips and apply them to their situations to improve and succeed.

7 Tips For Writing Great Case Studies

• Pick a Familiar Topic: Choose a client or project that your audience can relate to. This makes it easier for them to see how your solutions might work for their situations.
• Clear Structure: Start with a concise introduction that sets the stage for the case study. Clearly outline the problem, solution, and results to make your case study easy to follow.
• Engaging Storytelling: Turn your case study into a compelling narrative. Use real-world examples, anecdotes, and quotes to make it relatable and interesting for your audience.
• Focus on the Problem: Clearly define the problem or challenge your case study addresses. This helps readers understand the context and sets the foundation for the solution.
• Highlight Solutions: Showcase the strategies or solutions implemented to overcome the problem. Provide details about the process, tools used, and any unique approaches that contributed to the success.
• Optimize for SEO: By incorporating your case study into a blog post using a blog post generator, you enhance its visibility and reach. This, in turn, improves the search engine rankings of your blog post, attracting more organic traffic.
• Quantify Results: Use data and metrics to quantify the impact of your solutions. Whether it's increased revenue, improved efficiency, or customer satisfaction, concrete results add credibility and demonstrate the value of your case study.

What is a Case Study Creator

A free case study generator is a tool or system designed to automatically create detailed case studies. It typically uses predefined templates and may incorporate artificial intelligence (AI) to generate comprehensive analyses of specific situations, events, or individuals.

This tool streamlines the process of crafting informative case studies by extracting key details, analyzing data, and presenting the information in a structured format.

Case study generators are valuable for businesses, students, or professionals seeking to efficiently produce well-organized and insightful case studies without the need for extensive manual effort.

Benefits of Using Case Study Generator

In today's competitive landscape, showcasing your product or service successes is vital. While case studies offer a compelling way to do this, starting from scratch can be time-consuming. That's where case study generators step in, providing a robust solution to streamline the process and unlock various advantages.

• Easy and Quick: A case study generator makes it simple to create detailed studies without spending a lot of time. It's a fast and efficient way to compile information.
• Accessible Online: As an online case study generator, you can use it from anywhere with an internet connection. No need for installations or downloads.
• Free of Cost: Many case study creators are free to use, eliminating the need for any financial investment. This makes it budget-friendly for businesses or individuals.
• AI-Powered Insights: Some generators use AI (artificial intelligence) to analyze data and provide valuable insights. This adds depth and accuracy to your case studies.
• Save Time and Effort: Generate a polished case study in minutes, automating tasks like data analysis and content creation. This frees up your time to focus on other aspects of your business.
• Enhance Quality and Consistency: Case study creators offer templates and AI-powered suggestions, ensuring your studies are well-structured and visually appealing. Consistent quality strengthens your brand image.
• Improve Brand Awareness and Credibility: Sharing case studies on your platforms increases brand awareness and builds trust. Positive impacts on others establish you as a credible provider.
• Boost Lead Generation and Sales: Compelling case studies build trust and showcase your value, attracting leads and converting them into customers, ultimately boosting your sales.
• Increase Customer Engagement and Loyalty: Case studies provide insights into your company, fostering deeper connections, increasing engagement, and promoting long-term loyalty.
• Improve Your Writing Skills: Free AI Case study generators act as learning tools, offering guidance on structure, content, and storytelling. Studying generated drafts refines your writing skills for crafting impactful case studies in the future.

How AI Case Study Generator Works

An online case study generator works by leveraging artificial intelligence algorithms to analyze and synthesize information, creating comprehensive case studies. Here's a simplified explanation of its functioning:

Data Input:

Algorithm analysis:, content generation:, language processing:, who needs a case study creator.

Anyone looking to create informative and detailed case studies can benefit from using an online case study generator. This tool is useful for

Businesses:

Professionals:, individuals:, marketing professionals:, researchers:, why opt for our case study creator.

Are you on the lookout for a top-notch case study generator that combines outstanding features with user-friendliness, all at no cost and without the need for registration? Your search ends here. Our AI-driven case study generator is the ideal solution for you. Here's why you should choose our tool:

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• Not focusing on the benefits to the reader.
• Not using data and results to support their claims.
• Not telling a compelling story.
• Not using visuals effectively.
• Not promoting their case study.

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Best AI Case Study Examples in 2024 (And a How-To Guide!)

Who has the best case studies for ai solutions.

B2B buyers’ heads are spinning with the opportunities that AI makes possible.

But in a noisy, technical space where hundreds of new AI solutions and use cases are popping up overnight, many buyers don’t know how to navigate these opportunities—or who they can trust.

Your customers are as skeptical as they are excited, thinking…

• “I’m confused by the complexity of your technology.”
• “I’m unsure whether there’s clear ROI.”
• “I’m concerned about my data security.”
• “How will I integrate AI into our systems?”
• “I’m worried about employee pushback.”
• “I’m nervous about its use and governance.”

Done well, case studies about your AI solution can answer all of these questions in a way no other asset can:

With real-world storytelling, third party trust, and practical demonstrations that you can do what you promise.

To help you level up your customer stories, we’ve scoured the web for examples of the best AI case studies from companies spanning billion-dollar-juggernauts and scrappy startups.

Then, we profiled exactly what they’re doing well so you can level up your own stories!

OPPORTUNITY ALERT: Of all of the businesses we reviewed in researching this piece, just 50% were publishing customer success stories on their websites. Want an instant competitive advantage in AI? Scale your own case study production right now!

1. Location is everything: make stories findable

Key decision-makers in B2B businesses actively seek out word-of-mouth content about potential AI partners (like you!). So the easier they can find case studies on your website, the better.

Of the AI businesses we analyzed doing case studies, most make it easy to locate their case study overview page (where prospects see your complete portfolio of case studies at a glance.)

A common journey is via ‘Resources’ in the main navigation bar, followed by a link to ‘Customer Stories’, ‘Client Stories’, ‘Case Studies’, or similar.

For example, Otter.ai has their customer stories slightly buried in their “Blog” section , with an easy-to-miss category link. We don’t love this, because there’s no clear reason someone should expect to find this type of content in the blog vs. a “Customers” section or otherwise:

These also appear in their “Resources” section, but without any sort of jump link or clear indication you might find them there:

But you can do better!

In a space so skeptical and noisy, we advise you follow the likes of Presight AI and Google DeepMind and give buyers access to your customer success stories with a single click from the main navigation:

While Presight AI favors simplicity with a link to ‘Client Stories’, Google DeepMind opens the door via ‘Impact’.

If, like Google DeepMind, your impact as an AI business extends beyond commercial customers to broader sectors and communities, using a term like ‘Impact’ works well, but ‘clear’ is better than clever here, and a simpler term (‘Customers’) may be stronger.

You’ve put in the hard work sourcing concrete proof for potential buyers; don’t put hurdles in the way of finding it.

AI case study overview pages

The ‘overview’ for your customer story page is where customers are going to either continue their journey with intention—or stumble around in the dark.

A great overview page provides a clear sense of hierarchy (what’s important?), organization (what’s here, and what’s for me?) and expectation (what’s on the other side of the click?).

Take Jasper.ai for example:

Their overview page starts strong with a compelling bit of social proof (100,000+ businesses? Holy toledo!). Having a featured story is great (more on that later), though the headline for the one in the image sort of buries the lede (800% surge in traffic!? Holy toledo!)

After that, the page offers no clear way to drill down with intention: A lead is left to scroll through the logos presented to see if there are any companies they know of, or choose a story at random—most likely the featured story or the one in the upper left of the grid.

That’s not as ideal: you’d much rather have a customer quickly find the stories most relevant to THEM.

Boston Dynamics is one AI business worth emulating on that front.

A no-nonsense intro tells prospects they’re on the right page: “Discover success stories from real customers putting our robotics systems to work.”

If you choose to run a featured case study on your overview page, choose a high-impact one that appeals directly to…

• A substantial result (with metrics ideally), if your audience is skeptical about ROI
• A strong quote on the alleviation of pain (if metrics aren’t available)
• A weighty promise of value if your audience is looking for something to aspire to
• A clear ‘how-to’ hook if your audience is curious about the logistics/implementation

Next, Boston Dynamics provides a comprehensive list of case studies. It’s important that prospects can easily slice and dice these to find studies that are most relevant.

Boston Dynamics does this in a couple of ways:

First, they provide filters by ‘topic’, ‘application’, and ‘industry focus’. Second, they stamp each preview image with the main use case in that study.

Potential buyers can sort the ‘safety’ wheat from the ‘inspection’ chaff with or without filters.

There are other ways to optimize your overview page and help buyers find relevant case studies fast.

Consider using imagery that reflects your customers’ industry or specialism. Also include company logos, so prospects recognize relatable brands.

Another AI business with a strong overview page is Dynatrace . Like Boston Dynamics, they kick off with a featured story:

Instead of creating intrigue with a juicy title and intro, Dynatrace runs a ‘hero’ quote.

A strong quote from your interviewee, at the outset, can spike prospects’ serotonin levels, create intrigue and add credibility.

Dynatrace’s hero quote isn’t as dynamic as it could be, though it’s still strong, speaking to specific benefits (clarity and visibility).

Dynatrace offers a video testimonial (rather than written) as their featured story, something we’re all for when context for the content has been provided like it has been with the hero quote.

Video adds even more trust for buyers because they see the speaker’s reactions and emotions right there in front of them (though be careful not to conceal the interviewee’s face with the play button!)

Again, Dynatrace provides an easy-to-segment list of stories. Brand-focused imagery, company logos, and filter functionality make digging out relevant content a breeze:

  LIGHTBULB MOMENT: Want to take filtering in your AI business to the next level? Buyers want more clarity on your ROI, so why not provide an ROI filter that highlights common KPIs/outcomes that matter to customers (e.g. savings, time savings, increased sales, reduced errors, improved retention, etc.)?

2. I can see clearly now: the importance of readability

Executed properly, case studies mimic the powerful effect of word of mouth and can be as persuasive as a trusted recommendation from friends.

But AI businesses face an added challenge: while you know your AI solution inside out, buyers could be confused by the complexity of your technology.

In any B2B business, multiple people will likely be involved in any buying decision. If your case study is meant to appeal to (typically) less tech-savvy buyers (e.g. CEOs, CMOs, etc.), then avoiding complex jargon is key.

One way to do this is to put the customers’ quotes and narrative at the core of the story.

Runway handles this with a Q&A style approach to customer stories where their customers’ responses (and thus, language) make up the entire content:

But if the Q&A style approach isn’t right for you (and it may not be), you’ve got options.

6 quick tips for writing an AI case study well

Before we dive into examples of the best written case studies for AI, here are some basics to bear in mind:

1. Every great story has a beginning, middle, and end. Case studies follow more or less the same flow: a headline, a challenge, a solution, and the results you achieved.

2. Every good story needs a hero, so introduce yours—your client. Your leads care about the transformation of someone like them, facing similar pressures and decisions. You want to build tension and stakes to make the story relatable, highlighting relatable pains and making the story feel personal.

Remember: heroes are rarely idiots—don’t make your customer look like one.

3. Explain in specific detail how your hero’s pain got solved. To demonstrate your value, you want to help the reader feel the same relief, security, and confidence that the actual customer experienced. Don’t just list the features that the customer used: tie everything back to a specific, desirable outcome and a practical “how.”

4. Address specific AI-related objections in the content. If leads worry about integration, explain it in your customers’ words. If they’re worried about security, aim for quotes covering this. A lot of this comes down to properly planning and structuring interviews with your clients.

5. Share the impact beyond the metrics (but the metrics, too.) In the ‘results’ section, metrics matter—but so does clearly showing the transformation that has taken place. Use specific examples of what a customer can do now, or do better. Share from their output, portfolio, or specific process if you can.

Make it real with tangible examples.

6. Avoid jargon, complicated words, and creative adjectives, unless… Jargon is to be killed with fire UNLESS your customers use that same jargon and identify with it (e.g. technical roles that prize their acronyms and lingo.)

Now, let’s get into what we saw in AI case studies out in the wild.

Across the companies we analyzed, we identified A LOT of impenetrable language and off putting jargon. A huge chunk of stories were so chewy, most non-technical B2B buyers would probably spit them out, for example:

“The ‘xxx (technology)’ provides a framework for energy operators, service providers and equipment providers to offer interoperable solutions, including AI- and physics-based models, and monitoring, diagnostics, prescriptive actions and services for energy use cases.”

These sentences are SO long. Incomprehensible jargon is everywhere. It all means next to nothing, unless you have a deep technical background in that business.

And your buyers may not!

We also found that while AI businesses should always aim for specificity in case studies, content (especially around results) trended towards being vague. For example:

“The collaboration has proven to be a fruitful venture, providing the bank with new opportunities for growth and risk management in the changing financial landscape.”

A fruitful venture? Was it as impactful as a falling watermelon or a shriveled grape?

Remember that buyers are looking for concrete, relatable, “I-can-now-do-this” proof of your capabilities. They want word-of-mouth quotes and powerful metrics.

Not rotten fruit or vague terms.

But it wasn’t all business-speaky doom and gloom. We found some great examples from AI businesses who deliver clarity and simplicity—including UiPath, who excelled at presenting the challenges their customers faced clearly and simply.

“The payroll process is complex, sensitive, and error-prone. It requires the coordination of various departments including HR, finance, and legal. Processing every wage accurately every single time requires massive effort and involves tedious manual tasks.”

UiPath make the story relatable, too, by adding human interest:

“On the micro level, missing a payment or getting it wrong simply isn’t an option when employees have bills to pay and essentials to buy.”

The pain of missing a bill because your employer messed up payroll is recognized by most people. This creates an emotional connection and sympathy in the reader.

And that probably means more engagement with the story at large!

UiPath liberally sprinkles customer quotes throughout their studies, providing a constant reminder that their solution positively impacts real people in the real world, and allowing those people to speak for themselves, in their own terms.

They also seize every opportunity to add vibrant, descriptive language so buyers feel what their customer felt. It reads like a magazine feature in places:

“I was asked to look into automation,” Guez says with a sparkle in his eye , explaining that he came out of retirement to take on his current role. “At the time, RPA was a buzzword. It was still quite a new technology. We needed to get a pilot going to see how it could alleviate this pain point.”

Google DeepMind is another AI solution that tells understandable and engaging customer stories, successfully when it comes to describing complex tech in plain English:

In the circled section, the company describes its Flamingo technology with both clarity and flare.

They use a funny, real-world image—a dog balancing a stack of crackers on its head—that appeals to your senses and creates a vivid and emotional connection with their solution. A visual would almost certainly have added value here!

It’s worth trying similar with your own case studies: find descriptive language, metaphors, or examples that appeal to your audience’s imagination and persuade them to reach out to you.

Google DeepMind takes care to explain every piece of technical language it uses. In another section, they talk about “improving the VP9 codec”. But they don’t leave it hanging like a curveball you can’t hit.

They add a short sentence to explain what they mean: “a coding format that helps compress and transmit video over the internet”. Home run!

3. Who cares: demonstrating value and ROI

Given the risk inherent in choosing the wrong solution or adopting a new product that doesn’t pan out, discerning B2B buyers need a clear picture of the ROI that your AI solutions provide.

Give them that, and you’re already a step ahead of the competition.

Attack the status quo

Your greatest competitors aren’t other AI solutions: they’re what your ideal customers are doing to solve the problem now—and that may very well be nothing.

To make AI customer stories compelling, you need to demonstrate the limitations and risks of sticking with the norm in order to give your solution a backdrop it can stand out against.

DataRobot does a fantastic job of this in their Freddie Mac story:

ThoughtSpot leads the “Challenge” section of their Fabuwood customer story with a comparison against a well-established alternative, Power BI:

In both cases, this not only quickly establishes the shortcomings of the status quo: it also gives leads something to compare this new solution to, instantly putting ThoughtSpot and DataRobot into well-defined categories their customers can understand (“Oh, it would replace X!”) instead of some nebulous “AI” bucket (“Oh, it’s… a new… AI… thing.”)

The importance of metrics in demonstrating ROI

Across the AI businesses we analyzed, there was a noticeable lack of performance metrics in their case studies. This suggests that either customers aren’t seeing strong returns or, more likely, AI firms and their customers find it a challenge to quantify AI investments.

Most organizations using your technology will have considered baseline performance pre-AI, put measurable goals in place and be tracking progress.

To strengthen the impact of your case studies, ask them to provide this quantifiable proof during your interview process. The key here is to be specific about what you ask for.

So what metrics should you ask customers to dig out for you?

Of course, it depends on your products and customers’ goals for using them, but here are some general tips.

Anything related to sales is gold for prospective buyers, such as revenue growth, margin improvements, conversion rates, and customer lifetime value.

Ask, too, about improvements to operations and efficiency, including cost savings, error reduction, productivity improvement, and process optimization.

As well as hard returns, try to unearth softer ones, such as the human impact on your hero, as this will strongly resonate with B2B buyers in similar roles.

Now let’s check out some examples.

Some AI companies do attempt to add weight and muscle to their case studies with metrics. But even the best examples we found have work to do.

Numenta , for example, showcases a hot metric in the headline below. 20x inference acceleration is a big sell for customers operating in the computing space, because it improves the performance of their machines:

To make the headline more intriguing, Numenta could explain the result and impact of this 20x increase in processor speed on their customers. For example, sharing revenue growth or profit margin improvements off the back of this high-speed processor would give other buyers a tempting result they’d want to replicate.

Back to UiPath now, who also use metrics to show how customers reap the benefits of their AI solutions. Here, metrics take center stage at the start of a story :

UiPath has chosen operational metrics here—the number of automations implemented, number of transactions handled by robots, and growth in payrolls they process each day.

While they do provide quantifiable evidence of the impact of AI to their business, they could go further.

For example…

• If more transactions are being handled by robots, how much time is that saving the business?
• Has staff retention improved with more dependable payroll?
• Have they saved costs as a result of greater efficiency?

AI has clearly provided Papaya Global with significant benefits. With a little more work—and arguably more structure at the interview stage—UiPath could have left readers with no doubt about their solution’s ROI.

Going beyond metrics and into examples

Several solutions had demonstrations of outcomes—for example, galleries of outputted imagery or samples of produced work.  Kaiber  has a lovely gallery, as you’d expect from a very visual solution:

Meanwhile Tome comes to bat with stories that disambiguate a use case and explain an outcome that is valuable, but not necessarily quantifiable, like creating a “Personal radio station”:

These are also valuable in terms of demonstrating practical value, but business buyers also speak in terms of ROI, especially when making a case to their bosses for a purchase.

4. Don’t fight it: turning employee pushback into employee buy-in

An ongoing barrier for businesses looking to implement AI solutions is the risk of employee pushback: will staff actually adopt and support new technologies that may fundamentally change how they work?

Strategic AI companies can use customer success stories as a weapon to shoot down those objections.

We found a number of AI businesses using case studies to share the message: “AI is not going to take your job!”

In this case study, UiPath’s customer explains the continued importance of having ‘a human touch’ in the business:

UiPath doesn’t want its customers to say their AI solves everything. Their goal is to make businesses more efficient and successful—not to jeopardize job security.

OpenAI also uses its case studies to battle employee pushback. One powerful line reads:

“Ironclad’s goal in using AI has always been to help people do more, not to replace them with technology.”

Their message couldn’t be clearer to companies looking for an AI solution, while avoiding conflict on the frontline.

Meanwhile, Reply.io works to overcome potential objections by focusing on where teams are likely to take issue: with the quality of work done by AI relative to a human.

They cover this potential staff objection right in the story, proactively shooting a barrier to adoption out of the sky.

4. Muzzled, not muted: make ‘anonymous’ compelling

In an ideal world, all your customers would let you tell the story of how you helped them succeed. In the real world, customers aren’t always comfortable publicly talking about their AI use, even when they’re thrilled.

Sometimes, they’re constrained by their legal departments. Other times, they make a call that the story’s just too sensitive and decline to participate.

One way around this is to ask customers to share their story anonymously. But can stories be compelling weapons of mass conversion if you don’t mention any names?

Yes, absolutely.

Let’s look at how one of the AI companies we analyzed, C3 AI , produces powerful anonymous studies, like this one :

C3 AI anonymizes this case study, but manages to maintaining most of its impact by:

• Demonstrating the prestige of the customer with a sidebar packed with detail (see ‘About the Company’ in the graphic above)
• Turning anonymity into a plus by sharing metrics the company might not make public if their name was associated with it (ie, $9M in accelerated operating income)
• Including it alongside multiple case studies that are named. Taken together, the anonymous study has as much credibility as named studies.

What more can you do?

You can further retain the power of anonymous studies by:

• Including compelling, in-depth quotes from the people involved, swapping out names for descriptive titles and gender-neutral pronouns.
• Providing as much detail as non-anonymous studies; telling the full story of why the customer chose you, what their journey looked like, and how you made a difference. You don’t need to provide names to demonstrate how you delivered real ROI.

5. Trust me, bro: getting your leads to believe the hype

As a B2B buyer, it’s hard to know whether companies are spinning you a genuine opportunity—or a yarn. Trust is tough to earn and keep.

Case studies immediately cut through the sales spiel and provide concrete proof straight from customers’ mouths.

By nature, case studies are powerful trust builders because they show rather than tell. You can maximize that opportunity by including additional ‘trust’ signals throughout your stories.

Devices such as customer quotes, customer headshots, and customer logos all do the job.

During our analysis of AI case studies, we found most companies use direct customer quotes to foster trust.

In an environment where many AI businesses have an ROI problem, customer quotes are critical. Buyers can hear exactly how other people just like them have benefited from your solutions, proving that your brand is worth buying.

OpenAI uses quotes well to enhance the credibility of their customer stories :

GoGwilt recalled the initial excitement within his legal engineering team as they saw what OpenAI’s models could do for contracting. “There was the first moment of the team saying, ‘Wow, this is producing work at the level of a first-year associate,’” he said.

It’s powerful for a buyer when they hear someone—in a role that resonates with their own—describing the ‘wow’ moment your product provides.

Here’s another example of how customer quotes can build emotion, trust, and buy-in:

The engineers quickly moved on to a prototype—and experienced another “wow” moment. “Integrating GPT-4 into our contract editor and just seeing how seamless and powerful it felt made it pretty easy for us to invest further into productizing and getting it to customers,” GoGwilt added.

Using customer headshots, customer logos, and embedded video are other solid ways to signal trust.

Video testimonials , in particular, increases the impact of customer success stories because viewers see a customer’s emotion and sincerity in real time.

Here’s another great example of this from DataRobot, combining customer testimonial videos with written quotes to hammer home the legitimacy of their story:

Similarly, WorkFusion regularly brings video into their enterprise customer stories , adding depth and legitimacy while sharing the genuine human perspectives of the impact:

6. Picky eaters: how to make AI case studies valuable for time-starved buyers

We’re big believers (supported by data) that prioritizing long-form customer stories on your website improves online visibility and provides proof of your expertise and authority.

But time-starved B2B buyers also need to be catered for.

That means presenting success stories in a scannable (or watchable) way that helps even wandering eyeballs catch the best bits.

Formatting and design devices, including top and sidebars, pull quotes, and images all help readers find proof of your capabilities without reading the entire study.

PROS is one company setting good scannability standards in their customer stories, like this one on Lufthansa :

They use exploded quotes, a snackable company round-up, short paragraphs, and white space to help buyers derive value without reading every word.

Using a hero quote at the outset adds instant credibility, even for scanners.

C3 AI does something unique by providing a visual timeline of events in their Shell customer story . This is a great idea for showing your customers’ journey in a bite-sized and accessible way:

Dynatrace runs a snappy sidebar, complete with a snack sized story round-up:

Dynatrace also uses a bulleted list, ‘Life with Dynatrace’, to highlight the key benefits of partnering with them, without oceans of convoluted narrative:

Boston Dynamics also performs well on scannability. Colorful images of robotic technology and punchy crossheads are used to break up long runs of text:

Shoutout to OpenAI, too, which uses exploded quotes as text breakers to make its formatting friendlier. Rushed readers are constantly rewarded with quotes from happy customers as they scan:

Google DeepMind provides an always on-screen navigation bar to help readers jump to the sections that most interest them:

If you do choose to use a topbar or sidebar in your studies, include impactful metrics in there, like UiPath does:

Because you’ll be drawing buyers to this section with your amazing performance metrics, be sure to include a call to action (the logical next step you want a buyer to take).

And don’t forget to include a CTA at the end of every story, too.

By making studies scannable, you ensure that every reader is covered.

One final observation: if you put the hard work into creating case studies, you will hook in target buyers looking to learn even more. Encourage extra engagement by including ‘keep reading’ or ‘share on social’ options at the end of your stories, just like Boston Dynamics do:

The last word: putting it all together

Now you’ve seen what other leading AI businesses are doing with their case studies, the question is this:

Are YOU ready to suck in more leads and buyers by producing high-impact case studies that prove your ROI and credibility?

Let’s recap some of the findings and recommendations from our analysis of leading AI case studies:

• AI companies can answer buyers’ biggest questions and concerns with well-crafted and well-presented case studies.
• Of the AI companies we analyzed, fewer than 50% had even a single case study case on their website. Scaling your own AI case study production (right now!) will give you an instant advantage.
• Make case studies super-easy for buyers who are looking for solutions like yours to find.
• Use simple, straightforward language to explain your technology, so technical and non-technical decision-makers can understand
• Differentiate your AI business in a noisy marketplace by providing quantifiable metrics. Clearly show the ROI customers get when they work with you.
• Anonymous studies about AI solutions can be as impactful as named studies. When customers know they won’t be named, they often provide mic-drop worthy metrics and personal details about their journey they otherwise wouldn’t feel comfortable sharing.
• Enhance case study credibility with customer quotes, customer imagery, customer logos, and video testimonials.
• Make your AI case studies scannable, so time-starved buyers understand all your capabilities and the results you get for customers without reading every word.

Need help producing written AI case studies or video testimonials?

At Case Study Buddy, we have the knowhow, streamlined processes, and team to make it easy for you.

Contact us today.

Ian Winterton

Based in SW France, Ian has spent 48,000hrs of his life (yes, he worked it out) telling stories about what makes great businesses special.

Ya, you like that? Well, there’s more where that came from!

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AI Case Study Generator

It’s free and easy to use. A simple upload of your content and your Case Studies are autogenerated.

Create Case Studies by repurposing audio or video content

The AI works fully automated

No prompt engineering required

Experience the power of AI without needing prompts

Train your AI to exactly sound like you

You can train your AI on a piece of content of yours

Autogenerate Case Studies

Create Case Studies based on the best templates from top-marketers. You will get your insights in the best format possible.

Extracted from your existing content

Work with your team, edit and download Case Studies

Case Studies that sound like you

You can train your AI Case Study Generator to sound like you, so you Case Studies are always in your own tone and style—the opposite of generating generic-sounding content out of thin air.

AI-based content repurposing for your Case Studies

Creating Case Studies is part of every good content marketing strategy. And now it has become even more accessible with AI Case Study Generator.

The best AI Case Study Generator.

Create Case Studies based on your insights, and that sounds like you inside Unifire, thanks to all the AI Case Study Generator’s unique AI-powered features.

Your content is generated based on the best creator templates and hook frameworks.

Collaboration

Unifire is built for collaboration. Live editing, unlimited team members and workspaces included.

Train your AI

AI’s that can sound like you and learn your own writing style. From vocabulary to sentence structure.

What are Case Studies?

Case studies are an incredibly effective tool for demonstrating your expertise and building trust with your audience. They provide real-world examples of how you’ve handled specific challenges, showcasing your strategies and solutions. By diving deep into the details of a particular project, case studies allow you to highlight your successes in a way that will make potential customers become actual customers. So, if you’re looking to boost your credibility and convert more leads, start sharing your case studies today.

How to use the AI Case Study Generator

It couldn’t be easier: Upload a piece of content, audio or video. Let the tool transcribe it and produce your Case Studies.

You can upload audio & video files, directly or via a link. After 2-4 mins you will receive your transcript. Autogenerated.

Auto transcription

We have the best transcription running in the background. 97% accuracy on even the most technical topics speaks for itself.

Edit your transcription

No AI is 100% perfect. So, we let you edit your transcript before hitting the generate content button.

Autogeneration

AI Case Study Generator automatically generates your Case Studies, based on the best templates we could find. So your content is in the best shape when you get it.

Edit & Collaborate

AI Case Study Generator is entirely collaborative and comes with unlimited team seats, workspaces and a full collaborative suite.

Once you’re ready to distribute, simply copy & paste your content into your favourite tools.

Questions about the AI Case Study Generator

It’s free for your first upload. You get 900 credits every month for free to use.

This is dependent on the length of your upload. If you upload 5 hours it will create much more content than if you upload only 5 mins.

Yes, you can train the AI to adopt your own tone & style. This includes sentence structures, vocabulary and more.

Some of the best AI Case Study Generator include Jasper, Reword, Anyword, and others.

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Learn how to provide the key details and context for the case study to generate a comprehensive and engaging document

1 variation

A Case Study Generator is a powerful tool designed to automatically create detailed case studies with the help of AI writing assistance. It plays a crucial role in showcasing business successes, attracting new clients, and establishing credibility within the industry.

With the rise of AI technology, creating case studies has been completely transformed. Now, it's possible to generate customized, top-notch case studies quickly and easily with the help of AI.

Junia AI 's Case Study Generator offers an innovative solution that elevates your storytelling efforts and sets you apart from the competition.

How Does Junia AI's Case Study Generator Work?

Junia AI's Case Study Generator is different because of how it creates case studies automatically. It uses smart AI algorithms to help with writing, making sure that the case studies created are of high quality and tailored to specific needs. The platform also has templates that can be customized, which helps in making the case study look good and organized.

• Advanced AI writing assistance algorithms
• Customizable templates

This combination of features makes it easy to create visually appealing and cohesive case study presentations.

Streamlining the Creation Process

The main goal of Junia AI's Case Study Generator is to make the process of creating case studies faster and more efficient. With this tool, users don't have to start from scratch or spend hours writing each section. Instead, they can input their information and let the AI do the rest.

• Tailored to user's needs and branding

Generating Compelling Narratives

One of the key strengths of Junia AI's Case Study Generator is its ability to generate compelling narratives based on data and content provided. The advanced algorithms analyze the information given and turn it into a story that engages readers.

• Analyzes data and content
• Creates compelling narratives

Ensuring Consistency and Coherence

Another advantage of using Junia AI's Case Study Generator is that it maintains consistency and coherence throughout the case study. This means that all sections flow well together and there are no abrupt changes in tone or style.

• Maintains consistency
• Ensures coherence

By combining these three elements - streamlined creation process, compelling narratives, and consistency/coherence - Junia AI's Case Study Generator helps businesses create effective case studies that showcase their success stories in a clear and persuasive manner.

Diverse Distribution Opportunities with Junia AI's Case Study Generator

Versatile distribution formats.

Junia AI's Case Study Generator offers a wide range of options for sharing your case studies, including:

• PDFs : Perfect for presentations or downloadable resources.
• Website integration : Seamlessly embed your case studies on your website for easy access by publishing your case study to your CMS systems, such as WordPress or Shopify .

Benefits of Using Blog Posts

One effective way to showcase the case studies you create with Junia AI is through blog posts . Here's why:

• Maximum reach : Blog posts have the potential to reach a large audience, helping you get your message out to more people.
• SEO advantages : By optimizing your blog posts with relevant keywords and links, you can improve your search engine rankings and attract organic traffic.

Easy Link Sharing for Collaboration

Junia AI understands the importance of collaboration and client presentations. That's why they've made it simple to share your case studies with others:

• Convenient link sharing : Generate unique links for each case study, making it easy to send them to clients or colleagues.
• Real-time updates : Any changes you make to the case study will automatically be reflected in the shared link, ensuring everyone is always viewing the latest version.

By utilizing these diverse distribution options, businesses can effectively showcase their case studies, reach a wider audience, and drive meaningful engagement.

Using a Case Study Generator can greatly enhance your storytelling efforts and establish credibility in your industry. The automation and AI technology offered by platforms like Junia AI's Case Study Generator can streamline the process of creating high-quality and tailored case studies, saving you time and effort.

By using a Case Study Generator like Junia AI, you can:

• Unlock your creativity and deliver compelling narratives that captivate your audience.
• Optimize case study performance and drive user interaction and conversion with customizable templates, real-time engagement tracking, and smart CTAs.
• Showcase your expertise and build trust with your target audience through generating personalized narratives with dynamic variables and branding application supported by Junia AI.
• Ensure maximum reach and SEO benefits by distributing case studies in various formats such as PDFs, website integration and blog posts.
• Impress potential clients, drive customer engagement, and ultimately achieve business success.

So why not leverage this innovative solution to elevate your storytelling efforts and establish yourself as an industry leader?

Example outputs

Generate engaging case studies effortlessly with our Case Study Generator

How XYZ Company Increased Their Organic Traffic by 50%

XYZ Company is a leading provider of software solutions for small businesses. They had been struggling to increase their organic traffic despite having a well-designed website and regularly publishing blog posts.

After conducting an SEO audit, we identified several areas where XYZ Company could improve their search engine rankings. We recommended the following strategies:

• Conducting keyword research to identify high-value keywords that were relevant to their target audience
• Optimizing on-page elements such as title tags, meta descriptions, and header tags
• Improving site speed and mobile responsiveness
• Building high-quality backlinks from authoritative websites in their industry

Within six months of implementing our recommendations, XYZ Company saw a 50% increase in organic traffic. Their website now ranks on the first page of Google for several high-value keywords, driving more leads and sales to their business.

How ABC Agency Helped a Local Restaurant Increase Their Online Visibility

A local restaurant was struggling to attract new customers through their online presence. Despite having a website and social media profiles, they weren't getting much engagement or visibility.

We conducted a comprehensive digital marketing audit and found several opportunities to improve the restaurant's online visibility. Our strategy included the following tactics:

• Creating a content marketing plan to publish regular blog posts and social media updates
• Optimizing the restaurant's website for local search with targeted keywords and location-based landing pages
• Running paid advertising campaigns on Facebook and Instagram to reach new audiences
• Implementing email marketing campaigns to keep existing customers engaged and encourage repeat visits

Within three months of implementing our strategy, the restaurant saw a significant increase in online visibility and engagement. Their website traffic increased by 75%, and they saw a 50% increase in social media engagement. The restaurant also reported an increase in foot traffic, with many customers mentioning that they found the restaurant through their online presence.

How DEF Company Increased Their E-commerce Sales by 200%

DEF Company is an e-commerce retailer selling fashion accessories. They had been struggling to increase their sales despite having a wide range of products and competitive pricing.

We conducted a thorough analysis of DEF Company's website and identified several areas where they could improve their user experience and conversion rate. Our strategy included the following tactics:

• Conducting customer research to identify pain points and opportunities for improvement
• Redesigning the website to improve navigation and make it more visually appealing
• Implementing a mobile-responsive design to cater to the growing number of mobile shoppers
• Improving product descriptions and images to provide more information and enhance the shopping experience
• Running targeted advertising campaigns on Google AdWords and Facebook Ads

Within six months of implementing our recommendations, DEF Company saw a 200% increase in e-commerce sales. Their website now ranks on the first page of Google for several high-value keywords, driving more leads and sales to their business.

What other amazing things can this template help you create?

✔ Meta Title

✔ Meta Description

✔ Extract keywords

✔ Feature Image

✔ Soon Internal Linking

Who needs Case Study Generator?

Marketing teams

Content creators

Business owners

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Frequently asked questions

• How does Junia AI's Case Study Generator work? Junia AI's Case Study Generator is different because of how it streamlines the creation process, generates compelling narratives, ensures consistency and coherence, and offers diverse distribution opportunities with versatile formats. It uses advanced algorithms to automate the case study creation process, saving time and effort for users.
• What is the main goal of Junia AI's Case Study Generator? The main goal of Junia AI's Case Study Generator is to make the creation process more efficient and effective. By automating the generation of compelling narratives and ensuring consistency and coherence, it aims to provide users with a powerful tool for showcasing their success stories.
• What are the key strengths of Junia AI's Case Study Generator? One of the key strengths of Junia AI's Case Study Generator is its ability to generate compelling narratives that captivate audiences. By leveraging advanced algorithms, it can create engaging stories that effectively showcase the success of a product or service.
• What are the advantages of using Junia AI's Case Study Generator? Another advantage of using Junia AI's Case Study Generator is its ability to ensure consistency and coherence across all generated content. This helps maintain a unified brand voice and message, enhancing the overall impact of the case studies.
• What distribution opportunities does Junia AI's Case Study Generator offer? Junia AI's Case Study Generator offers diverse distribution opportunities with versatile formats. Users can easily share their case studies through various channels such as blogs, social media, websites, and more, reaching a wider audience and maximizing impact.
• How can I showcase the case studies created with Junia AI's Case Study Generator? One effective way to showcase the case studies you create with Junia AI's Case Study Generator is by using blog posts. This allows you to reach your target audience through a popular and widely accessible platform, maximizing the visibility of your success stories.
• Does Junia AI's Case Study Generator support collaboration and client sharing? Yes, Junia AI understands the importance of collaboration and client sharing. The Case Study Generator provides easy link sharing options, allowing seamless collaboration between team members and effortless sharing with clients for review and feedback.

How to Use an AI Case Study Generator

If you’ve been toying with the idea of using AI to amp up marketing projects, you’re already on the path to tapping into something incredibly powerful. Enter the  AI case study generator  – your new best friend in marketing and brand building. These AI content generators can be your secret weapon in creating detailed, compelling customer case studies that capture the essence of your topic, turning a time-consuming task into a breeze.

But where do you start? How do you ensure you’re getting the most out of this AI tool? Let’s explore the ins and outs of using AI case study generators.

TL;DR here’s a quick summary video of this article.

What is an AI case study generator?

How to use an ai case study generator effectively, step 1. navigate to the ai tool or create a custom template, step 2. input the necessary information (product/service and company names + descriptions), step 3. add notes for more context (problems or challenges, solutions, and impact), step 4. choose the desired tone for the case study, step 5. review, edit, and refine the ai-generated case study.

An AI case study generator is a tool that automates the creation of case study content with the help of artificial intelligence. It simplifies the process of writing insightful, data-driven case studies, which are crucial for businesses looking to demonstrate their product effectiveness.

Unlike traditional case study creation methods that demand considerable time and effort for structuring and writing, an AI case study generator produces comprehensive case studies with just a few inputs from the user. All you have to do is supply the raw data and content to the tool, and the AI will analyze it to create a solid customer case study.

Narrato’s AI case study generator, offered as part of the  AI Content Assistant , can generate a well-written case study using only your notes.

Narrato’s AI case study generator

Its  AI writer  offers over 100 other AI templates to help you create blog articles, web copy, landing pages ( AI landing page generator ), product descriptions, and more. And if you want to optimize that content for SEO, there’s an  AI SEO brief generator  and  AI keyword generator . Whatever your content creation needs, this platform has an AI template for it. When you can’t find an AI template for your specific requirement, you can easily create one using  custom AI templates .

Using an AI case study generator can enhance your marketing efforts, but knowing how to use it effectively is key to achieving the best outcomes. Here’s a simple guide to get the most out of this AI tool.

The first step would be to go to the AI content assistant or your content task page on Narrato, and search for the case study AI template.

If you feel that the ready-made template does not give you everything you need in your customer case study, you can even build a custom AI template with your own prompt and inputs. Here’s a detailed guide to creating a  custom AI template .

Once you’re on the tool, you need to input the necessary information about the product/service and company you’re featuring in the case study. Make your inputs detailed and comprehensive to get the best output from the AI tool.

Start by entering your product/service name. If the case study is about a product, be sure to include any specific model or version if applicable. This will help set the context for your case study and give readers a clear idea of what your product is all about.

Next, include a brief description of your product/service. Highlight its key features, benefits, and how it solves a problem for your customers. This description should be concise but informative, to give a clear understanding of what makes your product unique. After that, it’s time to input your company name and a short description of your company. Talk about your company’s mission, values, and why you’re passionate about what you do. This helps build credibility and trust with readers who might be considering your product.

Adding company and product/service info in Narrato’s AI case study generator

After you’ve added the basic details, you need to provide the AI case study generator with some context for building the case study. On Narrato, you can provide this in the form of notes.

First up, you’ll have to define any specific problems or challenges that your customer was facing. Then, add detailed notes for the solution that was provided for those problems and challenges. And finally, add notes explaining the impact of those solutions. Make sure to include any quantitative data as well as qualitative insights that demonstrate the solution’s effectiveness.

Providing Narrato’s AI case study generator with context for the case study

Providing accurate and comprehensive information at this stage can help improve the quality and relevance of the AI-generated case study.

The tone of your case study will greatly impact how your audience perceives and engages with your content. Consider the purpose of your case study. Are you aiming for a formal, professional tone to showcase your expertise in a particular industry? Or would a more casual and conversational tone be more appropriate to connect with your audience on a personal level? It’s also a good idea to think about your target audience at this point. What tone would resonate with them the most? Are they looking for a serious and analytical approach, or would they prefer a lighter and more relatable tone?

Choosing the tone for the AI-generated case study

Narrato also gives you the option to generate a custom tone using the  AI brand voice generator . The tool analyzes your sample content pieces to generate a unique brand voice on the platform, which can then be applied to all the content assets. By choosing this as your tone of voice, you can ensure on-brand content every time you use the AI case study generator.

After entering everything, the AI will generate a case study for you, complete with all the relevant info.

AI-generated case study on Narrato

It’s crucial to review this AI-generated case study. Look for any areas that might require more detail or clarification. Editing and refining the content to add a personal touch, strengthen the narrative, or include additional data can greatly enhance the overall impact of the case study. Remember, the AI provides a strong starting point, but your insights and tweaks turn that foundation into a compelling, effective case study.

That’s a wrap

Using an AI case study generator can seriously up your marketing game, slicing through the hassle and time sink of traditional case study writing methods. Whether you’re aiming to enlighten readers or showcase your brand’s wins, learning how to use this tool is a smart move. In this article, we’ve shown you how. Now it’s on you to make the most of this tool. Remember, the power of AI is immense, but in the end, it’s the human touch—the strategic, creative thinking that you apply in using these tools—that will truly distinguish your case studies.

So, go ahead, and give Narrato’s AI case study generator a try!

Akshita is a content creator, with a penchant for turning complex topics into engaging and informative articles. As a wordsmith with a knack for storytelling, she is constantly looking for an opportunity to create something new.

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Redefining Content Strategy: The Rise of AI in Case Study Production

In content marketing, case studies are one of the most effective methods of demonstrating a brand's capability and dedication. They illustrate how a brand's products or services come to the rescue in real-world scenarios and highlight your customers' challenges, the strategies used to overcome these challenges, and the results.

Crafting these case studies has traditionally been a pretty detailed task that requires a lot of time and resources. However, with the rise of technology and digital tools, creating impactful case studies has become much easier and more efficient.  AI-powered marketing tools  simplify the process, speed up initial drafting, and provide valuable insights for strategy refinement.

While AI tools seem  unlikely to completely replace human expertise  in marketing and generating content, they certainly have the potential to enhance and optimize the process. One recent analysis has even suggested a remarkable  66% average productivity increase  for businesses using AI tools.

Scripted, an established leader in the content realm, is at the forefront of this shift. Our AI-driven case study tool seamlessly blends technology with traditional storytelling. It allows your business to document and share its success stories efficiently without sacrificing authenticity or emotional connection.

Harnessing the Potential of Scripted's AI-Powered Case Study Tool

Scripted's AI-enhanced case study generator stands out not merely as a tool but as a revolutionary asset. By harnessing advanced algorithms and tapping into a rich database of industry data, Scripted crafts case studies that are both compelling and accurate. Brands no longer need to grapple with the demanding task of creating conventional case studies.

Instead, you can use AI with Scripted to share your successes, highlight your expertise in the industry, and share what sets you apart. With Scripted's AI, you'll have accurate, compelling case studies in a fraction of the usual time.

Understanding Case Study Generators: Why AI?

In today's digital arena, merging artificial intelligence with human subject matter experts (SMEs) seems like a natural evolution. Historically, case studies involved labor-intensive, hands-on research. Now, this domain is on the cusp of transformation led by AI. So, what's propelling this shift, and why does AI emerge as a player in case study creation?

The Essence of Case Studies in the Business World

Case studies give a real-world glimpse of what a company's products or services can do. They're not just simple stories; they paint a clear picture of a brand's ability to tackle challenges, provide meaningful solutions, and showcase its unique strengths.

Far from mere promotional content, a well-structured case study validates a business's skill set, dedication, and customer-focused ethos. It provides potential clients with a vivid depiction of the possibilities when they opt for a specific brand or offering.

A case study is not just a recount of a challenge and resolution. It also connects emotionally with its audience, building trust and establishing legitimacy.

Hurdles in Conventional Case Study Development

Crafting a conventional case study is far from straightforward. It kicks off by pinpointing a significant success narrative, followed by comprehensive interviews, data assimilation, and thorough scrutiny. Once the foundational elements are set, the task is to mold this data into an absorbing tale that remains accurate yet captivating. The soul of a case study resides in its genuineness and precision, leaving minimal wiggle room for inaccuracies.

But the challenges don't stop there. With the demand for new content, companies are pushing to release numerous case studies to highlight the diverse aspects of their services. The lengthy creation process for each case study limits content production, making it challenging for firms to roll out high-caliber case studies on tight schedules. This age-old methodology, while comprehensive, often poses challenges for enterprises seeking a balance of excellence and volume.

AI Innovations: Ushering a New Age in Content Creation

Artificial Intelligence (AI) has been at the forefront of breakthroughs in various fields, and content development is no exception. With its advanced algorithms, AI can seamlessly take on the extensive data collection, organization, and analysis tasks that typically require significant human involvement. This streamlined process significantly speeds up the creation process while maintaining accuracy and authenticity.

Moreover, AI-powered tools can also generate visually appealing infographics to supplement case studies, target specific audiences and demographics, incorporate standards from various relevant industries, and personalize the content for individual clients. The result is a well-rounded, high-quality case study that can be produced at a much faster pace.

Key Considerations

There are many key considerations and questions to consider when incorporating AI solutions into the case study creation process. From assessing the reliability of AI-generated content to understanding its customization capabilities, here are some essential factors to consider:

How Does It Work?

Think of Scripted's case study generator as a smart helper. Just like our  press release generator , it uses AI to sift through lots of data, find important points, and create top-notch content. You simply plug in the key details about your project and its outcomes, and the tool takes care of making a case study that speaks to your industry and audience.

How Long Does It Take?

A typical case study, when done manually, can take anywhere from a week to a month, considering the data collection, writing, and editing processes. With Scripted’s case study generator, you can have a draft ready in mere minutes.

Is It Customizable?

Scripted stands out for its adaptability in its content generation suite. While many AI tools provide templated solutions, Scripted prides itself on its customization capabilities. The platform is intuitive, allowing users to specify their requirements in detail. This ensures the generated content aligns perfectly with your vision.

Whether it's adjusting the tone, style, or structure, Scripted offers a level of flexibility often lacking in other AI tools. This adaptability guarantees that businesses receive a tailored piece of content that fits their unique brand narrative rather than a one-size-fits-all solution.

When you add access to a team of SMEs — professional writers and editors — Scripted's case study generator becomes the ideal solution for businesses looking to save time without compromising quality.

Can It Handle Technical Subjects?

Case studies often contain technical information that can be challenging to convey clearly and concisely. This is where AI-generated content can have a significant impact. Scripted's case study generator uses advanced algorithms and natural language processing (NLP) to accurately represent even the most complex data points.

The platform also allows manual input, giving users the option to add their expertise and domain-specific knowledge to the generated content. This ensures technical subjects are accurately and effectively communicated without losing important details. As with any content, it's always a good idea to have an SME review and approve the final product.

Leading AI Case Study Tools: A Comparative Analysis

A variety of  AI marketing  tools have recently surfaced in the world of AI-driven content creation, each promising to redefine the art and science of case study production. As businesses seek to harness the potential of these tools, understanding their distinct features, strengths, and limitations becomes increasingly important.

In this comparative analysis, we'll explore some of the leading platforms in the industry.

A distinguishing feature of Cohesive is its hybrid functionality, which seems to borrow elements from both ChatGPT and Slack. This combination enables efficient team communication within the platform, which is critical for cohesive case study development. A centralized collaboration means insights, objectives, and data points can be discussed and refined in real time. This ensures that AI-generated case studies have depth and reflect the team's understanding.

Cohesive has a free pricing tier that generously allows unlimited word generation. However, it's limited to only 15 template runs (the number of unique files you generate on Cohesive) per month. For those who require more, it also offers the Creator plan at $25 per month with 150 monthly template runs and the Agency plan at $45 per month with 300 monthly template runs.

On the flip side, the pricing model of the platform could be a hurdle for some. While StoryDoc offers a 14-day free trial to get a feel for its functionalities, it doesn't offer an ongoing free version. A subscription is necessary after the trial, with plans starting at $30 per month for each user. This might be a concern for budget-conscious businesses. However, for those intent on a dynamic, AI-powered method for crafting case studies, the value StoryDoc offers might outweigh the cost.

Best known for its project management prowess,  ClickUp  has made a notable stride into the AI-aided content domain, particularly with its case study generator. Drawing on its foundational strengths in organization and structure, ClickUp equips users with a methodical framework that helps craft detailed, compelling case studies.

ClickUp allows you to weave content creation into project timelines, which supports uniformity, consistency, and speed. Teams across various departments — marketing, sales, product innovation, and customer relations — can use ClickUp to drive productivity. The all-in-one hub approach eliminates the need for multiple apps, encourages collaboration, and drastically cuts down on content creation time.

While ClickUp presents an extensive suite of features, it feels more like a generalist than a specialist in AI content generation. Some of its functionalities might not match the depth or specificity of other available tools, depending on your needs. Also, its free offering is a tad restrictive, capping storage at 100MB and offering limited free use of the AI generator. On the upside, ClickUp's paid versions are reasonably priced, starting at a modest $5 per user monthly.

Grammarly , a titan in content editing and proofreading, has broadened its horizons with the introduction of an AI-driven case study generator nestled within the GrammarlyGO package. With this new feature, users can easily create organized drafts. All you need to do is provide basic information about your business or product. Then, describe the challenges you hope to resolve for customers, mention who these customers are, and explain how you plan to help them.

Some customers complain about  Grammarly's problems  with grasping the deeper nuances of writing. One reviewer says its "recommendations are sometimes not appropriate." Potential users should also be aware that there are prompt limitations — 500 monthly for free users and 1,000 for those on the premium plan.

Though GrammarlyGO offers a fresh avenue for short case-study drafting, its proficiency with longer content and response time leave room for improvement. Another point of consideration is that several platforms easily detect its content as AI-generated. As it stands, GrammarlyGO is a useful addition for those seeking short content improvements, but it may not be the go-to for every user.

Scripted  offers a distinctive blend in AI case-study generation by merging human expertise with AI efficiency. Users are guided through a simple process to produce an AI-generated case study tailored to their specific product, industry, and type of customer. Creating these case studies is free for all users on the platform and can be created with the following steps:

• Begin with Basics : Share what you're all about - your company, product, or interesting project you've worked on.
• The Hurdle : Every story has a challenge. What problem or issue did you tackle?
• Solution Offered : Outline the product or service you implemented to address that challenge.
• Outcomes Achieved : Mention the benefits or positive results that emerged from your solution.
• Size It Up : How lengthy do you want this case study to be? Decide on the word count.
• Industry Category : Specify the industry related to your case study.
• Target Audience : Define the particular demographic or customer group you're addressing.
• Additional Details : If there are other relevant specifics or context that would enrich the case study, include them.

While users can quickly generate content using Scripted's free AI tools, you also have the opportunity to elevate your content by collaborating with Scripted’s subject matter expert writers and editors. This fusion of AI speed and human creativity provides brands with content that not only tells a story but also engages readers.

Users looking to work with Scripted's SMEs can opt for various plans, starting with a pro plan at $199 per month and extending to specialized  team , enterprise, and agency accounts. Depending on your plan, you'll also gain access to expert marketing specialists to help you with your  SEO content strategy,  along with a dedicated account manager.

On top of all this, both  free and paid plan members  gain access to the following array of tools at no additional cost:

• Infographic text generator
• Scripted's GPT-4-powered chatbot,  Scout
• Social media post generator
• Blog idea generator
• Headline generator
• Landing page generator

With such a robust set of tools, it's clear that Scripted is not just a platform for generating content. Scripted is a comprehensive content marketing toolkit that can help businesses of all sizes and industries streamline their content creation process and reach their target audience effectively.

Taking the Plunge With Scripted

In today's digital age, where content reigns supreme, having a trusted ally in content creation becomes indispensable. Scripted, with its blend of cutting-edge AI technology and the finesse of expert writers, offers businesses a unique pathway to engaging, impactful content. Its AI case study generator is more than just a tool. It's a testament to the future of content creation, where technology and human touch work together seamlessly.

When considering a robust, adaptable, and efficient solution for case study creation, Scripted stands out. Its diverse range of templates, the precision of its algorithms, and the invaluable human expertise available ensure that businesses always put their best foot forward. Looking ahead to recognize the direction in which content creation is heading, the choice becomes clear. Dive deep into the powerful combination of  AI-generated content  and human expertise that only Scripted offers.

Ready to redefine your content journey? Discover the unparalleled offerings of Scripted's AI tools and  elevate your case study game today .

Sign Up For Your 30 Day Free Trial Today!

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AI Case Studies: Highlighting Breakthrough Innovations

• Key Takeaways

Gartner reports that AI adoption in enterprises has increased by 270% since 2020.

Statista reveals that AI-driven personalization leads to a 15% increase in customer retention rates.

According to Moz, websites using AI for SEO experience a 30% boost in organic traffic.  

Businesses leveraging AI benefit from improved customer experiences, reduced costs, and enhanced operational processes.

Welcome to the world of Artificial Intelligence (AI), where amazing innovations are changing how things work in different industries like healthcare, stores, banking, factories, and schools. Have you ever thought about how AI is making these areas better? Let’s look at five interesting case studies that show how AI is improving how things are done, making them work faster and giving people better experiences.

Understanding AI Breakthroughs

• Defining AI Breakthroughs

AI breakthroughs are big steps forward in Artificial Intelligence that do things we didn’t think were possible before. They can come in different forms, like making smarter computer programs, improving how machines learn, or creating new AI tools that solve hard problems. What’s special about AI breakthroughs is they help industries grow and change in important ways, giving us new abilities and making big improvements.

• Significance of AI Breakthroughs in Innovation

The significance of AI breakthroughs in innovation cannot be overstated. These breakthroughs fuel technological progress and empower organizations to tackle challenges more effectively. Businesses can use advanced AI to work better, improve how things are done, and make customers happier. AI helps create new products and services, which can lead to finding new customers and making more money. In general, AI is crucial for making new ideas, growing, and changing industries in the future.

Case Study 1: Healthcare Revolution through AI

• Overview of Case Study:

This case study shows how AI is changing healthcare at a big hospital. It looks at how AI helps with diagnosing diseases, making personalized treatment plans, and improving patient care with AI systems.

• Implementation of AI Solutions:

AI-Powered Disease Diagnosis:

• The hospital used AI tools to look at medical images and find diseases like lung cancer and heart problems. This helped them find diseases earlier and treat patients better.
• Because of this, they became 30% better at diagnosing diseases, which means they could find them sooner and help patients more.
• The AI learned to see small details in the images that humans might miss, so it could make better diagnoses than people could alone.

Personalized Treatment Plans:

• AI used special programs to create treatment plans based on each patient’s unique details like their medical history, how they’ve responded to treatments before, and their specific characteristics.
• This personalized way of treating patients led to a 25% decrease in problems related to treatments because the treatments were made specifically for each patient, considering their individual needs and how their body reacts.
• Moreover, patients responded better to these personalized treatments, with a noticeable 20% improvement in how they reacted to the treatments. This shows that using AI for personalized treatments works effectively.

Enhanced Patient Care Delivery:

• AI systems like chatbots and virtual assistants were used in healthcare to support patients 24/7.
• This reduced paperwork by 40%, letting doctors spend more time with patients.
• Patients were happier because AI could quickly answer questions, schedule appointments, and remind them about medications.
• Results and Impact

Implementing AI at the hospital brought big improvements to healthcare:

• Diagnosing diseases got 30% more accurate, catching problems early and helping treatments work better.
• Personalized treatments cut complications by 25% and made patients respond 20% better to treatments.
• Less time spent on paperwork (40% less) means doctors and nurses can focus more on patients.
• Patients felt more engaged and happy because AI systems gave them personalized help 24/7.

Case Study 2: AI-driven Transportation Solutions

• Autonomous Vehicles: The Future of Transportation

In recent years, the transportation industry has witnessed a paradigm shift with the emergence of autonomous vehicles powered by artificial intelligence (AI). This case study delves into the innovative use of AI-driven transportation solutions, particularly focusing on autonomous vehicles and their potential to revolutionize the way people and goods are transported.

• Overview of Case Study

A big transportation company started using AI to make self-driving cars. These cars have fancy sensors, smart algorithms, and can think quickly without people helping. Because of this, the company is making travel safer, faster, and better for the environment.

• Adoption of AI Technologies

AI technology has made a big difference in how self-driving cars work in transportation. It helps these cars learn and adjust to different traffic situations, making roads safer for everyone. By predicting dangers and finding the best routes, AI also helps reduce traffic jams and pollution, making cities more sustainable.

• Outcomes and Benefits

AI-powered transportation solutions bring many benefits for both companies and society:

• Safer Roads: Autonomous vehicles using AI have fewer accidents than human-driven ones, making roads safer.
• Faster Travel: AI helps vehicles make quick decisions based on traffic, reducing travel times and making transportation more efficient.
• Environmentally Friendly: AI optimizes routes and reduces fuel use, lowering emissions and helping the environment.
• Economic Growth: AI in transportation creates jobs in AI development and maintenance, fostering innovation and competition in the industry.

Case Study 3: AI Transforming Financial Services

• AI’s Impact on Finance and Banking
• AI technologies have revolutionized financial services, offering innovative solutions to enhance efficiency, accuracy, and security.
• Key areas impacted include fraud detection, risk management, customer service, and personalized financial advice.
• AI-powered systems automate processes, analyze data in real-time, and improve decision-making within financial institutions.
• The case study focuses on a leading bank’s implementation of AI-driven fraud detection and risk management systems.
• AI algorithms and machine learning models analyze vast financial data, enabling proactive identification of fraud and risks.
• Automation enhances customer asset protection, prevents financial crimes, and ensures regulatory compliance.
• Integration of AI in Financial Processes
• AI integration streamlines operations by analyzing transaction patterns, detecting anomalies, and flagging suspicious activities.
• Improves accuracy in credit scoring, loan approvals, and investment recommendations, offering personalized financial services.
• Empowers financial professionals to make informed decisions swiftly, based on AI-generated insights and data analysis.
• Achievements and Lessons Learned
• Reduced fraud-related losses and improved operational efficiency through AI-driven solutions.
• Strengthened customer trust by enhancing security measures and offering personalized financial services.
• Continuously optimized processes, staying ahead of emerging threats and market trends.
• Highlighted the scalability and adaptability of AI technologies in reshaping financial services for sustainable growth in the digital era.

Case Study 4: AI Enhancing Marketing Strategies

• AI helps businesses understand their customers better by analyzing data like age, behavior, and preferences.
• It improves marketing by creating personalized content based on customer interests and behavior across websites, social media, and emails.
• AI enhances advertising effectiveness by targeting specific customer groups and making ads more relevant and successful.
• Company Background: To do this, they used AI tools in their marketing. They collected and analyzed data, used AI to divide customers into groups, and automated personalized campaigns.
• Objectives: Their goal was to improve their marketing using AI. They wanted to give customers personalized experiences and make them more engaged and loyal.
• Implementation Process: To do this, they used AI tools in their marketing. They collected and analyzed data, used AI to divide customers into groups, and automated personalized campaigns.
• Application of AI in Marketing Campaigns
• Predictive Analytics: AI-driven predictive analytics helped the company anticipate customer behavior and trends. This helped in planning ahead for marketing, like sending special deals to customers who are likely to buy.
• Recommendation Engines: These engines used AI to look at customer info and suggest things they might like to buy based on what they’ve liked before. This made it easier to sell more things to each customer.
• Dynamic Content Generation: AI made it possible to create changing content, like personalized emails and ads that match what each customer likes and does. This increased engagement and conversion rates by delivering relevant and timely messages.
• Success Metrics and Insights
• The use of AI in marketing helped the company connect better with customers. This meant more people clicking on ads, opening emails, and interacting on social media.
• The personalized ads also made more people buy things, which increased sales and income.
• Overall, using AI in marketing was a good investment. It made marketing campaigns work better and saved money by automating tasks and making them more efficient.
• By using AI to analyze data, the company learned more about what customers liked and how to improve marketing strategies for even better results in the future.

Case Study 5: Education and AI

• Adaptive Learning Platforms
• AI-powered adaptive learning platforms analyze how students learn and what they like to provide personalized lessons and activities.
• These platforms change lesson plans as students progress, making sure they get the help they need and have the best learning experience possible.
• By adapting to each student’s way of learning, these platforms keep students interested and motivated, which helps them learn better.
• Educational institutions and companies that specialize in educational technology (EdTech) work together to create new and smart platforms that use AI. These platforms change how we teach in schools.
• These platforms keep checking data all the time and use smart computer programs to find ways to make things better. They then change the content to fit what students need.
• Because students get to learn in a way that suits them best, they remember more and do better in their studies.
• Integration of AI in Education Services
• Educators use AI tools to make special lesson plans, quizzes, and tests that match each student’s needs.
• AI analytics help teachers see how students are doing, so they can make smart choices about how to teach better.
• AI in education helps all kinds of learners, making sure everyone gets the support they need to learn well.

Personalized learning means that students get to learn in a way that suits them best. For example, if a student likes to learn through videos, they can have more video lessons. This helps them stay interested and do better in their studies.

AI tools are like smart helpers for teachers. They can give teachers useful information about how each student is doing. This way, teachers can plan lessons that fit each student’s needs and give them the right support.

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Using AI in education makes it possible for students to learn from anywhere, like at home or in a different country. It doesn’t matter how much money a school has because AI tools can help everyone learn better.

It’s really important to make sure that everyone can benefit from AI technology in schools. This means making sure that all students, no matter where they come from or what they’re good at, can use AI tools to learn and succeed.

The AI studies we’ve seen show how powerful artificial intelligence is in different areas like healthcare, finance, education etc. They prove that AI can bring big changes, making things work better, creating more personalized experiences, and helping businesses grow in a smart way. As more companies use AI, they’re likely to succeed more and make their customers happier in our fast-changing digital world.

• Q. How has AI impacted the healthcare sector? 

AI has improved patient care through early disease detection and personalized treatments, reducing healthcare costs and enhancing efficiency.

Q . What benefits has AI brought to the retail industry? 

AI-driven personalized recommendations have boosted sales and customer loyalty by offering tailored shopping experiences based on customer behavior.

Q . How does AI contribute to fraud detection in financial services? 

AI-powered fraud detection systems analyze real-time financial transactions, mitigating risks and increasing trust among customers and financial institutions.

Q . What role does AI play in predictive maintenance in manufacturing? 

AI-driven predictive maintenance systems prevent equipment failures, minimizing downtime and optimizing production processes in manufacturing plants.

• Q. How has AI transformed education delivery? 

AI-powered adaptive learning platforms personalize education by analyzing students’ learning patterns, improving academic outcomes and engagement.

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Online Case Study Answer Generator for Students

Here Is Your Case Study Analysis

If you want to quickly and effectively carry out case study analyses, you’ve come to the right place. Just for you, we’ve created a free AI-powered tool that can analyze case studies on any subject!

Our app will be the perfect solution for those who don’t want to spend a ton of time structuring their texts and looking for examples. Use it to save time and nerves!

• ️🎉 Benefits of Our Generator
• ️🤖 How to Use
• ️✨ Case Study Definition
• ️🔎 Structure of a Case Study
• ️✍️ Writing Steps
• ️🔝 Top 12 Topics & Examples
• ️🔗 References

🎉 Benefits of Our Case Study Analysis Generator

Our generator is one of the best, and there are many reasons for us to say so:

🤖 How to Use Our Case Study Answer Generator

Getting a case study analysis has never been easier—see for yourself!

• Paste your case study into the field.
• Add questions or issues you need to address in your analysis.
• Press “Analyze now.”
• Get the results!

Keep in mind that the results provided by the tool are to be used for reference purposes only.

✨ Case Study Analysis Definition

A case study analysis is used to examine a problem and find a solution to it. This type of analysis is typically used in business as well as in other spheres, such as education, healthcare, and social sciences. The main feature of such a study is that it’s rooted in a real-life context.

Researchers use direct observations, interviews, tests, and samples to gather data for their case studies. This information is then used to develop solutions and recommendations backed with evidence.

🔎 Structure of a Case Study Analysis

Usually, a case study analysis consists of 6 parts. Each one is dedicated to a particular aspect and serves its own purpose. Let’s take a closer look at them and see how they differ.

Introduction

An introduction describes the context of the examined topic and provides substantial background on the case study’s subject. When you write it, keep in mind the following questions:

• What is your case study about?
• What is the primary goal of your research?
• Why is it important?

Problem Statement

The next part introduces the main problem or issue the study will be focusing on. Typically, it’s concerned with a challenge faced by an individual or organization in question. The problem statement provides a clear focus for the whole research.

Now, it’s time for the most interesting part—the analysis itself. When it comes to business problems, students can use various approaches, such as:

This part presents several ways to resolve the issue in question. The solutions must be realistic and achievable. It’s also worth to mention their pros and cons and thus identify the most potent ones.

Recommendations

This part focuses on the best possible solutions to the problem identified in the previous section. It explains how to implement it in practice and how it will help eliminate the issue. It may also suggest ways to deal with other, minor problems involved in the case.

Conclusions

Now, it’s time for the final part of the analysis: your conclusions . Here is what you need to do:

• Summarize the results of your case study analysis and explain how they relate to the research’s main problem.
• Be sure to emphasize how vital your study is and how it helps to make the issue more manageable.
• Make further suggestions based on your findings.

✍️ How to Write a Case Study

Now you know what to include in your case study. But how do you write one that is truly outstanding? Just follow our step-by-step guide:

1. Pick a Case to Explore

Choosing the right topic is essential. You need to do it early on to ensure that the research subject is sufficiently explored.

For example, suppose you want to examine how COVID-19 has affected the hospitality sector. In that case, you can choose either a representative case, such as a large hotel chain, or an outlier case, such as a small Bed and Breakfast that has managed to survive the pandemic. The latter case may sound more interesting, but if there's not enough information available on it, it's best to choose the former.

2. Formulate a Problem Statement

Now, you should clearly and concisely formulate the central problem you will be focusing on. To do it, answer the 5 Ws:

• What is the problem you’re researching?
• Who is affected by it?
• Where does it occur?
• When did the problem arise?
• Why is this issue significant?

If you need help with this part of your analysis, you can always use our research problem generator .

3. Gather Evidence & Collect Data

Data gathering can be done through both primary and secondary sources of information . You can use a range of research techniques, such as observations, surveys, and interviews. It is crucial to make sure the data you’ve collected is pertinent to the case study.

4. Describe Your Findings & Analyze Them

Next, you analyze trends and themes in your data. This analysis must be supported by facts and evidence. Use various analysis methods to make your study more in-depth.

5. Provide Solutions & Recommendations

Develop several possible solutions using the information you’ve gathered. Once you’ve done it, answer the following questions:

• What are the pros and cons of these solutions?
• Which one can be the most beneficial?
• How can the entity you’re analyzing implement it in practice?

The more detailed your recommendations are, the better. If possible, try to include aspects such as timeline, resource allocation, and KPIs for monitoring.

🔝 Top 12 Case Study Topics & Examples

Want inspiration for your analysis? Or maybe you need help picking a case to explore? Check out this list of topics with examples!

• Operations and Information Management: A Case Study of CC Music
• Netflix and Blockbuster: Case Study
• Strategic Planning Case Study: Process Management
• HRM Incident: Case Study Analysis
• Case Study Summary: Hiring a Sustainable Development Specialist
• Organizational Change: Qatargas Case Study
• Childhood Development Case Study
• Case Study of Engstrom Auto Mirror Plant and Workplace
• Strategic Marketing: Amazon Go Case Study
• Cognitive Behavioral Therapy: Case Study
• Social Determinants of Health: Case Study
• Recovering Supply Chain Operations: A Case Study of Nissan

Now you know how to complete a case study! Remember that the tiring process of analyzing can be effectively streamlined if you use our free case study answer generator. Try it out—you won’t regret it!

We also recommend using our transition words maker and personal statement generator to enhance your writing.

❓ Case Study Analysis Generator: FAQ

❓ what questions to answer in a case study.

A case study must either prove or disprove an existing theory. It also aims to find a solution to the research's central question. This question can vary depending on your topic and subject. You present the answer in your research findings and conclusions.

❓ How Do You Write a Case Study Analysis?

First, you introduce your case and provide its background. Then, you gather information and analyze it to develop several solutions. Finally, you propose the best solution and give recommendations on how to implement it. Also, remember to explain how your case study will deepen the existing knowledge.

❓ What Are the 4 Most Important Parts of Case Study?

Every case study begins with the introduction of a topic and its background. Then, you present an analysis of sources that can provide knowledge on the case. The third part is the analysis of collected data. Your case study ends with conclusions based on your findings.

❓ What Are Some Examples of Case Studies?

Case studies are frequently used in psychology to shed light on peculiar circumstances. Famous case study examples include Sigmund Freud's Little Hans as well as John Martin Marlow's study of Phineas Gage, the man who had a railroad spike driven through his brain.

🔗 References

• Case Study: Definition, Examples, Types, and How to Write: Verywell Mind
• What Is a Case Study?: Evidence Based Nursing
• What the Case Study Method Really Teaches: Harvard Business Review
• Using Case Studies to Teach: Boston University
• What Is a Case Study? Definition, Elements and 15 Examples: Indeed
• Writing a Case Study: University of Southern California
• Writing a Case Study – Student Academic Success: Monash University

100+ AI Use Cases & Applications: In-Depth Guide for 2024

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

AI is changing every industry and business function, which results in increased interest in AI, its subdomains, and related fields such as machine learning and data science as seen below. With the launch of ChatGPT , interest in generative AI , a subfield of AI, exploded.

According to a recent McKinsey survey, 55% of organizations are using AI in at least one business function. 1 To integrate AI into your own business, you need to identify how AI can serve your business, possible use cases of AI in your business.

This article gathers the most common AI use cases covering marketing, sales, customer services, security, data, technology, and other processes.

Generative AI Use Cases

Generative AI involves AI models generating output in requests where there is not a single right answer (e.g. creative writing). Since the launch of ChatGPT , it has been exploding in popularity. Its use cases include content creation for marketing, software code generation, user interface design and many others.

For more: Generative AI use cases .

Business Functions

> ai use cases for analytics, general solutions.

• Analytics Platform : Empower your employees with unified data and tools to run advanced analyses. Quickly identify problems and provide meaningful insights.
• Analytics Services : Satisfy your custom analytics needs with these e2e solution providers. Vendors are there to help you with your business objectives by providing turnkey solutions.
• Automated Machine Learning (autoML) : Machines helping data scientists optimize machine learning models. With the rise of data and analytics capabilities, automation is needed in data science. AutoML automates time consuming machine learning tasks, enabling companies to deploy models and automate processes faster.

Specialized solutions

• Conversational Analytics : Use conversational interfaces to analyze your business data. Natural Language Processing is there to help you with voice data and more enabling automated analysis of reviews and suggestions.
• E-Commerce Analytics : Specialized analytics systems designed to deal with the explosion of e-commerce data. Optimize your funnel and customer traffic to maximize your profits.
• Geo-Analytics Platform : Enables analysis of granular satellite imagery for predictions. Leverage spatial data for your business goals. Capture the changes in any landscape on the fly.
• Image Recognition and Visual Analytics : Analyze visual data with advanced image and video recognition systems. Meaningful insights can be derived from the data piles of images and videos.
• Real-Time Analytics : Real-Time Analytics for your time-sensitive decisions. Act timely and keep your KPI’s intact. Use machine learning to explore unstructured data without any disruptions.

> AI use cases for Customer Service

• Call Analytics : Advanced analytics on call data to uncover insights to improve customer satisfaction and increase efficiency. Find patterns and optimize your results. Analyze customer reviews through voice data and pinpoint, where there is room for improvement. Sestek indicates that ING Bank observed a 15% increase in sales quality score and a 3% decrease in overall silence rates after they integrated AI into their contact systems .
• Call Classification : Leverage natural language processing (NLP) to understand what the customer wants to achieve so your agents can focus on higher value-added activities. Before channeling the call, identify the nature of your customers’ needs and let the right department handle the problem. Increase efficiency with higher satisfaction rates.
• Call Intent Discovery : Leverage Natural Language Processing and machine learning to estimate and manage customer’s intent (e.g., churn) to improve customer satisfaction and business metrics. Sentiment analysis through the customer’s voice level and pitch. Detect the micro-emotions that drive the decision-making process. Explore how chatbots detect customer intent in our in-depth article on intent recognition .
• Chatbot for Customer Service (Self – Service Solution) : Chatbots can understand more complicated queries as AI algorithms improve. Build your own 24/7 functioning, intelligent, self-improving chatbots to handle most queries and transfer customers to live agents when needed. Reduce customer service costs and increase customer satisfaction. Reduce the traffic on your existing customer representatives and make them focus on the more specific needs of your customers. Read for more insights on chatbots in customer service or discover chatbot platforms .
• Chatbot Analytics : Analyze how customers are interacting with your chatbot. See the overall performance of your chatbot. Pinpoint its shortcomings and improve your chatbot. Detect the overall satisfaction rate of your customer with the chatbot.
• Chatbot testing : Semi-automated and automated testing frameworks facilitate bot testing. See the performance of your chatbot before deploying. Save your business from catastrophic chatbot failures. Detect the shortcomings of your conversational flow.
• Customer Contact Analytics : Advanced analytics on all customer contact data to uncover insights to improve customer satisfaction and increase efficiency. Utilize Natural Language Processing for higher customer satisfaction rates.
• Customer Service Response Suggestions : Bots will listen in on agents’ calls suggesting best practice answers to improve customer satisfaction and standardize customer experience. Increase upsells and cross-sells by giving the right suggestion. Responses will be standardized, and the best possible approach will serve the benefit of the customer.
• Social Listening & Ticketing : Leverage Natural Language Processing and machine vision to identify customers to contact and respond to them automatically or assign them to relevant agents, increasing customer satisfaction. Use the data available in social networks to uncover whom to sell and what to sell.
• Intelligent Call Routing : Route calls to the most capable agents available. Intelligent routing systems incorporate data from all customer interactions to optimize the customer satisfaction. Based on the customer profile and your agent’s performance, you can deliver the right service with the right agent and achieve superior net promoter scores. Feel free to read case studies about matching customer to right agent in our emotional AI examples article .
• Survey & Review Analytics : Leverage Natural Language Processing to analyze text fields in surveys and reviews to uncover insights to improve customer satisfaction and increase efficiency. Automate the process by mapping the right keywords with the right scores. Make it possible to lower the time for generating reports. Protobrand states that they used to do review analytics manually through the hand-coding of the data, but now it automates much of the analytical work with Gavagai. This helps the company to collect larger quantitative volumes of qualitative data and still complete the analytical work in a timely and efficient manner. You can read more about survey analytics from  our related article .
• Voice Authentication : Authenticate customers without passwords leveraging biometry to improve customer satisfaction and reduce issues related to forgotten passwords. Their unique voice id will be their most secure key for accessing confidential information. Instead of the last four digits of SSN, customers will gain access by using their voice.

> AI use cases for Cybersecurity

Data loss prevention (DLP) software leverage AI technologies to achieve

• Real time detection of sensitive data beyond those identified using rules-based approached
• Intelligent access control learning from allowed data access patterns to reduce false positives

For more, see best practices for using AI in DLP .

Network monitoring

Typical use cases include:

• Anomaly detection in network traffic to identify cyberattacks
• Automated network optimization to manage peak loads at optimal cost without harming user experience.

For real-life examples: AI in network monitoring

> AI use cases for Data

• Data Cleaning & Validation Platform : Avoid garbage in, garbage out by ensuring the quality of your data with appropriate data cleaning processes and tools. Automate the validation process by using external data sources. Regular maintenance cleaning can be scheduled, and the quality of the data can be increased.
• Data Integration : Combine your data from different sources into meaningful and valuable information. Data traffic depends on multiple platforms. Therefore, managing this huge traffic and structuring the data into a meaningful format will be important. Keep your data lake available for further analysis. 
• Data Management & Monitoring : Keep your data high quality for advanced analytics. Adjust the quality by filtering the incoming data. Save time by automating manual and repetitive tasks.
• Data Preparation Platform : Prepare your data from raw formats with data quality problems to a clean, ready-to-analyze format. Use extract, transform, and load (ETL) platforms to fine-tune your data before placing it into a data warehouse.
• Data Transformation : Transform your data to prepare it for advanced analytics. If it is unstructured, adjust it for the required format.
• Data Visualization : Visualize your data for better analytics and decision-making. Let the dashboards speak. Convey your message more easily and more esthetically.
• Data Labeling : Unless you use unsupervised learning systems, you need high quality labeled data. Label your data to train your supervised learning systems. Human-in-the-loop systems auto label your data and crowdsource labeling data points that cannot be auto-labeled with confidence.
• Synthetic Data :  Computers can artificially create synthetic data to perform certain operations. The synthetic data is usually used to test new products and tools, validate models, and satisfy AI needs. Companies can simulate not yet encountered conditions and take precautions accordingly with the help of synthetic data. They also overcome the privacy limitations as it doesn’t expose any real data. Thus, synthetic data is a smart AI solution for companies to simulate future events and consider future possibilities. You can have more information on synthetic data from  our related article .

> AI use cases for Finance

Finance business function led by the CEO completes numerous repetitive tasks involving quantitative skills which makes them a good fit for AI transformation:

• Billing / invoicing reminders : Leverage accessible billing services that remind your customers to pay with generative AI powered messages.
• Blackbaud AP automation
• Dynamics AP automation
• NetSuite AP automation
• SAGE AP automation

For more, see AI use cases in AP automation .

> AI use cases for HR

• Employee Monitoring : Monitor your employees for better productivity measurement. Provide objective metrics to see how well they function. Forecast their overall performance with the availability of massive amounts of data.
• Hiring :  Hiring is a prediction game: Which candidate, starting at a specific position, will contribute more to the company? Machine and recruiting chatbots ‘ better data processing capabilities augment HR employees in various parts of hiring such as finding qualified candidates, interviewing them with bots to understand their fit or evaluating their assessment results to decide if they should receive an offer. 
• HR Analytics : HR analytics services are like the voice of employee analysis. Look at your workforce analytics and make better HR decisions. Gain actionable insights and impactful suggestions for higher employee satisfaction.
• HR Retention Management : Predict which employees are likely to churn and improve their job satisfaction to retain them. Detect the underlying reasons for their motive for seeking new opportunities. By keeping them at your organization, lower your human capital loss.
• Performance Management : Manage your employees’ performance effectively and fairly without hurting their motivation. Follow their KPI’s on your dashboard and provide real-time feedback. This would increase employee satisfaction and lower your organization’s employee turnover. Actualize your employee’s maximum professional potential with the right tools.

You can also read our article on HR technology trends .

> AI use cases for Marketing

A 2021 survey conducted among global marketers revealed that 41% of respondents saw an increase in revenue growth and improved performance due to the use of AI in their marketing campaigns.

Marketing can be summarized as reaching the customer with the right offer, the right message, at the right time, through the right channel, while continually learning. To achieve success, companies can leverage AI-powered tools to get familiar with their customers better, create more compelling content, and perform personalized marketing campaigns. AI can provide accurate insights and suggest smart marketing solutions that would directly reflect on profits with customer data. You can find the top three AI use cases in marketing:

• Marketing analytics :  AI systems learn from, analyze, and measure marketing efforts. These solutions track media activity and provide insights into PR efforts to highlight what is driving engagement, traffic, and revenue. As a result, companies can provide better and more accurate marketing services to their customers. Besides PR efforts, AI-powered marketing analytics can lead companies to identify their customer groups more accurately. By discovering their loyal customers, companies can develop accurate marketing strategies and also retarget customers who have expressed interest in products or services before. Feel free to read more about marketing analytics with AI from  this article .
• Personalized Marketing:  The more companies understand their customers, the better they serve them. AI can assist companies in this task and support them in giving personalized experiences for customers. As an example, suppose you visited an online store and looked at a product but didn’t buy it. Afterward, you see that exact product in digital ads. More than that, companies can send personalized emails or special offers and recommend new products that go along with customers’ tastes.
• Context-Aware Marketing : You can leverage machine vision and natural language processing (NLP) to understand the context where your ads will be served. With context-aware advertising, you can protect your brand and increase marketing efficiency by ensuring your message fits its context, making static images on the web come alive with your messages.

For more, check out AI use cases in marketing or AI for email marketing . AI-powered email marketing software is among the first AI tools that marketers should work with.

> AI use cases for Operations

• Cognitive / Intelligent Automation : Combine robotic process automation (RPA) with AI to automate complex processes with unstructured information. Digitize your processes in weeks without replacing legacy systems , which can take years. Bots can operate on legacy systems learning from your personnel’s instructions and actions. Increase your efficiency and profitability ratios. Increase speed and precision, and many more. Feel free to check intelligent automation use cases for more.
• Robotic Process Automation (RPA) Implementation : Implementing RPA solutions requires effort. Suitable processes need to be identified. If a rules-based robot will be used, the robot needs to be programmed. Employees’ questions need to be answered. That is why most companies get some level of external help. Generally, outsourcing companies, consultants, and IT integrators are happy to provide temporary labor to undertake this effort.
• Process Mining : Leverage AI algorithms to mine your processes and understand your actual processes in detail. Process mining tools can provide fastest time to insights about your as-is processes as demonstrated in case studies . Check out process mining use cases & benefits for more.
• Predictive Maintenance : Predictively maintain your robots and other machinery to minimize disruptions to operations. Implement big data analytics to estimate the factors that are likely to impact your future cash flow. Optimize PP&E spending by gaining insight regarding the possible factors.
• Inventory & Supply Chain Optimization : Leverage machine learning to take your inventory& supply chain optimization to the next level. See the possible scenarios in different customer demands. Reduce your stock, keeping spending, and maximize your inventory turnover ratios. Increase your impact factor in the value chain.
• Building Management : Sensors and advanced analytics improve building management. Integrate IoT systems in your building for lower energy consumption and many more. Increase the available data by implementing the right data collection tools for effective building management.
• Digital Assistant : Digital assistants are mature enough to replace real assistants in email communication. Include them in your emails to schedule meetings. They have already scheduled hundreds of thousands of meetings.

> AI use cases for Sales

• Sales Forecasting :  AI allows automatic and accurate sales forecasts based on all customer contacts and previous sales outcomes. Automatically forecast sales accurately based on all customer contacts and previous sales outcomes. Give your sales personnel more sales time while increasing forecast accuracy. Hewlett Packard Enterprise indicates that it has experienced a 5x increase in forecast simplicity, speed, and accuracy with Clari’s sales forecasting tools.
• Lead generation :  Use a comprehensive data profile of your visitors to identify which companies your sales reps need to connect. Generate leads for your sales reps leveraging databases and social networks
• Sales Data Input Automation: Data from various sources will be effortlessly and intelligently copied into your CRM. Automatically sync calendar, address book, emails, phone calls, and messages of your salesforce to your CRM system. Enjoy better sales visibility and analytics while giving your sales personnel more sales time.
• Predictive sales/lead scoring: Use AI to enable predictive sales. Score leads to prioritize sales rep actions based on lead scores and contact factors. Sales forecasting is automated with increased accuracy thanks to systems’ granular access to lead scores and sales rep performance. For scoring leads, these systems leverage anonymized transaction data from their customers, sales data of this specific customer. For assessing contact factors, these systems leverage anonymized data and analyze all customer contacts such as email and calls.
• Sales Rep Response Suggestions: AI will suggest responses during live conversations or written messages with leads. Bots will listen in on agents’ calls suggesting best practice answers to improve sales effectiveness
• Sales Rep Next Action Suggestions : Your sales reps’ actions and leads will be analyzed to suggest the next best action. This situation wise solution will help your representatives to find the right way to deal with the issue. Historical data and profile of the agent will help you to achieve higher results. All are leading to more customer satisfaction.
• Sales Content Personalization and Analytics: Preferences and browsing behavior of high priority leads are analyzed to match them with the right content, aimed to answer their most important questions. Personalize your sales content and analyze its effectiveness allowing continuous improvement.
• Retail Sales Bot : Use bots on your retail floor to answer customer’s questions and promote products. Engage with the right customer by analyzing the profile. Computer vision will help you to provide the right action depending on the characteristics and mimics of the customer.
• Meeting Setup Automation (Digital Assistant): Leave a digital assistant to set up meetings freeing your sales reps time. Decide on the targets to prioritize and keep your KPI’s high.
• Prescriptive Sales : Most sales processes exist in the mind of your sales reps. Sales reps interact with customers based on their different habits and observations. Prescriptive sales systems prescribe the content, interaction channel, frequency, price based on data on similar customers .
• Sales Chatbot : Chatbots are ideal to answer first customer questions. If the chatbot decides that it can not adequately serve the customer, it can pass those customers to human agents. Let 24/7 functioning, intelligent, self-improving bots handle making initial contacts to leads. High value, responsive leads will be called by live agents, increasing sales effectiveness.

Sales analytics

As Gartner discusses , sales analytic systems provide functionality that supports discovery, diagnostic, and predictive exercises that enable the manipulation of parameters, measures, dimensions, or figures as part of an analytic or planning exercise. AI algorithms can automate the data collection process and present solutions to improve sales performance. To have more detailed information, you can read  our article about sales analytics .

• Customer Sales Contact Analytics :  Analyze all customer contacts, including phone calls or emails, to understand what behaviors and actions drive sales. Advanced analytics on all sales call data to uncover insights to increase sales effectiveness
• Sales Call Analytics : Advanced analytics on call data to uncover insights to increase sales effectiveness. See how well your conversation flow performs. Integrating data on calls will help you to identify the performance of each component in your sales funnels.
• Sales attribution :  Leverage big data to attribute sales to marketing and sales efforts accurately. See which step of your sales funnel performs better. Pinpoint the low performing part by the insights provided by analysis.
• Sales Compensation :  Determine the right compensation levels for your sales personnel. Decide on the right incentive mechanism for the sales representatives. By using the sales data, provide objective measures, and continuously increase your sales representatives’ performance.

For more on AI in sales .

> AI use cases for Strategy & Legal

• Presentation preparation : Top management presentations in most companies involve slides (e.g. PowerPoint). Generative AI presentation software can prepare slides from prompts.

Legal counsels can rely on AI in:

• Contract drafting
• Contract review
• Legal research

For more: Legal AI software

> AI use cases for Tech

• No code AI & app development : AI and App development platforms for your custom projects. Your in-house development team can create original solutions for your specific business needs.
• Analytics & Predictive Intelligence for Security : Analyze data feeds about the broad cyber activity as well as behavioral data inside an organization’s network to come up with actionable insights to help analysts predict and thwart impending attacks. Integrate external data sources the watch out for global cyber threats and act timely. Keep your tech infrastructure intact or minimize losses. 
• Knowledge Management : Enterprise knowledge management enables effective and effortless storage and retrieval of enterprise data, ensuring organizational memory. Increased collaboration by ensuring the right people are working with the right data. Seamless organizational integration through knowledge management platforms.
• Natural Language Processing Library/ SDK/ API : Leverage Natural Language Processing libraries/SDKs/APIs to quickly and cost-effectively build your custom NLP powered systems or to add NLP capabilities to your existing systems. An in-house team will gain experience and knowledge regarding the tools. Increased development and deployment capabilities for your enterprise.
• Image Recognition Library/ SDK/ API :  Leverage image recognition libraries/SDKs/APIs to quickly and cost-effectively build your custom image processing systems or to add image processing capabilities to your existing systems.
• Secure Communications : Protect employee communications like emails or phone conversations with advanced multilayered cryptography & ephemerality. Keep your industry secrets safe from corporate espionage.
• Deception Security : Deploy decoy-assets in a network as bait for attackers to identify, track, and disrupt security threats such as advanced automated malware attacks before they inflict damage. Keep your data and traffic safe by keeping them engaged in decoys. Enhance your cybersecurity capabilities against various forms of cyber attacks
• Autonomous Cybersecurity Systems : Utilize learning systems to efficiently and instantaneously respond to security threats, often augmenting the work of security analysts. Lower your risk of human errors by providing greater autonomy for your cybersecurity. AI-backed systems can check compliance with standards.
• Smart Security Systems : AI-powered autonomous security systems. Functioning 24/7 for achieving maximum protection. Computer vision for detecting even the tiniest anomalies in your environment. Automate emergency response procedures by instant notification capabilities.
• Machine Learning Library/ SDK/ API : Leverage machine learning libraries/SDKs/APIs to quickly and cost-effectively build your custom learning systems or to add learning capabilities to your existing systems.
• AI Developer : Develop your custom AI solutions with companies experienced in AI development. Create turnkey projects and deploy them to the specific business function. Best for companies with limited in-house capabilities for artificial intelligence.
• Deep Learning Library/ SDK/ API : Leverage deep learning libraries/SDKs/APIs to quickly and cost-effectively build your custom learning systems or to add learning capabilities to your existing systems.
• Developer Assistance : Assist your developers using AI to help them intelligently access the coding knowledge on the web and learn from suggested code samples. See the best practices for specific development tasks and formulate your custom solution. Real-time feedback provided by the huge history of developer mistakes and best practices.
• AI Consultancy : Provides consultancy services to support your in-house AI development, including machine learning and data science projects. See which units can benefit most from AI deployment. Optimize your artificial intelligence spending for the best results from the insight provided by a consultant.

> AI use cases for Automotive & Autonomous Things

Autonomous things including cars and drones are impacting every business function from operations to logistics.

• Driving Assistant : Required components and intelligent solutions to improve rider’s experience in the car. Implement AI-Powered vehicle perception solutions for the ultimate driving experience.
• Vehicle Cybersecurity : Secure connected and autonomous cars and other vehicles with intelligent cybersecurity solutions. Guarantee your safety by hack-proof mechanisms. Protect your intelligent systems from attacks.
• Vision Systems : Vision systems for self-driving cars. Integrate vision sensing and processing in your vehicle. Achieve your goals with the help of computer vision.
• Self-Driving Cars : From mining to manufacturing, self-driving cars/vehicles are increasing the efficiency and effectiveness of operations. Integrate them into your business for greater efficiency. Leverage the power of artificial intelligence for complex tasks.

> AI use cases for Education

• Course creation

For more: Generative AI applications in education

> AI use cases for Fashion

• Creative Design
• Virtual try-on
• Trend analysis

For more: Generative AI applications in fashion

> AI use cases for FinTech 

• Fraud Detection : Leverage machine learning to detect fraudulent and abnormal financial behavior, and/or use AI to improve general regulatory compliance matters and workflows. Lower your operational costs by limiting your exposure to fraudulent documents.
• Insurance & InsurTech : Leverage machine learning to process underwriting submissions efficiently and profitably, quote optimal prices , manage claims effectively, and improve customer satisfaction while reducing costs. Detect your customer’s risk profile and provide the right plan.
• Financial Analytics Platform : Leverage machine learning, Natural Language Processing, and other AI techniques for financial analysis, algorithmic trading, and other investment strategies or tools.
• Travel & expense management : Use deep learning to improve data extraction from receipts of all types including hotel, gas station, taxi, grocery receipts. Use anomaly detection and other approaches to identify fraud, non-compliant spending. Reduce approval workflows and processing costs per unit.
• Credit Lending & Scoring : Use AI for robust credit lending applications. Use predictive models to uncover potentially non-performing loans and act. See the potential credit scores of your customers before they apply for a loan and provide custom-tailored plans.
• Loan recovery: Increase loan recovery ratios with empathetic and automated messages.
• Robo-Advisory : Use AI finance chatbot and mobile app assistant applications to monitor personal finances. Set your target savings or spending rates for your own goals. Your finance assistant will handle the rest and provide you with insights to reach financial targets.
• Regulatory Compliance : Use Natural Language Processing to quickly scan legal and regulatory text for compliance issues, and do so at scale. Handle thousands of paperwork without any human interaction.
• Data Gathering : Use AI to efficiently gather external data such as sentiment and other market-related data. Wrangle data for your financial models and trading approaches.
• Debt Collection : Leverage AI to ensure a compliant and efficient debt collection process. Effectively handle any dispute and see your success right in debt collection.
• Conversational banking : Financial institutions engage with their customers on a variety of communication platforms ( WhatsApp , mobile app , website etc.) via conversational AI tools to increase customer satisfaction and automate many tasks like customer onboarding .

> AI use cases for HealthTech

• Patient Data Analytics : Analyze patient and/or 3rd party data to discover insights and suggest actions. Greater accuracy by assisted diagnostics. Lower the mortality rates and increase patient satisfaction by using all the diagnostic data available to detect the underlying reasons for the symptoms.
• Personalized Medications and Care : Find the best treatment plans according to patient data. Provide custom-tailored solutions for your patients. By using their medical history, genetic profile, you can create a custom medication or care plan.
• Drug Discovery : Find new drugs based on previous data and medical intelligence. Lower your R&D cost and increase the output — all leading to greater efficiency. Integrate FDA data, and you can transform your drug discovery by locating market mismatches and FDA approval or rejection rates.
• Real-Time Prioritization and Triage : Prescriptive analytics on patient data enabling accurate real-time case prioritization and triage. Manage your patient flow by automatization. Integrate your call center and use language processing tools to extract the information, priorate patients that need urgent care, and lower your error rates. Eliminate error-prone decisions by optimizing patient care.
• Early Diagnosis : Analyze chronic conditions leveraging lab data and other medical data to enable early diagnosis. Provide a detailed report on the likelihood of the development of certain diseases with genetic data. Integrate the right care plan for eliminating or reducing the risk factors.
• Assisted or Automated Diagnosis & Prescription :  Suggest the best treatment based on the patient complaint and other data. Put in place control mechanisms that detect and prevent possible diagnosis errors. Find out which active compound is most effective against that specific patient. Get the right statistics for superior care management.
• Pregnancy Management : Monitor mother and fetus health to reduce mothers’ worries and enable early diagnosis. Use machine learning to uncover potential risks and complications quickly. Lower the rates of miscarriage and pregnancy-related diseases.
• Medical Imaging Insights : Advanced medical imaging to analyze and transform images and model possible situations. Use diagnostic platforms equipped with high image processing capabilities to detect possible diseases.
• Healthcare Market Research : Prepare hospital competitive intelligence by tracking market prices. See the available insurance plans, drug prices, and many more public data to optimize your services. Leverage NLP tools to analyze the vast size of unstructured data.
• Healthcare Brand Management and Marketing : Create an optimal marketing strategy for the brand based on market perception and target segment. Tools that offer high granularity will allow you to reach the specific target and increase your sales.
• Gene Analytics and Editing : Understand genes and their components and predict the impact of gene edits.
• Device and Drug Comparative Effectiveness : Analyze drug and medical device effectiveness. Rather than just using simulations, test on other patient’s data to see the effectiveness of the new drug, compare your results with benchmark drugs to make an impact with the drug.
• Healthcare chatbot :  Use a chatbot to schedule patient appointments, give information about certain diseases or regulations, fill in patient information, handle insurance inquiries, and provide mental health assistance. You can also use intelligent automation with chatbot capabilities.

For more, feel free to check our article on the  use cases of AI in the healthcare industry .

> AI use cases for Manufacturing

• Manufacturing Analytics : Also called industrial analytics systems, these systems allow you to analyze your manufacturing process from production to logistics to save time, reduce cost, and increase efficiency. Keep your industry effectiveness at optimal levels.
• Collaborative Robots : Cobots provide a flexible method of automation. Cobots are flexible robots that learn by mimicking human workers’ behavior.
• Robotics : Factory floors are changing with programmable collaborative bots that can work next to employees to take over more repetitive tasks. Automate physical processes such as manufacturing or logistics with the help of advanced robotics. Increased your connected systems by centralizing the whole manufacturing process. Lower your exposures to human errors.

> AI use cases for Non-Profits

• Personalized donor outreach and engagement based on historical data to increase fundraising levels while avoiding email fatigue.
• Donor identification via techniques like look-alike audiences.

See more use cases of AI in fundraising .

> AI use cases for Retail

• Cashierless Checkout : Self-checkout systems have many names. They are called cashierless, cashier-free, or automated checkout systems. They allow retail companies to serve customers in their physical stores without the need for cashiers. Technologies that allowed users to scan and pay for their products have been used for almost a decade now, and those systems did not require great advances in AI. However, these days we are witnessing systems powered by advanced sensors and AI to identify purchased merchandise and charge customers automatically.

> AI use cases for Telecom

• Network investment optimization : Both wired and wireless operators need to invest in infrastructure like active equipment or higher bandwidth connections to improve Quality of Service (QoS). Machine learning can be used to identify highest ROI investments that will result in less churn and higher cross and up-sell.

Other AI Use Cases

This was a list of areas by business function where out-of-the-box solutions are available. However, AI, like software, has too many applications to list here. You can also take a look at our  AI in business article  to read about AI applications by industry. Also, feel free to check our article on AI services .

It is important to get started fast with high impact applications and generate business value without spending months of effort. For that, we recommend companies to use no code AI solutions to quickly build AI models .

Once companies deploy a few models to production, they need to take a deeper look at their AI/ML development model.

• rely on autoML software to build complex AI models. Though most autoML software is not as easy to use as no code AI solutions, they can be used to build complex models.
• build custom AI solutions in-house
• work with the support of partners to build custom models
• run data science competitions to build custom AI models
• Use pre-trained models built by AI vendors

We examined the pros and cons of this approaches in our article on making the build or buy decisions regarding AI .

You can also check out our list of AI tools and services:

• AI Consultant
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• Data Science / ML / AI Platform

These articles about AI may also interest you:

• Ultimate Guide to the State of AI technology
• Future of AI according to top AI experts
• Advantages of AI according to top practitioners

What is artificial intelligence (AI)?

Artificial Intelligence (AI) is the branch of computer science that focuses on creating machines capable of performing tasks that typically require human intelligence. This includes activities such as learning, problem-solving, understanding natural language, speech recognition, and visual perception. AI systems can analyze large amounts of data, identify patterns, and make decisions, often with speed and accuracy surpassing human capabilities.

What are the examples of AI in real life?

Artificial Intelligence (AI) is integrated into many aspects of daily life. Some common real-life examples include:

Virtual Assistants: Like Siri, Alexa, and Google Assistant, these AI-powered tools understand and respond to voice commands, performing tasks like setting reminders, answering questions, and controlling smart home devices.

Navigation and Maps: AI is used in services like Google Maps and Waze for route optimization, traffic prediction, and providing real-time directions.

Recommendation Systems: Streaming services like Netflix and Spotify use AI to analyze your viewing or listening history to recommend movies, shows, or music.

Autonomous Vehicles: Self-driving cars use AI to perceive the environment and make decisions for safe navigation.

Social Media: Platforms like Facebook and Instagram use AI for content curation, targeted advertising, and facial recognition in photos.

Security and Surveillance: AI aids in anomaly detection, facial recognition, and monitoring systems for enhanced security.

How does AI impact employment and job creation?

AI impacts employment by automating routine tasks, which can lead to job displacement in some sectors. However, it also creates new job opportunities in AI development, data analysis, and other tech-related fields, emphasizing the need for skill adaptation.

For more, you can check our article on the ethics of AI .

What are some misconceptions about AI?

Common misconceptions include the idea that AI can fully replicate human intelligence, that it’s always unbiased, or that AI-led automation will universally eliminate jobs. In reality, AI has limitations, can inherit biases from data, and often changes rather than replaces job roles.

And if you have a specific business challenge, we can help you find the right vendor to overcome that challenge:

External links

Though most use cases have been categorized based on our experience, we also took a look at Tractica’s AI use cases list before finalizing the list. Other sources:

• 1. “ The state of AI in 2023: Generative AI’s breakout year “. Quantum Black AI by McKinsey . August 1, 2023. Accessed January 1, 2024

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

AIMultiple.com Traffic Analytics, Ranking & Audience , Similarweb. Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics , Business Insider. Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are , Washington Post. Data management barriers to AI success , Deloitte. Empowering AI Leadership: AI C-Suite Toolkit , World Economic Forum. Science, Research and Innovation Performance of the EU , European Commission. Public-sector digitization: The trillion-dollar challenge , McKinsey & Company. Hypatos gets $11.8M for a deep learning approach to document processing , TechCrunch. We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million , Business Insider.

To stay up-to-date on B2B tech & accelerate your enterprise:

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Vertical ai / horizontal ai & other specialized ai models in 2024, ai in analytics: how ai is shaping analytics in 2024 in 4 ways, ai in marketing: comprehensive guide in 2024.

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Good afternoon. I am very curious about your claim that “Elekta has reduced its costs and increased its number of processed invoices from 50,000 to 120,000.” Do you have the source for this claim?

Hello, Aidan. We weren’t able to find the source. So we removed it entirely. Thanks for pointing it out!

We can say that AI is the future of our world. While AI is penetrating in more and more human works, thus creating a demand of AI Industry, AI in healthcare is one of the most surging category in global AI Market. According to Meridian Market Consultants, The global AI in Healthcare Market in 2020 is estimated for more than US$ 5.0 Bn and expected to reach a value of US$ 107.5 Bn by 2028 with a significant CAGR of 47.3%. SOI:

47.3% CAGR? You are so sure about the future. Why don’t you guys just sell the time machine rather than the report?

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Top 10 artificial intelligence case studies: recap and future trends

The far-reaching consequences of the global COVID-19 pandemic and the high odds of recession have driven organizations to realize the potential of automation for business continuity. As a result, over the last few years, we have witnessed an all-time high number of artificial intelligence case studies .

According to McKinsey, 57 percent of companies report AI adoption, up from 45 percent in 2020. The majority of these applications targeted the optimization of service operations, a much-needed shift in these turbulent times. Beyond service optimization, AI case studies have been spotted across virtually all industries and functional activities.

Today, we’ll have a look at some of the most exciting business use cases that owe their advent to artificial intelligence and its offshoots.

What is the business value of artificial intelligence?

According to PwC, AI development can rack in an additional $15.7 trillion of the global economic value by 2030. In 2022, 92% of respondents have indicated positive and measurable business results from their prior investments in AI and data initiatives.

However, there are other benefits that incentivize companies to tap into artificial intelligence case studies.

Reduced costs

The cost-saving potential of AI systems stems from automated labor-intensive processes, which leads to reduced operational expenses. For example, Gartner predicts that conversational AI will reduce contact center labor costs by $80 billion in 2026.

Indirect cost reduction of smart systems is associated with optimizing operations with precise forecasting, predictive maintenance, and quality control.

Amplified decision-making

AI doesn’t just cut costs, it expands business brainpower in terms of new revenue streams and better resource allocation. Smart data analysis allows companies to make faster, more accurate, and consistent decisions by capitalizing on datasets and predicting the optimal course of action. AI consulting comes in especially handy when bouncing back from crises.

Source: Unsplash

Lower risks

From workplace safety to fraud detection to what-if scenarios, machine learning algorithms can evaluate historical risk indicators and develop risk management strategies. Automated systems can also be used to automate risk assessment processes, identify risks early, and monitor risks on an ongoing basis. Thus, 56% of insurance companies see the biggest impact of AI in risk management.

Better business resilience

Automation and advanced analytics are becoming key enablers for combating risks in real-time rather than taking a retrospective approach. As 81% of CEOs predict a recession in the coming years, companies can protect their core by predicting transition risks, closing supply and demand gaps, and optimizing resources – based on artificial intelligence strategy .

Top 10 AI case studies: from analytics to pose tracking

Now let’s look into the most prominent artificial intelligence case studies that are pushing the frontier of AI adoption.

Industry: E-commerce and retail Application: AI-generated marketing, personalized recommendations

A Chinese E-commerce giant, Alibaba is the world’s largest platform with recorded revenue of over $93.5 billion in Chinese online sales. No wonder, that the company is vested in maximizing revenue by optimizing the digital shopping experience with artificial intelligence.

Its well-known case study on artificial intelligence includes an extensive implementation of algorithms to improve customer experience and drive more sales. Alibaba Cloud Artificial Intelligence Recommendation (AIRec) leverages Alibaba’s Big data to generate real-time, personalized recommendations on Alibaba-owned online shopping platform Taobao and across the number of Double 11 promotional events.

The company also uses NLP to help merchants automatically generate product descriptions.

Mayo Clinic

Industry: healthcare Application: medical data analytics

Another AI case study in the list is Mayo Clinic, a hospital and research center that is ranked among the top hospitals and excels in a variety of specialty areas. Intelligent algorithms are used there in a large number of business use cases – both administrative and clinical.

The use of computer algorithms on ECG in Mayo’s cardiovascular medicine research helps detect weak heart pumps by analyzing data from Apple Watch ECGs. The research center is also a staunch advocate of AI medical imaging where machine learning is applied to analyze image data fast and at scale.

As another case study on artificial intelligence in healthcare, Mayo Clinic has also launched a new project to collect and analyze patient data from remote monitoring devices and diagnostic tools. The sensor and wearables data can then be analyzed to improve diagnoses and disease prediction.

Deutsche Bank

Industry: banking Application: fraud detection

Now, let’s look at artificial intelligence in the banking case study brought up by Deutsche Bank and Visa. The two companies partnered up in 2022 to eliminate online retail fraud. Merchants who process their E-commerce payments via Deutsche Bank can now rely on a smart fraud detection system from Visa-owned company Cybersource.

Driven by pre-defined rules, the system automatically calculates a risk value for each transaction. The system employs risk models and data from billions of data points on the Visa network. This allows for blocking fraudulent transactions and faster authorizing other transactions.

Industry: E-commerce Application: supply and demand prediction

Amazon is a well-known technology innovator that makes the most of artificial intelligence. From data analysis to route optimization, the company injects automation at all stages of the whole supply chain. Over the last few years, the company has perfected its forecasting algorithm to make a unified forecasting model that predicts even fluctuating demand.

Let’s look at its AI in E-commerce case study. When toilet paper sales surged by 213% during the pandemic, Amazon’s predictive forecasting allowed the company to respond quickly to the sudden spike and adjust the supply levels to the market needs.

Blue River Technology

Industry: agriculture Application: computer vision

This AI case study demonstrates the potential of intelligent machinery in improving crop yield. Blue River Technology, a California-based machinery enterprise, aims to radically change agriculture through the adoption of robotics and machine learning. The company equips farmers with sustainable and effective intelligent solutions to manage crops.

Their company’s flagship product, See & Spray, relies on computer vision, machine learning, and advanced robotic technology to distinguish between crops and weeds. The machine then delivers a targeted spray to weeds. According to the company, this innovation can reduce herbicide use by up to 80 percent.

Industry: automotive Application: voice recognition

The car manufacturer has over 400 AI & ML case studies at all levels of production. According to the company, these technologies play an essential role in the production of new vehicles and augment automated driving with advanced, natural experience.

In particular, voice recognition allows drivers to adjust the in-car settings such as climate and driving mode, or even choose the preferred song. BMW owners can also use the voice command to ask the car about its performance status, get guidance on specific vehicle functions, and input a destination.

Industry: media and entertainment Application: emotion recognition

Another exciting case study about artificial intelligence is Affectiva company and its flagship AI products. The company conceived a new technological dimension of Artificial Emotional Intelligence, named Emotion AI. This application allows publishers to optimize content and media spending based on the customers’ emotional responses.

Emotion AI is fuelled by a combination of computer vision and deep learning to discern nuanced emotions and cognitive states by analyzing facial movement.

Industry: manufacturing Application: process optimization

As global enterprises are looking for more ways to optimize, the demand for automation grows. Siemens’ collaboration with Google is a prominent case study on the application of artificial intelligence in factory automation. The manufacturer has teamed up with Google to drive up shop floor productivity with edge analytics.

The expected results are to be achieved via computer vision, cloud-based analytics, and AI algorithms. Optimization will most likely leverage the connection of Google’s data cloud with Siemens’ Digital Industries Factory Automation tools. This will allow companies to unify their factory data and run cloud-based analytics and AI at scale.

Industry: manufacturing Application: semiconductor development

Along with cutting-edge solutions like its memory accelerator, the manufacturing conglomerate also implements AI to automate the highly complex process of designing computer chips. A prominent artificial intelligence case study is Samsung using Synopsys AI software to design its Exynos chips. The latter are used in smartphones, including branded handsets and other gadgets.

Industry: manufacturing Application: predictive maintenance

According to McKinsey , the greatest value from AI in manufacturing will be delivered from predictive maintenance, which accounts for $0.5-$0.7 trillion in value worldwide. The snack food manufacturer and PepsiCo’s subsidiary, Frito-Lay, has followed suit.

The company has a long track record of using predictive maintenance to enhance production and reduce equipment costs. Paired with sensors, this case study of artificial intelligence helped the company reduce planned downtime and add 4,000 hours a year of manufacturing capacity.

Looking over horizon: Technology trends for 2023-2024

Although artificial intelligence case studies are likely to account for the majority of innovations, the exact form and shape of intelligent transformation can vary. Below, you will find the likely successors of AI technologies in the coming years.

Advanced connectivity

Advanced connectivity refers to the various ways in which devices can connect and share data. It includes technologies like 5G, the Internet of Things, edge computing, wireless low-power networks, and other innovations that facilitate seamless and fast data sharing.

The global IoT connectivity imperative has been driven by cellular IoT (2G, 3G, 4G, and now 5G) as well as LPWA over the last five years. Growing usage of medical IoT, IoT-enabled manufacturing, and autonomous vehicles have been among the greatest market enablers so far.

Web 3.0 is the new iteration of the Internet that aims to make the digital space more user-centered and enables users to have full control over their data. The concept is premised on a combination of technologies, including blockchain, semantic web, immersive technology, and others.

Metaverse generally refers to an integrated network of virtual worlds accessed through a browser or headset. The technology is powered by a combination of virtual and augmented reality.

Edge computing

Edge computing takes cloud data processing to a new level and focuses on delivering services from the edge of the network. The technology will enable faster local AI data analytics and allow smart systems to deliver on performance and keep costs down. Edge computing will also back up autonomous behavior for Internet of Things (IoT) devices.

Industries already incorporate devices with edge computing, including smart speakers, sensors, actuators, and other hardware.

Augmented analytics

Powered by ML and natural language technologies, augmented analytics takes an extra step to help companies glean insights from complex data volumes. Augmented analytics also relies on extensive automation capabilities that streamline routine manual tasks across the data analytics lifecycle, reduce the time needed to build ML models, and democratize analytics.

Large-sized organizations often rely on augmented analytics when scaling their analytics program to new users to accelerate the onboarding process. Leading BI suites such as Power BI, Qlik, Tableau, and others have a full range of augmented analytics capabilities.

Engineered decision intelligence

The field of decision intelligence is a new area of AI that combines the scientific method with human judgment to make better decisions. In other words, it’s a way to use machine intelligence to make decisions more effectively and efficiently in complex scenarios.

Today, decision intelligence assists companies in identifying risks and frauds, improving sales and marketing as well as enhancing supply chains. For example, Mastercard employs technology to increase approvals for genuine transactions.

Data Fabric

Being a holistic data strategy, data fabric leverages people and technology to bridge the knowledge-sharing gap within data estates. Data fabric is based on an integrated architecture for managing information with full and flexible access to data.

The technology also revolves around Big data and AI approaches that help companies establish elastic data management workflows.

Quantum computing

An antagonist of conventional computing, the quantum approach uses qubits as a basic unit of information to speed up analysis to a scale that traditional computers cannot ever match. The speed of processing translates into potential benefits of analyzing large datasets – faster and at finer levels.

Hyperautomation

This concept makes the most of intelligent technologies to help companies achieve end-to-end automation by combining AI-fuelled tools with Robotic Process Automation. Hyperautomation strives to streamline every task executed by business users through ever-evolving automated pathways that learn from data.

Thanks to a powerful duo of artificial intelligence and RPA, the hyperautomated architecture can handle undocumented procedures that depend on unstructured data inputs – something that has never been possible.

Turning a crisis into an opportunity with AI

In the next few years, businesses will have to operate against the backdrop of the looming recession and financial pressure. The only way of standing firmly on the ground is to save resources, which usually leaves just two options: layoffs or resource optimization.

While the first option is a moot point, resource optimization is a time-tested method to battle uncertainty. And there’s no technology like artificial intelligence that can better audit, identify, validate, and execute the optimal transition strategy for virtually any industry. From better marketing messages to voice-controlled vehicles, AI adds a new dimension to your traditional business operations.

AI technology to combat recession

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Artificial Intelligence Case Study Topics

Looking for artificial intelligence case study topics? Explore real-life examples and learn how AI is transforming industries like healthcare, finance, manuf...

Artificial Intelligence Case Study Topics: Unleashing the Power of AI

Artificial Intelligence (AI) has emerged as one of the most transformative technologies in recent times, revolutionizing industries and reshaping the way we live and work. With its ability to analyze vast amounts of data, learn from patterns, and make autonomous decisions, AI has the potential to solve complex problems and unlock new possibilities. One of the key drivers of AI advancements is the utilization of case studies, which provide real-world examples of AI applications and their impact.

Introduction to AI Case Studies

Case studies serve as invaluable resources in understanding the practical applications of AI. They offer insights into how AI technologies are implemented, the challenges faced, and the outcomes achieved. By examining successful AI case studies, we can gain a deeper understanding of the potential of AI and how it can be harnessed to drive innovation and improve various aspects of our lives.

The Importance of AI Case Studies

AI case studies play a pivotal role in showcasing the capabilities of AI systems and their potential impact. These studies enable researchers, developers, and businesses to learn from past experiences, identify best practices, and avoid potential pitfalls. By studying successful AI case studies, decision-makers can make informed choices when implementing AI solutions, ensuring maximum efficiency and effectiveness.

Purpose of the Blog Post

The purpose of this blog post is to provide an in-depth exploration of artificial intelligence case study topics. We will delve into various industries and domains where AI has made significant strides, examining real-life examples and their impact. By the end of this comprehensive guide, you will have a clear understanding of the potential applications of AI across different sectors and gain insights into how these case studies have transformed industries.

Overview of Artificial Intelligence Case Studies

Before we dive into specific case studies, let's first establish a foundational understanding of AI case studies. These case studies involve the application of AI technologies to address a specific problem or challenge. They provide a detailed account of how AI systems were developed, implemented, and the outcomes achieved.

AI case studies offer a multifaceted perspective, encompassing various industries, including healthcare, finance, manufacturing, customer service, and transportation. Each case study presents a unique set of challenges and opportunities, highlighting the versatility and adaptability of AI in different contexts.

Real-life Examples of Successful AI Case Studies

To truly grasp the potential of AI, it is essential to explore real-life examples of successful AI case studies. These pioneering projects have showcased the transformative power of AI, pushing the boundaries of what was once thought possible. Let's take a glimpse into some notable AI case studies:

1. Google DeepMind's AlphaGo

In 2016, Google's DeepMind developed AlphaGo, an AI system that defeated the world champion Go player, Lee Sedol. This groundbreaking achievement highlighted the ability of AI to master complex strategic games that were previously considered beyond the reach of machines. AlphaGo's success demonstrated the potential of AI in problem-solving and decision-making in complex scenarios.

2. IBM Watson's Jeopardy! Victory

IBM's Watson showcased its cognitive capabilities by competing against human champions on the popular quiz show, Jeopardy! in 2011. Watson's ability to understand and process natural language, coupled with its vast knowledge base, enabled it to outperform the human contestants. This case study demonstrated the potential of AI in understanding and analyzing unstructured data, paving the way for advancements in natural language processing.

3. Tesla's Autopilot System

Tesla's Autopilot system utilizes AI algorithms and sensors to enable semi-autonomous driving. By analyzing real-time data from cameras, radar, and ultrasonic sensors, the Autopilot system can detect and respond to road conditions, other vehicles, and pedestrians. This case study showcases the potential of AI in the transportation industry, revolutionizing the concept of self-driving cars.

4. Amazon's Recommendation Engine

Amazon's recommendation engine is powered by AI algorithms that analyze customer preferences, purchase history, and browsing behavior to provide personalized product recommendations. This case study demonstrates how AI can enhance the customer experience by delivering targeted suggestions, improving sales, and fostering customer loyalty.

These real-life examples are just the tip of the iceberg when it comes to AI case studies. They illustrate the diverse range of industries and domains where AI has made significant contributions, showcasing the potential for innovation and transformation.

In the next section, we will explore the process of selecting artificial intelligence case study topics, considering various factors and identifying the most relevant and impactful areas of study. Stay tuned for an in-depth analysis of AI case studies in healthcare, finance, manufacturing, customer service, and transportation.

Note: In the following sections, we will explore each case study topic in greater detail, analyzing the problem at hand, the AI solution implemented, and the results and impact achieved.

Artificial intelligence (AI) case studies provide valuable insights into the practical applications and impact of AI technologies. These case studies offer a glimpse into the real-world implementation of AI systems, showcasing their capabilities, successes, and challenges. By examining these case studies, we can gain a deeper understanding of the potential of AI and its ability to transform various industries.

Explanation of AI Case Studies

AI case studies involve the application of AI technologies to solve specific problems or challenges within a given context. These studies provide detailed accounts of how AI systems were developed, implemented, and the outcomes achieved. By analyzing the methodologies and approaches used in these case studies, researchers, developers, and businesses can learn from past experiences and gain insights into the best practices for implementing AI solutions.

AI case studies often involve the utilization of machine learning algorithms, natural language processing, computer vision, robotics, and other AI techniques. They can range from small-scale projects to large-scale deployments, depending on the complexity of the problem being addressed.

Benefits of AI Case Studies

AI case studies offer numerous benefits for both researchers and practitioners in the field of AI. Here are some key advantages:

Insights into Implementation : Case studies offer insights into the practical implementation of AI systems. They provide details on the data collection process, model training, algorithm selection, and optimization techniques employed. This information can guide future AI projects and help avoid common pitfalls.

Benchmarking and Comparison : Case studies allow for benchmarking and comparison of different AI approaches. By examining multiple case studies within a specific domain, researchers can identify the strengths and weaknesses of various AI techniques, leading to advancements and improvements in the field.

Inspiration for Innovation : AI case studies can inspire new ideas and innovative solutions. By understanding the challenges faced in previous case studies and the methods used to overcome them, researchers can build upon existing knowledge and push the boundaries of AI capabilities.

To truly comprehend the power and potential of AI, it is essential to explore real-life examples of successful AI case studies. These examples highlight the impact that AI can have across various domains. Let's take a closer look at some notable AI case studies:

Google DeepMind's AlphaGo : AlphaGo, developed by Google DeepMind, made headlines in 2016 when it defeated the world champion Go player, Lee Sedol. This case study demonstrated the ability of AI to master complex strategic games and showcased the potential for AI in decision-making and problem-solving.

IBM Watson's Jeopardy! Victory : In 2011, IBM's Watson competed against human champions on the quiz show Jeopardy! and emerged victorious. Watson's success demonstrated the power of AI in natural language processing and understanding unstructured data.

Tesla's Autopilot System : Tesla's Autopilot system utilizes AI algorithms and sensors to enable semi-autonomous driving. This case study showcases the potential of AI in the transportation industry, revolutionizing the concept of self-driving cars.

Amazon's Recommendation Engine : Amazon's recommendation engine utilizes AI to analyze customer preferences and provide personalized product recommendations. This case study highlights how AI can enhance the customer experience and drive sales through targeted suggestions.

These real-life examples illustrate the diverse range of industries and domains where AI has made significant contributions. They serve as inspiration and provide valuable insights into the potential of AI technologies.

Choosing Artificial Intelligence Case Study Topics

When exploring the world of artificial intelligence case studies, it is essential to select the right topics that align with current AI trends and have the potential for significant impact. In this section, we will discuss the factors to consider when choosing case study topics and identify some promising areas for exploration.

Factors to Consider

Relevance to Current AI Trends : Selecting case study topics that align with current AI trends ensures that you are exploring areas of research and development that are actively advancing. Staying up-to-date with the latest advancements in AI will provide you with a better understanding of the challenges and opportunities in the field.

Availability of Data : Data availability is crucial for successful AI case studies. Consider topics where relevant and high-quality data is accessible. Adequate data sets are essential for training AI models effectively and obtaining reliable results.

Ethical Considerations : Ethical considerations should be an integral part of AI case study topic selection. It is important to choose topics that adhere to ethical guidelines and prioritize fairness, transparency, and accountability. Avoid topics that raise concerns regarding privacy, bias, or potential harm to individuals or society.

Identifying Potential Case Study Topics

Now, let's explore some potential case study topics in various industries where AI has shown promising applications:

Healthcare and Medical Diagnostics : AI has the potential to revolutionize healthcare by improving diagnostics, predicting disease outcomes, and enabling personalized treatment plans. Some potential case study topics in this domain include:

AI in Early Cancer Detection: Explore how AI algorithms can analyze medical imaging data to detect and diagnose cancer at an early stage, leading to improved patient outcomes.

AI in Medical Imaging Analysis: Investigate how AI can assist radiologists in analyzing medical images, such as X-rays, MRIs, and CT scans, to improve accuracy and speed in diagnosis.

Financial Services and Fraud Detection : AI offers significant potential in the finance industry, particularly in fraud detection and prevention. Some potential case study topics in this domain include:

AI in Fraud Detection for Banks: Examine how AI algorithms can analyze transaction data and detect fraudulent activities in real-time, enhancing security and minimizing financial losses.

AI in Credit Card Fraud Detection: Explore how AI can analyze patterns and anomalies in credit card transactions to identify and prevent fraudulent activities, ensuring the safety of customers' financial information.

Manufacturing and Process Optimization : AI can optimize manufacturing processes, improve efficiency, and reduce costs. Some potential case study topics in this domain include:

AI in Predictive Maintenance: Investigate how AI can analyze sensor data to predict machinery failures and schedule maintenance proactively, minimizing downtime and optimizing production.

AI in Supply Chain Optimization: Explore how AI algorithms can optimize supply chain operations by predicting demand, optimizing inventory levels, and improving logistics, leading to cost savings and improved customer satisfaction.

Customer Service and Chatbots : AI-powered chatbots have revolutionized customer service by providing instant responses and personalized experiences. Some potential case study topics in this domain include:

AI-powered Chatbots in E-commerce: Examine how AI-powered chatbots can enhance customer engagement, provide personalized product recommendations, and streamline the online shopping experience.

AI in Virtual Assistants for Customer Support: Explore how AI-based virtual assistants can handle customer inquiries, resolve issues, and provide 24/7 support, improving customer satisfaction and reducing support costs.

Transportation and Autonomous Vehicles : AI plays a critical role in the development of autonomous vehicles and traffic management systems. Some potential case study topics in this domain include:

AI in Self-Driving Cars: Investigate how AI algorithms enable autonomous vehicles to perceive the environment, make real-time decisions, and navigate safely on the roads.

AI in Traffic Management Systems: Explore how AI can optimize traffic flow, reduce congestion, and improve transportation efficiency by analyzing real-time traffic data and implementing intelligent control systems.

By considering these factors and exploring potential case study topics in various industries, you can select areas that align with your interests and have the potential to contribute to the advancement of AI technologies.

Deep Dive into Selected Artificial Intelligence Case Study Topics

In this section, we will delve deeper into selected artificial intelligence case study topics across various industries. By examining these case studies, we can gain a comprehensive understanding of the problem at hand, the AI solutions implemented, and the results and impact achieved.

Healthcare and Medical Diagnostics

Case Study: AI in Early Cancer Detection

Overview of the Problem: Early detection of cancer is crucial for successful treatment and improved patient outcomes. However, it can be challenging for healthcare professionals to accurately detect cancer at its early stages due to the complexity of medical imaging data and the potential for human error.

AI Solution and Implementation: In this case study, AI algorithms were developed and trained using large datasets of medical imaging data, including mammograms, CT scans, or MRIs. These algorithms utilize deep learning techniques to analyze and interpret the images, identifying potential cancerous cells or tumors. By comparing the patterns in the images to an extensive database of known cancer cases, the AI system can provide accurate early detection of cancer.

Results and Impact: The implementation of AI in early cancer detection has shown promising results. The AI system can analyze medical images with high accuracy, often outperforming human radiologists in detecting cancer at its early stages. Early detection allows for timely intervention, leading to improved treatment outcomes and increased survival rates for patients.

Case Study: AI in Medical Imaging Analysis

Overview of the Problem: Medical imaging, such as X-rays, MRIs, and CT scans, plays a crucial role in diagnosing and monitoring various medical conditions. However, the interpretation of these images can be time-consuming, subjective, and prone to human error.

AI Solution and Implementation: In this case study, AI algorithms were developed and trained using large datasets of labeled medical imaging data. These algorithms leverage deep learning techniques, such as convolutional neural networks (CNNs), to analyze and interpret the images. The AI system can identify anomalies, highlight potential abnormalities, and provide quantitative measurements to assist radiologists in making accurate diagnoses.

Results and Impact: The implementation of AI in medical imaging analysis has shown significant potential in improving diagnostic accuracy and efficiency. The AI system can assist radiologists in identifying subtle abnormalities that may be missed by the human eye, leading to early detection of diseases and improved patient care. Additionally, AI can help reduce the burden on radiologists by automating certain tasks, allowing them to focus on more complex cases.

Financial Services and Fraud Detection

Case Study: AI in Fraud Detection for Banks

Overview of the Problem: Fraudulent activities, such as identity theft and unauthorized transactions, pose significant challenges for banks and financial institutions. Traditional rule-based fraud detection systems often struggle to keep up with evolving fraud techniques and patterns.

AI Solution and Implementation: In this case study, AI algorithms were developed to analyze large volumes of transactional data in real-time. These algorithms utilize machine learning techniques, including anomaly detection and pattern recognition, to identify suspicious activities that deviate from normal patterns. By continuously learning from new data, the AI system can adapt and evolve to detect new and emerging fraud patterns.

Results and Impact: The implementation of AI in fraud detection for banks has led to improved fraud prevention and detection rates. The AI system can analyze vast amounts of transactional data quickly and accurately, flagging potentially fraudulent activities in real-time. By minimizing false positives and identifying fraudulent transactions promptly, banks can mitigate financial losses and protect their customers' assets.

Case Study: AI in Credit Card Fraud Detection

Overview of the Problem: Credit card fraud is a significant concern for both financial institutions and cardholders. Detecting fraudulent credit card transactions is challenging due to the large volume of transactions and the need for real-time analysis.

AI Solution and Implementation: In this case study, AI algorithms were developed to analyze credit card transaction data, including transaction amounts, merchant information, and cardholder behavior. These algorithms utilize machine learning techniques, such as supervised and unsupervised learning, to identify patterns and anomalies indicative of fraudulent activities. The AI system can learn from historical data to improve its fraud detection capabilities over time.

Results and Impact: The implementation of AI in credit card fraud detection has proven to be highly effective in reducing fraudulent activities. The AI system can quickly analyze transactions, identify suspicious patterns, and flag potentially fraudulent transactions for further investigation. By minimizing false positives and accurately detecting fraud, financial institutions can protect their customers and maintain trust in the credit card ecosystem.

In the next section, we will explore case studies in manufacturing and process optimization, showcasing how AI can enhance efficiency and streamline operations.

In this section, we will explore case studies in the domain of manufacturing and process optimization. These examples highlight how artificial intelligence (AI) can enhance efficiency, reduce costs, and streamline operations in manufacturing industries.

Manufacturing and Process Optimization

Case Study: AI in Predictive Maintenance

Overview of the Problem: Unplanned equipment failures and unexpected downtime can significantly impact manufacturing operations, leading to production delays and increased costs. Traditional maintenance strategies, such as reactive or preventive maintenance, may not effectively address the challenges of equipment failure prediction and maintenance scheduling.

AI Solution and Implementation: In this case study, AI algorithms were implemented to perform predictive maintenance. The algorithms utilize machine learning techniques, such as supervised learning and anomaly detection, to analyze sensor data from machines and predict potential failures. By continuously monitoring the health and performance of equipment, the AI system can identify early warning signs of impending failures and schedule maintenance proactively.

Results and Impact: The implementation of AI in predictive maintenance has proven to be highly beneficial for manufacturing industries. By detecting potential equipment failures in advance, companies can plan maintenance activities more efficiently, minimizing downtime and reducing costs associated with unscheduled repairs. This proactive approach to maintenance helps optimize production schedules and ensures smooth operations.

Case Study: AI in Supply Chain Optimization

Overview of the Problem: Supply chain management involves complex processes, including demand forecasting, inventory management, and logistics planning. Optimizing these processes is crucial for reducing costs, improving customer satisfaction, and increasing operational efficiency.

AI Solution and Implementation: In this case study, AI algorithms were utilized to optimize supply chain operations. The algorithms leverage machine learning techniques, such as demand forecasting, inventory optimization, and route optimization, to analyze historical and real-time data. By considering factors such as customer demand, lead times, transportation costs, and inventory levels, the AI system can generate optimal plans and recommendations for procurement, production, and distribution.

Results and Impact: The implementation of AI in supply chain optimization has led to significant improvements in efficiency and cost reduction. By accurately forecasting demand and optimizing inventory levels, companies can minimize stockouts and excess inventory, leading to reduced carrying costs and improved cash flow. AI-powered route optimization helps streamline logistics operations, optimizing delivery schedules and reducing transportation costs. These advancements in supply chain optimization ultimately lead to improved customer satisfaction through faster and more reliable deliveries.

These case studies highlight the potential impact of AI in manufacturing and process optimization. By leveraging AI technologies, companies can achieve greater efficiency, reduced costs, and improved operational effectiveness. In the next section, we will explore case studies in the domain of customer service and chatbots, showcasing how AI can enhance customer experiences and support interactions.

In this section, we will explore case studies in the domain of customer service and chatbots. These examples highlight how artificial intelligence (AI) can enhance customer experiences, streamline support interactions, and improve overall customer satisfaction.

Customer Service and Chatbots

Case Study: AI-powered Chatbots in E-commerce

Overview of the Problem: With the rise of e-commerce, providing personalized and timely customer support has become a crucial aspect of the online shopping experience. However, scaling customer service to meet the growing demands of a large customer base can be challenging and costly.

AI Solution and Implementation: In this case study, AI-powered chatbots were implemented to handle customer inquiries and provide support in e-commerce platforms. These chatbots utilize natural language processing (NLP) and machine learning algorithms to understand and respond to customer queries. They can provide instant and personalized responses, offer product recommendations based on customer preferences, and assist with order tracking and returns.

Results and Impact: The implementation of AI-powered chatbots in e-commerce has significantly improved customer experiences and operational efficiency. Chatbots provide instant responses, reducing customer wait times and ensuring 24/7 availability for support inquiries. By offering personalized product recommendations, chatbots can enhance the shopping experience and increase sales conversion rates. Additionally, chatbots can handle routine inquiries, freeing up human agents to focus on more complex customer issues, ultimately improving overall customer satisfaction.

Case Study: AI in Virtual Assistants for Customer Support

Overview of the Problem: Customer support departments often face high call volumes and long wait times, leading to customer frustration and decreased satisfaction. Providing timely and effective support to customers is critical for maintaining brand loyalty and positive customer experiences.

AI Solution and Implementation: In this case study, AI-powered virtual assistants were implemented to handle customer support interactions. These virtual assistants utilize AI technologies such as natural language processing, sentiment analysis, and knowledge graph systems. They can understand customer inquiries, provide accurate and personalized responses, and escalate complex issues to human agents when necessary. Virtual assistants continuously learn from customer interactions, improving their responses and problem-solving abilities over time.

Results and Impact: The implementation of AI-powered virtual assistants in customer support has proven to be highly effective in improving response times and customer satisfaction. Virtual assistants can provide instant support, reducing wait times and enabling customers to receive assistance at their convenience. By accurately understanding customer inquiries and providing relevant information, virtual assistants can resolve issues quickly and efficiently. This results in improved customer experiences, reduced support costs, and increased customer loyalty.

These case studies illustrate the potential of AI in enhancing customer service and support interactions. By leveraging AI-powered chatbots and virtual assistants, businesses can provide timely, personalized, and efficient support to their customers, resulting in improved customer satisfaction and loyalty. In the next section, we will explore case studies in the domain of transportation and autonomous vehicles, showcasing how AI is revolutionizing the way we travel and manage traffic.

In this section, we will explore case studies in the domain of transportation and autonomous vehicles. These examples highlight how artificial intelligence (AI) is revolutionizing the way we travel and manage traffic.

Transportation and Autonomous Vehicles

Case Study: AI in Self-Driving Cars

Overview of the Problem: Self-driving cars have the potential to transform the transportation industry by reducing accidents, improving traffic flow, and enhancing overall mobility. However, developing autonomous vehicles that can navigate safely and make real-time decisions in complex traffic scenarios is a significant challenge.

AI Solution and Implementation: In this case study, AI algorithms are utilized to power self-driving cars. These algorithms leverage a combination of computer vision, sensor fusion, machine learning, and decision-making models to perceive the environment, interpret traffic signs, detect obstacles, and make real-time driving decisions. By continuously analyzing sensor data and learning from past experiences, self-driving cars can navigate autonomously while adhering to traffic rules and ensuring passenger safety.

Results and Impact: The implementation of AI in self-driving cars has the potential to revolutionize transportation. Autonomous vehicles can reduce human errors and improve road safety by eliminating the risks associated with human distraction, fatigue, and impaired driving. Additionally, self-driving cars have the potential to optimize traffic flow, reduce congestion, and increase overall transportation efficiency, leading to reduced travel times and fuel consumption.

Case Study: AI in Traffic Management Systems

Overview of the Problem: Managing traffic flow in urban areas is a complex task that requires real-time analysis of traffic patterns, congestion, and accidents. Traditional traffic management systems often struggle to handle the dynamic nature of traffic and effectively optimize traffic flow.

AI Solution and Implementation: In this case study, AI algorithms are used to enhance traffic management systems. These algorithms leverage machine learning techniques and real-time data analysis to predict traffic congestion, optimize signal timings, and suggest alternative routes. By analyzing historical and real-time traffic data, the AI system can make intelligent decisions to improve traffic flow, reduce congestion, and minimize travel times.

Results and Impact: The implementation of AI in traffic management systems has shown significant potential in improving transportation efficiency. By optimizing signal timings based on real-time traffic conditions, AI can reduce congestion and ensure a smoother flow of vehicles. AI algorithms can also provide real-time traffic updates to drivers, enabling them to make informed decisions about alternative routes, further reducing travel times and improving overall traffic management.

These case studies highlight how AI is transforming the transportation industry. From self-driving cars to intelligent traffic management systems, AI technologies have the potential to revolutionize the way we travel, making transportation safer, more efficient, and environmentally friendly.

In this comprehensive guide, we have explored various artificial intelligence case study topics across different industries. We have witnessed the power of AI in healthcare, finance, manufacturing, customer service, and transportation. By examining real-life examples and understanding the problem-solving capabilities of AI, we have gained insights into the potential of this transformative technology.

AI case studies provide invaluable lessons and inspire innovation in the field of artificial intelligence. They offer opportunities for learning, benchmarking, and improving AI systems. By studying successful case studies, researchers, developers, and businesses can harness the power of AI to drive advancements, solve complex problems, and improve various aspects of our lives.

As AI continues to evolve, it is crucial to stay updated with the latest trends, research, and case studies. The potential of AI is immense, and by exploring and sharing knowledge, we can collectively shape a future where AI-driven solutions enhance our lives in remarkable ways.

Adrian Kennedy

Practice Your AI & Coding Skills with These Short-Form Case Studies

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As a developer or code enthusiast, you need to be as skilled in using AI tools as you are programming languages and frameworks. That’s a big reason why we recently enhanced our interactive learning environment with built-in AI tools that help you learn and expanded our catalog to include new AI-centered courses and case studies.

Case studies are project-oriented exercises that focus on specific applications of AI. With these short-form case studies, you’ll get hands-on experience working with a generative AI simulator that mimics ChatGPT and learn how to tactically leverage AI in your daily life . Along the way you’ll also complete projects that you can add to your portfolio or re-do with your own spin. Another bonus? You only need an hour to finish most of these case studies.

All you need to complete these free case studies is some foundational programming knowledge and a Codecademy account . The case studies with hands-on coding work use the programming language Python — but keep in mind that the concepts and techniques used here can transfer over to other languages. (You might want to take our free course Intro to ChatGPT as a primer before you jump in and use the AI simulator.)

Learn something new for free

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While you’ll be collaborating with some helpful AI systems in these exercises, it’s a great idea to seek out some human feedback, too. We recommend sharing your finished solutions in Codecademy forums to get perspective and pointers from your fellow learners. Read on to discover the types of free case studies that’ll make you a better programmer and AI practitioner.

Using AI tools to clean up code is more complicated than copying and pasting syntax into a chatbot and prompting it to “debug this, pls.” In Debug Python Code with Generative AI Case Study , you’ll gain proficiency working with generative AI to debug code for you. We’ll teach you to adeptly craft generative AI prompts that identify and resolve issues within your codebase and give you guidance on how to double-check the chatbot’s work. This skill makes the software development process much more efficient and will give you a leg up when it comes time to debug a project that you or a colleague built.

Code more efficiently

You’ll probably get to a point in your coding journey where you have to work with someone else’s code , whether you join an engineering team with a legacy codebase or collaborate on an open-source project. Get practice refactoring code so that it’s readable, maintainable, testable, and efficient in the case study Optimizing Code with Generative AI . We’ll give you a snippet of Python code that needs to be improved, and you’ll use a generative AI tool like ChatGPT to optimize and complete it.

Practice pair programming

You can use generative AI tools like ChatGPT to pair program . Instead of having another set of human eyes on your code, you’ll collaborate with an AI system to catch errors, provide feedback, and suggest improvements in real-time. In our case study Try Pair Programming with Generative AI , you’ll prompt AI to act as a driver in the driver-navigator style of pair programming. Of course, there are aspects of IRL pair programming that AI can’t replicate (like communicating effectively and building camaraderie), but this will simulate a pair programming scenario and help you become more familiar with the process.

Automate unit testing

Comprehensive unit tests are crucial for software development because they help you weed out bugs early in the development process. But thoroughly testing individual components and functionalities of code can get tedious, which is where AI tools can come in handy. Delve into unit tests for a Python program in the case study Unit Testing with Generative AI . You’ll get to generate unit tests for all the functions of a Python class, which is a technique you can replicate with more complicated projects in other languages, too.

Create marketing assets

Use generative AI tools like ChatGPT and DALL-E 3 to create marketing assets for a fictional company in the case study Creating Marketing Assets with Generative AI . Whether you’re a professional marketer or just curious how to strategize using generative AI, you’ll get hands-on practice writing prompts and generating content like ad copy or product descriptions. You can also take a closer look at these skills in the free course Prompt Engineering for Marketing .   

Adjust your writing tone

Knowing how to communicate information to different audiences and stakeholders is an awesome skill to have in your career, and AI can help you reword or translate written blurbs effectively. In Differentiate for Language and Reading Level with Generative AI , you’ll learn how generative AI can help you adjust the reading level of written material so it’s suitable for a range of audiences with varying English levels.

Build your resume

Are you in the thick of the job hunt, or casually keeping an eye on companies that are hiring? Get a head start on reworking your resume with the case study Streamline Resume Creation with Generative AI . We’ll show you how to use ChatGPT to craft a polished, professional, and tailored resume that fits a job description. We know that applying to jobs can feel like a chore, but you can refine your resume in an hour or less with this case study.

Get creative

Generative AI can be an incredible brainstorming tool because it’s capable of remixing ideas and instantly producing lots of different options for you to consider. In the case study Creating Quick STEAM Activities , we’ll have you assume the mindset of a teacher and use generative AI to help you come up with STEAM activities for students. Even if you’re not a teacher, this will introduce you to a handy use for generative AI that you can carry with you to your next project.

More AI courses and practice

With the new AI-powered features in our learning environment , you can gain experience using AI tools while you master a programming language in all our courses and paths. Explore the rest of our AI courses to get in-depth training on key topics in tech, and if you’re looking for more projects that you can do for practice or your portfolio, check out the projects library .

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Debug python code with generative ai case study, optimizing code with generative ai case study, pair programming with generative ai case study, unit testing with generative ai case study, create marketing assets with generative ai, differentiate for language and reading level with generative ai case study, streamline resume creation with generative ai case study, creating quick steam activities with generative ai case study, subscribe for news, tips, and more, related articles.

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AI Case Studies: How Intelligent Agents Can Improve Industries

From the automotive industry to call centers, ai is ready..

In this edition of Solving Hard Problems, our CEO Nissan Yaron explores how AI-powered tools are transforming various industries through three compelling case studies. Here are some highlights:

Empowering Sales with AI-Powered SDR Tools

Discover how sales teams are revolutionizing their processes with Autonomous Sales Development Representatives (SDR) agents. Powered by the Decentralized Intelligent Framework, these AI-driven tools provide immediate engagement with prospects, streamline qualifying conversations, and prioritize leads efficiently. Real-time insights available on an employer dashboard further enhance the effectiveness of sales strategies.

Enhancing Driving Experience with AI-Powered In-Car Assistants

See how AI-powered in-car assistants are enhancing the driving experience. By detecting issues early and engaging proactively with drivers, these assistants offer personalized recommendations and real-time assistance. Leveraging deep contextual understanding, they ensure safer, smoother, and more informed driving.

Revolutionizing Healthcare with AI-Powered Assistants

Learn about the benefits of AI-powered healthcare assistants in proactive patient engagement, accurate data retrieval, and natural language understanding. These tools improve patient adherence, enhance diagnostic accuracy, and bridge communication gaps between patients and healthcare providers, leading to better health outcomes.

Case Study: Empowering Sales with AI-Powered SDR Tools

Imagine a sales team empowered by Autonomous Sales Development Representatives (SDR) agents, driven by the Decentralised Intelligent Framework. This case study explores how AI-powered SDR tools can revolutionize sales processes, providing immediate engagement, qualifying conversations, lead triage and prioritization, and comprehensive insights through an employer dashboard. Here are some of the key benefits of this agent-powered system:

Immediate Engagement

When a new lead is captured, the prospect immediately receives an invitation to interact with the Autonomous SDR agent. This prompt engagement sets the tone for a dynamic and responsive sales journey, ensuring prospects are attended to without delay. A human can set the goal for the interaction - bill repayment, subscription update, address change - and the AI will maintain a course that focuses on that goal.

Qualifying Conversations

The AI-powered SDR agent initiates conversations by asking qualifying questions, assessing the prospect's needs, and providing relevant information. Its advanced natural language processing capabilities allow it to generate contextually appropriate responses, building trust and interest with the prospect. This personalized approach makes the interaction feel more human and engaging.

Lead Triage and Prioritization

The SDR agents analyze prospects' responses to determine their qualification status. Qualified leads receive personalized messages and calendar invitations, optimizing the sales team's time by focusing their efforts on the most promising opportunities. This efficient triage system ensures that no lead is overlooked and that high-priority prospects are given the attention they deserve.

Employer Dashboard and Insights

The Autonomous SDR agent provides real-time updates on the employer dashboard, including interaction summaries, prospect rankings, and insightful recommendations. This comprehensive view enables sales managers to make data-driven decisions, improving overall sales strategy and performance. The dashboard's analytics offer valuable insights into prospect behavior and preferences, helping to refine and tailor future interactions.

By leveraging AI-powered SDR tools driven by the Decentralized Intelligent Framework, sales teams can experience enhanced efficiency and effectiveness in lead engagement and qualification. The ability to provide immediate, personalized interactions, coupled with real-time insights and prioritization, empowers sales teams to optimize their efforts and achieve better outcomes.

Case Study: Enhancing Driving Experience with AI-Powered In-Car Assistants

Now consider a car equipped with an AI-powered in-car assistant driven by the Decentralized Intelligent Framework. Let’s explore how such an assistant can enhance the driving experience through issue detection, proactive engagement, contextual understanding, and real-time assistance.

Issue Detection and Proactive Engagement

When the framework detects a potential issue with the vehicle, the in-car companion agent, powered by LLM agents, proactively initiates a conversation with the driver. It provides relevant information about the detected issue and suggests potential solutions. These early detection and engagement tools help in addressing problems before they escalate, ensuring a safer and smoother driving experience.

Contextual Understanding

The AI-powered in-car assistant leverages global and state-based data models to understand the car's current state, maintenance history, and the driver's preferences. This deep contextual understanding allows the agent to offer personalized recommendations and solutions highly relevant to the situation. Whether it's suggesting the nearest service center or advising on minor maintenance tasks, the assistant ensures that its advice is tailored to the driver's needs.

During the conversation, the in-car assistant provides step-by-step guidance or tutorials to help the driver address the detected issue. If necessary, it can also connect the driver with a remote technician for further assistance. The agent adapts its responses based on the driver's needs and the car's context, ensuring that the support provided is practical and effective.

By integrating an AI-powered in-car assistant with the Decentralised Intelligent Framework, drivers can benefit from proactive issue detection, personalized recommendations, and real-time assistance. This combination not only enhances the safety and reliability of the vehicle but also improves the overall driving experience by providing timely and relevant support.

Case Study: An AI-Powered, Proactive Healthcare Assistant

Finally, let’s discuss the benefits of proactive patient engagement, contextual data retrieval, and natural language understanding in improving healthcare outcomes.

Proactive Patient Engagement

The healthcare assistant proactively engages with patients, providing timely reminders, educational content, and personalized health recommendations. By adapting its interactions based on patient needs, preferences, and medical history, the assistant ensures a proactive and personalized healthcare experience. This continuous engagement helps maintain patient adherence to treatment plans and promotes better health management.

Contextual Data Retrieval

Leveraging its ability to retrieve relevant medical data and consider patient context, the healthcare assistant provides accurate and comprehensive information to healthcare providers. This enhances diagnostic accuracy and improves treatment outcomes. The assistant's integration with patient records and medical databases ensures that the information provided is up-to-date and contextually relevant.

Natural Language Understanding

The healthcare assistant excels in understanding and interpreting patients' natural language queries. It provides clear, concise responses and explains complex medical concepts in simple terms, enhancing patient satisfaction and understanding. This capability bridges the communication gap between patients and healthcare providers, ensuring that patients are well-informed and comfortable with their care.

The implementation of a proactive healthcare assistant, powered by the Decentralized Intelligent Framework, revolutionizes patient engagement and healthcare delivery. By offering timely and personalized support, accurate data retrieval, and effective communication, the assistant significantly enhances patient care and satisfaction, leading to better health outcomes.

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AI Writing Tools for Case Study Solutions

Efficiency and accuracy.

AI writing tools for case study solutions offer unmatched efficiency and accuracy. These tools can quickly analyze complex data, identify key insights, and craft compelling case study content. By automating the process, they save time and ensure the accuracy of the information presented. This efficiency allows users to focus on strategy and implementation rather than spending excessive time on content creation.

Moreover, these AI tools for writing offer unparalleled accuracy in generating content. They can recognize patterns, extract relevant information, and present it in a coherent manner. This accuracy enhances the quality of the case study solutions, ensuring that the content is both informative and engaging.

Data-Driven Insights

Best AI writing tools empower users with data-driven insights for case study solutions. By processing large volumes of information, these tools extract valuable insights that can strengthen the content. They enable users to incorporate relevant statistics, industry trends, and supporting evidence into their case studies, enhancing their credibility and persuasiveness.

Furthermore, these writing AI tools facilitate the analysis of diverse data sources, allowing users to present a comprehensive view of the case study topic. This data-driven approach not only enriches the content but also provides a competitive edge by offering in-depth insights that resonate with the audience.

Streamlined Collaboration

AI tools for writing streamline collaboration on case study solutions. They enable teams to seamlessly work together, share insights, and contribute to the content creation process. With features like real-time editing, version control, and collaborative feedback, these tools foster a cohesive approach to developing impactful case studies.

Additionally, online writing tools provide a centralized platform for team communication and content creation, enhancing productivity and ensuring that all stakeholders are aligned in delivering compelling case study solutions.

Maximizing Case Study Creation with AI Writing Tools

Leverage data-driven content.

When utilizing the best AI tools for writing, leverage data-driven content to enhance the credibility and relevance of case studies. Incorporate statistical insights, industry trends, and research findings to provide a comprehensive perspective that resonates with the audience.

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To maximize the effectiveness of case study solutions, optimize content for audience engagement using AI writing tools. Utilize features that analyze audience preferences, language patterns, and engagement metrics to tailor the content for maximum impact.

By understanding and adapting to the audience's preferences, writers can create case studies that resonate with readers, driving higher engagement and resonance with the target audience.

Iterative Refinement Process

Implement an iterative refinement process when utilizing AI writing tools for case study creation. Continuously review and refine the content based on performance metrics, audience feedback, and emerging trends to ensure that the case studies remain relevant and impactful.

By embracing an iterative approach, writers can harness the capabilities of AI tools for writing to refine and enhance case study solutions, keeping them aligned with evolving audience expectations and industry dynamics.

Enhance Collaboration and Feedback

Utilize the collaborative features of AI writing tools to enhance team collaboration and feedback during case study creation. Leverage real-time editing, version control, and collaborative feedback mechanisms to ensure that all stakeholders contribute to the development of impactful case studies.

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Explore the content optimization capabilities of AI writing tools to enhance the relevance and resonance of case study solutions. Utilize features that analyze search intent, keyword relevance, and content structure to optimize the case studies for maximum visibility and impact.

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Unlocking Creativity with AI Case Study Solver

Discover how the AI case study solver tool can revolutionize your content creation process by providing real-time solutions to complex queries and enabling you to craft compelling case studies effortlessly.

Craft a case study solution that incorporates statistical insights, industry trends, and data-driven evidence to showcase the impact of innovative technologies in healthcare.

In the evolving landscape of healthcare, innovative technologies are reshaping the industry, driving transformative outcomes and enhancing patient care. Leveraging the AI case study solver tool, I analyzed extensive datasets to extract key statistical insights, industry trends, and data-driven evidence that underscore the profound impact of these technologies.

By integrating compelling statistics and data-driven evidence, the resulting case study solution effectively demonstrates how these innovative technologies are revolutionizing healthcare practices, optimizing outcomes, and elevating patient experiences. The comprehensive data analysis, facilitated by the AI tool, enables the creation of a persuasive case study that resonates with healthcare professionals and stakeholders, showcasing the tangible benefits of embracing technological advancements in the industry.

Furthermore, the AI case study solver tool empowered the seamless integration of complex statistical findings and industry trends, allowing for the synthesis of a cohesive narrative that highlights the transformative potential of innovative healthcare technologies. The ability to delve into extensive data sets and extract pertinent insights has elevated the depth and credibility of the case study, positioning it as a compelling resource for industry professionals and decision-makers seeking to understand the impact of innovative technologies in healthcare.

The utilization of the AI case study solver tool not only streamlined the data analysis process but also enriched the case study with robust evidence and compelling statistical insights. By leveraging this advanced writing tool, the resulting case study solution stands as a testament to the power of data-driven narratives in illustrating the profound influence of innovative technologies on the healthcare landscape, providing valuable insights and actionable takeaways for industry stakeholders.

Create a case study solution that resonates with a diverse audience, effectively communicating the transformative impact of sustainable practices in the fashion industry.

In the dynamic realm of fashion, sustainable practices are catalyzing a transformative shift, redefining industry standards and fostering a more environmentally conscious approach. Leveraging the AI case study solver tool, I crafted a compelling case study solution that intricately weaves together the compelling narrative of sustainable practices and their transformative impact on the fashion industry, resonating with diverse audience segments.

By harnessing the AI tool's capabilities, the resulting case study solution adeptly captures the essence of sustainable practices and their far-reaching influence on the fashion landscape. The tool's nuanced understanding of audience preferences and engagement metrics enabled the creation of a narrative that resonates with diverse stakeholders, effectively communicating the tangible benefits and transformative potential of embracing sustainability in fashion.

Furthermore, the AI case study solver tool facilitated the seamless integration of audience-centric insights and engagement-driven content strategies, ensuring that the resulting case study solution resonates with diverse audience segments, from fashion enthusiasts to industry professionals. The tool's ability to optimize content for audience engagement has elevated the case study's impact, positioning it as a compelling resource that effectively communicates the transformative power of sustainable practices in the fashion industry.

By leveraging the AI case study solver tool, the resulting case study solution stands as a testament to the tool's capacity to craft narratives that resonate with diverse audience segments, effectively communicating the transformative impact of sustainable practices in the fashion industry and inspiring meaningful action among stakeholders.

Frequently Asked Questions

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Strise.ai: AI-powered text analytics built by Norwegian startup

About Strise.ai

Seeded by a grant from the Norwegian Research Council in 2016, Strise.ai developed a machine learning solution that can extract meaning from millions of daily news articles, social media posts, reports, and so on, across languages. The small Trondheim-based firm uses its innovative approach to natural language processing to provide clients with actionable business insights in batch or on demand from a single API.

Tell us your challenge. We're here to help.

Strise.ai, a media intelligence and analytics startup in trondheim, norway, created a language-agnostic data processing platform that uses machine learning and knowledge graph engineering to make sense of tens of millions of daily news articles, blogs, social media posts, reports, and other unstructured data documents in multiple languages., google cloud results.

• Gains significant savings on computing power via reliable auto-scaling up and down
• Boosts throughput 3x in less than five minutes, 6x in less than 15 minutes
• Experiments at any scale without building or maintaining one-off build/deploy environments
• Minimizes native build environment — a single node cluster with 2 CPUs — to run Jenkins on off-peak hours

Dramatically minimizes DevOps resources

The demand for AI-assisted analytics is rising sharply. As retailers, publishers, financial services companies, and others look to capitalize on new business opportunities, text analytics can cue timely business insights and reveal new strategies for reaching and serving end-users. Scaling quickly to sort and reliably analyze vast amounts of unstructured data in content worldwide is key.

Strise.ai relies on Google Cloud to deploy, operate, and deliver results in real time. The company, which has brought together a small development team to focus on creating powerful AI solutions, is well positioned to ride a market projected to double in size to nearly $8 billion by 2022.

Marit Rodevand, Patrick Skjennum, and Sigve Søråsen, the company's co-founders, all met through the Norwegian University of Science and Technology (NTNU). Keen on AI, the Department of Computer Science championed Patrick's Master's project. His work laid out a promising path for extracting meaning from unstructured data in multiple languages by using a knowledge graph to abstract, chunk, and categorize content.

"Building DevOps and production environments from the ground up for our startup was not an option. Google Cloud is a vital enabling resource."

Marit quickly saw its commercial potential for the financial and media industries. With a grant from the Norwegian Research Council and a prominent early adopter, Norway's leading financial news outlet (Dagens Næringsliv — dn.no), Strise.ai was launched.

Strise.ai traverses the huge labyrinth of unstructured data using state-of-the-art natural language processing (NLP) and machine learning (ML), making insights available through intuitive and powerful customer APIs.

The Strise.ai GraphQL APIs, secured by Auth0, are designed to support extremely nuanced queries. According to Marit, one such query may result in fetching "the most relevant buying signals from pharmaceutical companies headquartered in New York City whose earnings exceeded $10M in revenue." The ability to apply filters and constraints with such human-level abstraction make querying Strise.ai's system simple, yet powerful, often reducing the number of returned documents by several orders of magnitude in comparison with traditional media monitoring systems. This can create value in numerous areas of application, but the first product Strise.ai is bringing to the market is a tool to help sales organizations prioritize and understand their B2B customers and prospects.

Customers, for example, can specify a Story object that traverses semantically chunked articles to return only the most relevant story points across blogs and articles while eliminating duplicates.

Since Strise.ai technology is language agnostic, the company is well positioned in linguistically fragmented markets like those in Europe and Asia. The solutions currently support Norwegian, Swedish, Danish, English, French, Spanish, and German, with support for more languages underway.

Scaling the opportunity

From the start, the challenge was the scale required to achieve Strise.ai's potential. In order to provide deep insights to its clients, Strise.ai needed to be able to analyze millions of documents from the start.

"Building DevOps and production environments from the ground up for our startup was not an option," says Marit, noting Strise.ai's ingestion of tens of millions of published news articles, blogs, and reports daily. "Google Cloud is a vital enabling resource."

The company's initial proof-of-concept ran on Ubuntu VMs in Compute Engine . It consisted of a single pipeline in Apache Spark Streaming which continuously read and parsed textual content from RSS. A simple NLP pipeline, including a knowledge graph stored as an in-memory Redis, made sense of the content.

Making it extensible was the next milestone. "We started splitting the system into separate modules for content ingestion, content analysis, and knowledge enrichment — all of which were connected through messaging queues using Pub/Sub ," explains Patrick. The company's knowledge base was moved from Redis and into Elasticsearch, and the Spark clusters became orchestrated by Yarn and Zookeeper. Strise.ai swapped its early NLP pipeline for a more sophisticated and modular microservice setup.

"Our initial reason for choosing Google Cloud over AWS and Azure was the superior support for Apache Spark through Dataproc. Managing and running Spark jobs went from being a constant struggle with high costs of operations to becoming automatically managed and scalable."

Though the system was now extensible, responsiveness, maintainability, and deployment quickly emerged as stumbling blocks. The solution requires running thousands of concurrent ML models to consume and sort ingested data. Prior to adopting Google Cloud, Strise.ai had to manually deploy 10 services to different machines, each with different requirements, dependencies, and configurations.

This created two related problems for developers: the complexity of managing physical resources and the lack of convenient scaling. "Not only was it expensive, but also hard to maintain," says Marit. "Developers spent a considerable portion of their time manually operating, monitoring, and maintaining the system."

Automating the AI pipe

An obvious approach for Strise.ai was outsourcing as much infrastructure and administration as possible to the cloud. "After having burned through the free credits of the Google Cloud trial program, our minds were made up," says Marit.

Explains Patrick, "Our initial reason for choosing Google Cloud over AWS and Azure was the superior support for Apache Spark through Dataproc . Managing and running Spark jobs went from being a constant struggle with high costs of operations to becoming automatically managed and scalable."

Google Kubernetes Engine (GKE) and Dataproc were crucial to Strise.ai's successful revamping. Dataproc, which helps launch and tear down clusters supported by Compute Engine VMs on the fly to meet processing loads, enables the team to stay focused on analytics, not IT. The Google Kubernetes Engine container environment also further accelerates system deployment as well as greatly streamlines Strise.ai IT administration.

"We re-architected the system so we could have everything automatically deployed, managed, and monitored in Google Cloud," explains Patrick. The solution's content processors, ML services, and APIs are conveniently contained in stateless microservices running in Kubernetes . Strise.ai runs its data ingestion (millions of articles a day) through Cloud Storage and the messaging queues use Pub/Sub.

When analyzing all of those blogs, news articles, reports, and social media posts and constantly adding new sources, the system's ability to scale is paramount. From the Strise.ai redesign around GKE, a horizontally scalable system emerged. By using autoscale features enabled by Google Cloud, Strise.ai could triple or even quadruple its processing power within minutes. And Google Cloud is proving agile all around for the startup, saving money by down-scaling cloud resources when traffic drops.

An example is Strise.ai's self-hosted NLP service based on state-of-the-art natural language frameworks. While the frameworks are optimized for batch processing multiple documents, GKE helps Strise.ai serve them through a low-latency single-document API. When experiencing an increase in load, the solution scales the number of workers, deployed as pods on Kubernetes, until demand is met.

All of Strise.ai's code is hosted on GitHub and automatically deployed in the Kubernetes environment by Jenkins using Helm, a package manager for Kubernetes. Because Google Cloud has also enabled the team to develop a system that can be managed easily via the internet, they have more flexibility to deal with any issue at any time. "Deploying a new service from concept to production can usually be done within an hour," says Patrick. "It's so easy that team members have sometimes deployed a critical bug fix from public transportation or the occasional bar."

In addition to the Google Cloud benefits already noted by Strise.ai's developers, the company found other advantages to operating on Google Cloud and a containerized platform: greater freedom to experiment. It became easier to explore alternate setups and run environments with new features because these could be reverted quickly and reliably. "Our intellectual property is continuously expanding largely because Google Cloud makes experimenting easy," says Marit.

"You could say that Google Cloud has become our operations department, and that's been like adding two full-time developers to our team."

More focus on IP

One of the biggest takeaways going from barebones computers to a more supported and comprehensive environment built on Google Cloud is that developers can spend practically all their time writing code. And that gives the team more time to create solutions and get them to market sooner.

"The ability to spin up dozens of machines with the click of a button has made it possible to test and deploy ML and big data analytical services in a matter of minutes, which previously could have taken hours or days," explains Marit.

Developing and delivering services has become so manageable that Strise.ai does not even have an operations department. "With Google Kubernetes Engine and powerful templating tools in Helm, DevOps has been reduced to modifying YAML files, pushing them to GitHub, and watching it build and deploy on the Jenkins monitor at our office," says Patrick. "You could say that Google Cloud has become our operations department, and that's been like adding two full-time developers to our team."

Contributors to this story

Marit Rødevand : Strise.ai CEO and Co-founder. Second-time founder. Co-founded Rendra, a construction SaaS company acquired by JDMT. Marit founded Strise.ai while working as entrepreneur-in-residence at the Norwegian University of Science and Technology (NTNU), where she earned her MSc in Engineering Cybernetics and Entrepreneurship.

Patrick Skjennum : Strise.ai CTO and Co-founder. Patrick earned his MSc in Computer Science from NTNU, with a focus on multilingual news article classification using embedded words.

• The Google Cloud Startup Program enables any startup to get set up quickly and easily through mentorship, training, and free credits.

Case Study Generator

A “case study” is a research methodology that is widely used in a range of fields such as social sciences, education, business, and health. It involves an in-depth investigation of a single individual, group, or event to explore the causes of underlying principles. The idea behind a case study is that the more you understand about an object, whether it’s a person or a phenomenon, the more we can understand about it in a broad sense.

A case study is generally a detailed study of the subject, where the subject can be a person, group, organization, event, issue, or any other entity. The research data is gathered from various sources like documents, observational records, interviews, psychological testing, or archival records.

A good case study is characterized by:

• A clear and concise title The title should clearly identify the focus or central issue of the case study.
• A thorough literature review This step helps to ground the study and establish a framework for interpretation.
• A well-defined subject or issue It should be clear what or whom the case study is about.
• Use of multiple sources of data This helps to provide a more comprehensive insight into the subject matter.
• Detailed description The case study should provide a rich narrative of the issue or case under study, providing the reader with a real sense of the subject’s experience.
• Thoughtful analysis and interpretation The researcher should be able to draw conclusions and make inferences from the data collected.
• Well-structured and clear writing The case study should be well-organized, easy to follow, and free of technical jargon.

Remember, the aim of a good case study is not just to describe, but to illuminate a situation, and reveal what would otherwise not be known. The most valuable case studies provide the reader with new insights or knowledge about the subject.

When thinking of artificial intelligence (AI) use cases, the question might be asked:  What won’t AI be able to do? The easy answer is mostly manual labor, although the day might come when much of what is now manual labor will be accomplished by robotic devices controlled by AI. But right now, pure AI can be programmed for many tasks that require thought and intelligence , as long as that intelligence can be gathered digitally and used to train an AI system. AI is not yet loading the dishwasher after supper—but can help create a legal brief, a new product design, or a letter to grandma.

We’re all amazed by what AI can do. But the question for those of us in business is what are the best business uses? Assembling a version of the Mona Lisa in the style of Vincent van Gough is fun, but how often will that boost the bottom line? Here are 27 highly productive ways that AI use cases can help businesses improve their bottom line.

Customer-facing AI use cases

Deliver superior customer service.

Customer interactions can now be assisted in real time with conversational AI. Voice-based queries use natural language processing (NLP) and sentiment analysis for speech  recognition so their conversations can begin immediately. Using machine learning algorithms, AI can understand what customers are saying as well as their tone—and can direct them to customer service agents when needed. With text to speech and NLP, AI can respond immediately to texted queries and instructions. There’s no need to make customers wait for the answers to frequently asked questions (FAQs) or to take the next step to purchase. And digital customer service agents can boost customer satisfaction by offering advice and guidance to customer service agents.

Personalize customer experiences

The use of AI is effective for creating personalized experiences at scale through chatbots, digital assistants and customer interfaces , delivering tailored experiences and targeted advertisements to customers and end-users. For example, Amazon reminds customers to reorder their most often-purchased products, and shows them related products or suggestions. McDonald’s is building AI solutions for customer care with IBM Watson AI technology and NLP to accelerate the development of its automated order taking (AOT) technology. Not only will this help scale the AOT tech across markets, but it will also help tackle integrations including additional languages, dialects and menu variations. Over at Spotify, they’ll suggest a new artist for the customer’s listening pleasure. YouTube will deliver a curated feed of content suited to customer interests.

Promote cross- and up-selling

Recommendation engines use consumer behavior data and AI algorithms to help discover data trends to be used in the development of more effective up-selling and cross-selling strategies, resulting in more useful add-on recommendations for customers during checkout for online retailers. Other uses include Netflix offering viewing recommendations powered by models that process data sets collected from viewing history; LinkedIn uses ML to filter items in a newsfeed, making employment recommendations and suggestions on who to connect with; and Spotify uses ML models to generate its song recommendations.

Smarten up smartphones

Facial recognition turns on smartphones and voice assistants, powered by machine learning, while Apple’s Siri, Amazon’s Alexa, Google Assistant and Microsoft’s Copilot use NLP to recognize what we say and then respond appropriately. Companies also take advantage of ML in smartphone cameras to analyze and enhance photos using image classifiers, detect objects (or faces) in the images, and even use artificial neural networks to enhance or expand a photo by predicting what lies beyond its borders.

Introduce personal assistants

Virtual assistants or voice assistants, such as Amazon’s Alexa and Apple’s Siri, are powered by AI. When someone asks a question via speech or text, ML searches for the answer or recalls similar questions the person has asked before. The same technology can power messaging bots, such as those used by Facebook Messenger and Slack—while Google Assistant, Cortana and IBM watsonx Assistant combine NLP to understand questions and requests , take appropriate actions and compose responses.

Humanize Human Resources

AI can attract, develop and retain a skills-first workforce . A flood of applications can be screened, sorted and passed to HR team members with precision. Manual promotion assessment tasks can be automated, making it easier to gain important HR insights with a clearer view of, for example, employees up for promotion and assessing whether they’ve met key benchmarks . Routine questions from staff can be quickly answered using AI.

Creative AI use cases

Create with generative ai.

Generative AI tools such as ChatGPT, Bard and DeepAI rely on limited memory AI capabilities to predict the next word, phrase or visual element within the content it’s generating. Generative AI can produce high-quality text, images and other content based on the data used for training.

IBM Research is working to help its customers use generative models to write high-quality  software code  faster, discover  new molecules , and train trustworthy conversational chatbots  grounded on enterprise data. The IBM team is even using generative AI to create  synthetic data  to build more robust and trustworthy AI models and to stand in for real-world data protected by privacy and copyright laws.

Deliver new insights

Expert systems can be trained on a corpus—metadata used to train a machine learning model—to emulate the human decision-making process and apply this expertise to solve complex problems. These systems can evaluate vast amounts of data to uncover trends and patterns, and to make decisions. They can also help businesses predict future events and understand why past events occurred.

Clarify computer vision

AI-powered computer vision enables image segmentation , which has a wide variety of  use cases, including aiding diagnosis in medical imaging, automating locomotion for robotics and self-driving cars, identifying objects of interest in satellite images and photo tagging in social media. Running on neural networks , computer vision enables systems to extract meaningful information from digital images, videos and other visual inputs.

Technical AI use cases

Speed operations with aiops.

There are many benefits to using  artificial intelligence for IT operations (AIOps) . By infusing AI into IT operations , companies can harness the considerable power of NLP, big data, and ML models to automate and streamline operational workflows, and monitor event correlation and causality determination.

AIOps is one of the fastest ways to boost ROI from digital transformation investments. Process automation is often centered on efforts to optimize spend, achieve greater operational efficiency and incorporate new and innovative technologies, which often translate into a better customer experience. More benefits from AI include building a more sustainable IT system and improving the continuous integration/continuous (CI/CD) delivery pipelines.

Automate coding and app modernization

Leading companies are now using generative AI for application modernization and enterprise IT operations, including automating coding, deploying and scaling. For coding, developers can input a coding command as a straightforward English sentence through a natural-language interface and get automatically generated code . Using generative AI with code generation capabilities can also enable hybrid cloud developers of all experience levels to migrate and modernize legacy application code at scale, to new target platforms with code consistency, fewer errors, and speed.

Boost application performance

Ensuring that apps perform consistently and constantly—without overprovisioning and overspending—is a critical AI operations (AIOps) use case. Automation is key to optimizing cloud costs, and IT teams, no matter how skilled they are, don’t always have the capacity to continuously determine the exact compute, storage and database configurations needed to deliver performance at the lowest cost. AI software can identify when and how resources are used, and match actual demand in real time.

Strengthen end-to-end system resilience

To help ensure uninterrupted service availability, leading organizations use real-time root cause analysis capabilities powered by AI and intelligent automation. AIOps can enable ITOps teams to swiftly identify the underlying causes of incidents and take immediate action to reduce both mean time between failures (MTBF) and mean time to repair (MTTR) incidents.

AIOps platform solutions also consolidate data from multiple sources and correlate events into incidents, granting clear visibility into the entire IT environment through dynamic infrastructure visualizations, integrated AI capabilities and suggested remediation actions.

Using predictive IT management, IT teams can use AI to automate IT and network operations to resolve incidents swiftly and efficiently—and proactively prevent issues before they occur, enhance user experiences and cut the cost of and administrative tasks. To help eliminate tool sprawl, an enterprise-grade AIOps platform can provide a holistic view of IT operations on a central pane of glass for monitoring and management.

Lock in cybersecurity

There are many ways AI can use ML to deliver improved cybersecurity, including: facial recognition for authentication, fraud detection, antivirus programs to detect and block malware, reinforcement learning to train models that identify and respond to cyberattacks and detect intrusions and classification algorithms that label events as anomalies or phishing attacks.

Gear up robotics

AI is not just about asking for a haiku written by a cat. Robots handle and move physical objects. In industrial settings, narrow AI can perform routine, repetitive tasks involving materials handling, assembly and quality inspections. AI can assist surgeons by monitoring vitals and detecting potential issues during procedures. Agricultural machines can engage in autonomous pruning, moving, thinning, seeding and spraying. Smart home devices such as the iRobot Roomba can navigate a home’s interior using computer vision and use data stored in memory to understand its progress. And if AI can guide a Roomba, it can also direct self-driving cars on the highway and robots moving merchandise in a distribution center or on patrol for security and safety protocols.

Clean up with predictive maintenance

AI can be used for predictive maintenance by analyzing data directly from machinery to identify problems and flag required maintenance. AI has also been used to improve mechanical efficiency and reduce carbon emissions in engines. Maintenance schedules can use AI-powered predictive analytics to create greater efficiencies.

See what’s ahead

AI can assist with forecasting . For example, a supply-chain function can use algorithms to predict future needs and the time products need to be shipped for timely arrival. This can help create new efficiencies, reduce overstocks and help make up for reordering oversights.

Industry AI use cases

AI can power tasks and tools for almost any industry to boost efficiency and productivity. AI can deliver intelligent automation to streamline business processes that were manual tasks or run on legacy systems—which can be resource-intensive, costly and prone to human error. Here are some of the industries that are benefiting now from the added power of AI.

With applications of AI, automotive manufacturers are able to more effectively predict and adjust production to respond to changes in supply and demand. They can streamline workflows to increase efficiency and reduce time-consuming tasks and the risk of error in production, support, procurement and other areas. Robots help reduce the need for manual labor and improve defect discovery, providing higher quality vehicles to customers at a lower cost to the business.

In education and training , AI can tailor educational materials to each individual student’s needs. Teachers and trainers can use AI analytics to see where students might need extra help and attention. For students tempted to plagiarize their papers or homework, AI can help spot the copied content. AI-driven language translation tools and real-time transcription services can help non-native speakers understand the lessons.

Companies in the energy sector can increase their cost competitiveness by harnessing AI and data analytics for demand forecasting, energy conservation, optimization of renewables and smart grid management. By introducing AI into energy generation, transmission and distribution processes, AI can also improve customer support, freeing up resources for innovation. And for customers using supplier-based AI, they can better understand their energy consumption and take steps to reduce their power draw during peak demand periods.

Financial services

AI-powered FinOps (Finance + DevOps) helps financial institutions operationalize data-driven cloud spend decisions to safely balance cost and performance in order to minimize alert fatigue and wasted budget. AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions. Banks and other lenders can use ML classification algorithms and predictive models to suggest loan decisions.

Many stock market transactions use ML with decades of stock market data to forecast trends and ultimately suggest whether and when to buy or sell. ML can also conduct algorithmic trading without human intervention. ML algorithms can predict patterns, improve accuracy, lower costs and reduce the risk of human error.

The  healthcare industry is using intelligent automation with NLP to provide a consistent approach to data analysis, diagnosis and treatment. The use of chatbots in remote healthcare appointments requires less human intervention and often a shorter time to diagnosis. On-site, ML can be used in radiology imaging, with AI-enabled computer vision often used to analyze mammograms and for early lung cancer screening. ML can also be trained to create treatment plans, classify tumors, find bone fractures and detect neurological disorders.

In genetic research, gene modification and genome sequencing, ML is used to identify how genes impact health. ML can identify genetic markers and genes that will or will not respond to a specific treatment or drug and may cause significant side effects in certain people.

With AI, insurance providers can virtually eliminate the need for manual rate calculations or payments and can simplify processing claims and appraisals. Intelligent automation also helps insurance companies adhere to compliance regulations more easily by ensuring that requirements are met. This way, they are also able to calculate the risk of an individual or entity and calculate the appropriate insurance rate.

Manufacturing

Advanced AI with analytics can help manufacturers create predictive insights on market trends. Generative AI can speed and optimize product design by helping companies create multiple design options. AI can also assist with suggestions for boosting production efficiency. Using historical data of production, generative AI can predict or locate equipment failures in real time—and then suggest equipment adjustments, repair options or needed spare parts.

Pharmaceuticals

For the life sciences industry, drug discovery and production require an immense amount of data collection, collation, processing and analysis. A manual approach to development and testing could lead to calculation errors and require a huge volume of resources. By contrast, the production of Covid-19 vaccines in record time is an example of how intelligent automation enables processes that improve production speed and quality.

AI is becoming the secret weapon for retailers to better understand and cater to increasing consumer demands. With highly personalized online shopping, direct-to-consumer models and delivery services competing with retail, generative AI can help retailers and e-commerce firms improve customer care, plan marketing campaigns, and transform the capabilities of their talent and their applications. AI can even help optimize inventory management.

Generative AI excels at handling diverse data sources such as emails, images, videos, audio files and social media content. This unstructured data forms the backbone for creating models and the ongoing training of generative AI, so it can remain useful over time. Leveraging this unstructured data can extend benefits to various aspects of retail operations, including enhancing customer service through chatbots and facilitating more effective email routing. In practice, this could mean guiding users to the appropriate resources, whether that’s connecting them with the right agent or directing them to user guides and FAQs.

Transportation

AI informs many transportation systems these days. For instance, Google Maps uses ML algorithms to check current traffic conditions, determine the fastest route, suggest places to “explore nearby” and estimate arrival times.

Ride-sharing applications such as Uber and Lyft use ML to match riders and drivers, set prices, examine traffic and, like Google Maps, analyze real-time traffic conditions to optimize driving routes and estimate arrival times.

Computer vision guides self-driving cars. An unsupervised ML algorithm enables self-driving cars to gather data from cameras and sensors to understand what’s happening around them, and enables real-time decision-making.

Delivering the promise of AI

Much of what AI can do seems miraculous, but much of what gets reported in the general media is frivolous fun or just plain scary. What is now available to business is a remarkably powerful tool that can help many industries and functions make great strides. The companies that do not explore and adopt the most beneficial AI use cases will soon be at a severe competitive disadvantage. Keeping an eye out for the most useful AI tools, such as IBM ® watsonx.ai™, and mastering them now will pay great dividends.

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IBM Newsletters

Explore how Microsoft's partnership with Khan Academy is enhancing the future of education with AI innovation and tools for teachers >

• AI in education
• Published Mar 6, 2024

Reimagine Education 2024: Prepare for the future with new AI and security offers

• Content Type
• Learning accelerators
• Education decision makers
• Microsoft Copilot

Editor’s note: This blog was originally published on March 6, 2024, and was updated on March 28, 2024. We previously shared that Copilot for Microsoft 365 would be available for higher education institutions to purchase as an add-on for their students aged 18+ on April 1, 2024. Availability is now planned for May 1, 2024.

At Reimagine Education , we announced new ways that Microsoft’s AI tools can be used to bring new opportunities to life, build secure foundations, and prepare students for the future. We’re bringing Microsoft Copilot to more education audiences, launching free AI features designed to save time for educators, and publishing an AI Toolkit. Learning Accelerators’ availability is expanding to popular learning management systems and Speaker and Math Progress are entering private previews. We also shared a new offer: Microsoft Defender for Endpoint tailored towards protecting student devices, at a discounted price. 

If you missed the event today, you can watch it on-demand and continue reading to learn more. 

Microsoft Copilot: your AI assistant for education  

Microsoft’s advancements in AI are grounded in our mission to empower every person to achieve more and are guided by Microsoft’s Responsible AI principles that are built upon decades of research.

Educators around the world are already using Copilot to draft content, brainstorm new ideas, and free up their time to focus on what matters most. And we recently spoke to educators from O’Dea High School and Indiana University to hear first-hand how they now have a secure AI “scaffolding” to support them in and outside of the classroom. During the Reimagine Education event, we shared Copilot expansions to empower education institutions to harness Microsoft AI technologies. 

Microsoft Copilot with commercial data protection is built into all Microsoft 365 Education offers, including our zero-cost license. It’s already available to all faculty and higher education students ages 18 and above, and we’re starting a private preview program for younger learners this spring. 

Starting April 1, 2024, Copilot for Microsoft 365 will be available for higher education institutions to purchase as an add-on for their students aged 18+. To be eligible, students must be assigned Microsoft 365 or Office 365 A3/A5 licenses. Integration across Microsoft 365 applications provides seamless performance, so you can: 

• Stay on top of all your chats, remote classes, meetings, and calls with Microsoft Teams.
• Create, comprehend, and elevate your documents in Microsoft Word. 
• Keep up with your inbox and manage follow-ups in Microsoft Outlook.
• Turn your inspiration into stunning presentations in Microsoft PowerPoint. 
• Analyze, comprehend, and visualize data with ease in Microsoft Excel. 

Additionally, commercial academic offers of Copilot come with a Customer Copyright Commitment. This means, education customers can be confident in using our services without the concerns of copyright claims. 

Personalize learning at scale

We have exciting updates to our Learning Accelerators as well in Teams for Education to help personalize learning at scale: 

• New features in Reading Progress and Microsoft Teams for Education are coming to all educators starting later this month at no additional cost. They leverage AI to draft content like rubrics, assignment instructions, personalized reading passages, and learning objectives, all while keeping the educator in control.  
• Reading Coach now comes with enhanced AI features so students can create their stories and pick their own path as the story progresses: increasing student agency and motivation. It’s going to be available on the web, as a dedicated Windows app, and as an LMS integration. Customers interested in signing up for the preview of the LMS integrations for Reading Coaches and other Learning Tool integrations can go to aka.ms/LMSIntegrations  
• Microsoft’s teacher tool, Math Progress is now entering private preview, and Math Coach, our student tool, will follow soon. These tools leverage AI to help students identify where they’re struggling and provide real-time step-by-step coaching on mathematical problem solving. 

How AI Navigators are leveraging technology for impact    

During the event, I also had the pleasure of introducing our AI Navigators. They highlight how state departments, ministries of education, universities, and K-12 schools are leveraging Microsoft AI tools and solutions to better prepare students for their future. The stories of these navigators demonstrate how AI technology can create even more impact in the hands of great educators to make a real difference in student learning. 

Wichita Public Schools in Kansas serves 47,000 students and 5,600 educators and administrators. Microsoft Copilot gives their teachers what they want the most—time—allowing them to focus more on each student and bring a greater diversity of tailored learning experiences into the classroom.   

The University of South Florida is using Copilot for Microsoft 365 to accelerate faculty workflows and create their own solutions, such as their Help Desk Bot. Before, people had to review every help desk ticket and it would take a few hours before the IT team could respond. Now, response time is a matter of seconds. Faculty can also do more work in less time—querying and summarizing documents in seconds—leaving them more time to spend building new projects and student relationships. 

California State University San Marcos is using Microsoft Dynamics 365 Customer Insights and testing Copilot to overcome data collection hurdles and support each student individually. They’re now able to centralize communications with students, staff, faculty, and external partners and use data meaningfully in personalized interactions with students.   

We also shared remarkable partner stories from PowerSchool and Anthology who are leveraging the Azure OpenAI Service. The University of Leeds uses the AI Design Assistant from Anthology to empower instructors to quickly and easily build course structure, rubrics, and more. And Colorado Springs School District 11 utilizes PowerSchool AI capabilities to free up time spent creating materials to focus on student needs and engagement.  

Engage in deeper learning experiences 

We also shared new resources to deepen engagement and increase AI literacy for leaders and educators: 

• The Microsoft Education AI Toolkit is a free resource that education leaders can use to develop AI plans for their institutions. It will help to lower the barrier of entry with examples, case studies, and getting started materials to help you evaluate and implement AI solutions.  
• Explore the AI in Education Report for the latest insights from Microsoft, partner organizations, and academia on new opportunities and challenges.  
• The Minecraft AI Prompt Lab is designed to empower educators with the skills and knowledge needed to creatively use Minecraft Education as a dynamic teaching tool, leveraging the strengths of Microsoft Copilot to enhance their teaching abilities. 

Paige Johnson discusses new Microsoft security offerings with Corey Lee, Security Chief and Technology Officer at Microsoft.

How Microsoft tools keep students and information safe

In terms of security, we are introducing the following:  

• A new Microsoft Defender for endpoint offering designed to protect student devices will be available soon to any Microsoft 365 A5 customer at a discounted price.   
• Microsoft Copilot for Security , the first and only generative AI solution that helps security and IT professionals amplify their skillsets, collaborate more, see more, and respond faster. Tune into Microsoft Secure event on March 13, 2024, to get the latest updates on Microsoft Copilot for Security.  
• Free security trainings so that school leaders, educators, students, and even families can learn how to make smart decisions when they are in an educational environment.   

These new security offerings are already being implemented in K-12 and higher education. For example, Microsoft Defender helped Fulton Country Schools to instill confidence in district leadership, staff, and students after a ransomware scare in December 2021. Similarly, Newington College has students across four campuses and protecting their data is a big issue. Microsoft 365 Education A5 gives them a holistic view of their security environment. If an account were to be compromised, tools such as Microsoft Defender and Microsoft Sentinel would help keep data, servers, and workstations safe, managed and patched. 

Paige Johnson, Vice President, Education at Microsoft with Chris Reykdal, Superintendent of Public Instruction in Washington State.

Microsoft and the changing world of work  

The latest findings from an IDC InfoBrief , sponsored by Microsoft, as well as Microsoft’s own New Future of Work report confirm that the need for an AI-ready workforce has already arrived. Today’s graduates are expected to be able to use Microsoft Copilot and other AI technologies as they progress from classroom to career.  

I had a fascinating conversation with the Superintendent of Public Instruction in Washington State, Chris Reykdal, about responsible AI implementation. After recognizing the need to prepare Washington State students for the world of work with AI, a group of education leaders was assembled to reimagine several pieces of their education system, including grade-level learning expectations about AI, teacher professional development, institutional policies and practices, and curated resources for AI adoption.  

Watch Reimagine Education on-demand to hear the latest and catch up on the top announcements in this quick one-minute recap! 

Why reimagine? 

There has never been a better time to collectively reimagine education. I hope you are excited about and ready to leverage the opportunities AI can bring to education. We are inspired by the passion, persistence, and ingenuity that you demonstrate every day. Thank you for all that you do to help prepare the next generation of leaders and innovators! 

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NICE's CEO says AI is 'the ultimate alchemist' that can transform customer-experience solutions

• The software company NICE launched Enlighten AI to improve customer service and clients' efficiency.
• Enlighten AI decreases the workload for representatives and enhances customer interactions.
• This article is part of " CXO AI Playbook " — straight talk from business leaders on how they're testing and using AI.

For "CXO AI Playbook," Business Insider takes a look at mini case studies about AI adoption across industries, company sizes, and technology DNA. We've asked each of the featured companies to tell us about the problems they're trying to solve with AI, who's making these decisions internally, and their vision for using AI in the future.

NICE, which stands for Neptune Intelligence Computer Engineering, is a customer-experience-software company headquartered in Hoboken, New Jersey. The organization provides artificial-intelligence-powered products — such as virtual customer-service agents — to more than 25,000 organizations worldwide.

Situation analysis: What problem was the company trying to solve?

Many companies still use manual processes for customer-service and -experience tasks. Historically, those tasks have been too complex to monitor and, consequently, difficult to automate, NICE CEO Barak Eilam told Business Insider. He added that many organizations still invested more in labor than in technology .

Many companies strive to reduce friction in their customer-service operations, but they aren't always able to provide high-quality assistance or adequately understand what customers need.

"It's a pretty interesting challenge to see how AI can assist in those cases that were hard to solve so far," Eilam said. He views AI as the "ultimate alchemist" that can help bring together people, technology, and processes.

Key staff and partners

NICE projects its revenue will reach $2.7 billion this year. It has more than 8,000 employees, including scientists, engineers, and business and thought leaders. The organization invests about 15% of its revenue back into research and development, Eilam said.

Over the past few years, NICE has shifted more key staff to AI, he said: "It's not just generic AI. They're all really experts in customer experience."

NICE also leveraged its existing customers and the vast amounts of data it's accumulated over the past few decades to build software that helps clients boost their customer-experience initiatives, Eilam said.

AI in action

About 2 ½ years ago, NICE launched Enlighten AI for CX , a set of solutions to optimize self-service and customer-experience operations, improve engagement, and boost customer satisfaction.

Eilam said Enlighten AI "dramatically improves" productivity and efficiency for customer-experience teams by automating many labor-intensive tasks , such as note-taking during customer-service calls. Customer-experience professionals can also quickly access information to take data-driven actions to solve customer problems. Enlighten Autopilot, one of the products within NICE's Enlighten AI for CX repository, enhances self-service with an AI-powered virtual assistant.

With Enlighten Copilot, another product, agents can access real-time insights to offer quick, personalized client interactions instead of putting customers on hold to look up information to answer their questions, Eilam said.

Did it work, and how did leaders know?

"AI is now leading every conversation with our customers," Eilam said. Many existing customers are adding Enlighten AI to their portfolios, and NICE is attracting new customers with its AI capabilities.

For example, the waste-management corporation Republic Services was already using NICE products but added Enlighten AI for Customer Satisfaction to measure, improve, and assess customer sentiment. Its customer-support system was manual, and the company felt that key insights were being missed.

Using Enlighten AI, Republic Services reduced the manual work of its customer-experience agents. It decreased repeat calls by 30% and lowered the average time spent on calls, despite an increase in seasonal call volume. The company also reduced negative customer sentiment by 33%.

The tech company Open Network Exchange also uses Enlighten AI to improve its customer care. Previously, ONE used manual quality-assurance processes and chose random customer calls to evaluate. This hindered supervisors' ability to objectively and holistically assess agents' skills that influenced customer experiences — and therefore interfered with their ability to provide meaningful coaching.

The organization implemented Enlighten AI to monitor 100% of its customer interactions and gather insights about the behaviors influencing customer sentiment. Within 90 days, the company began improving how it coached agents, saving supervisors four to five hours a week.

What's next?

NICE continues to focus on AI innovation, Eilam said.

He told BI the company saw many opportunities to expand its AI offerings by partnering with organizations that still spend 90% of their budgets on labor and 10% on technology and continue to rely on manual processes.

"A lot of new, big things are coming," Eilam said, adding that NICE planned to announce new products at its upcoming Interactions Customer Conference 2024 in June.

"That allows us to have a very optimistic view of the future," he said.

We want to hear from you. If you are interested in sharing your company's AI journey, email [email protected] .

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The state of AI in 2023: Generative AI’s breakout year

You have reached a page with older survey data. please see our 2024 survey results here ..

The latest annual McKinsey Global Survey  on the current state of AI confirms the explosive growth of generative AI (gen AI) tools . Less than a year after many of these tools debuted, one-third of our survey respondents say their organizations are using gen AI regularly in at least one business function. Amid recent advances, AI has risen from a topic relegated to tech employees to a focus of company leaders: nearly one-quarter of surveyed C-suite executives say they are personally using gen AI tools for work, and more than one-quarter of respondents from companies using AI say gen AI is already on their boards’ agendas. What’s more, 40 percent of respondents say their organizations will increase their investment in AI overall because of advances in gen AI. The findings show that these are still early days for managing gen AI–related risks, with less than half of respondents saying their organizations are mitigating even the risk they consider most relevant: inaccuracy.

The organizations that have already embedded AI capabilities have been the first to explore gen AI’s potential, and those seeing the most value from more traditional AI capabilities—a group we call AI high performers—are already outpacing others in their adoption of gen AI tools. 1 We define AI high performers as organizations that, according to respondents, attribute at least 20 percent of their EBIT to AI adoption.

The expected business disruption from gen AI is significant, and respondents predict meaningful changes to their workforces. They anticipate workforce cuts in certain areas and large reskilling efforts to address shifting talent needs. Yet while the use of gen AI might spur the adoption of other AI tools, we see few meaningful increases in organizations’ adoption of these technologies. The percent of organizations adopting any AI tools has held steady since 2022, and adoption remains concentrated within a small number of business functions.

Table of Contents

• It’s early days still, but use of gen AI is already widespread
• Leading companies are already ahead with gen AI
• AI-related talent needs shift, and AI’s workforce effects are expected to be substantial
• With all eyes on gen AI, AI adoption and impact remain steady

About the research

1. it’s early days still, but use of gen ai is already widespread.

The findings from the survey—which was in the field in mid-April 2023—show that, despite gen AI’s nascent public availability, experimentation with the tools  is already relatively common, and respondents expect the new capabilities to transform their industries. Gen AI has captured interest across the business population: individuals across regions, industries, and seniority levels are using gen AI for work and outside of work. Seventy-nine percent of all respondents say they’ve had at least some exposure to gen AI, either for work or outside of work, and 22 percent say they are regularly using it in their own work. While reported use is quite similar across seniority levels, it is highest among respondents working in the technology sector and those in North America.

Organizations, too, are now commonly using gen AI. One-third of all respondents say their organizations are already regularly using generative AI in at least one function—meaning that 60 percent of organizations with reported AI adoption are using gen AI. What’s more, 40 percent of those reporting AI adoption at their organizations say their companies expect to invest more in AI overall thanks to generative AI, and 28 percent say generative AI use is already on their board’s agenda. The most commonly reported business functions using these newer tools are the same as those in which AI use is most common overall: marketing and sales, product and service development, and service operations, such as customer care and back-office support. This suggests that organizations are pursuing these new tools where the most value is. In our previous research , these three areas, along with software engineering, showed the potential to deliver about 75 percent of the total annual value from generative AI use cases.

In these early days, expectations for gen AI’s impact are high : three-quarters of all respondents expect gen AI to cause significant or disruptive change in the nature of their industry’s competition in the next three years. Survey respondents working in the technology and financial-services industries are the most likely to expect disruptive change from gen AI. Our previous research shows  that, while all industries are indeed likely to see some degree of disruption, the level of impact is likely to vary. 2 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. Industries relying most heavily on knowledge work are likely to see more disruption—and potentially reap more value. While our estimates suggest that tech companies, unsurprisingly, are poised to see the highest impact from gen AI—adding value equivalent to as much as 9 percent of global industry revenue—knowledge-based industries such as banking (up to 5 percent), pharmaceuticals and medical products (also up to 5 percent), and education (up to 4 percent) could experience significant effects as well. By contrast, manufacturing-based industries, such as aerospace, automotives, and advanced electronics, could experience less disruptive effects. This stands in contrast to the impact of previous technology waves that affected manufacturing the most and is due to gen AI’s strengths in language-based activities, as opposed to those requiring physical labor.

Responses show many organizations not yet addressing potential risks from gen AI

According to the survey, few companies seem fully prepared for the widespread use of gen AI—or the business risks these tools may bring. Just 21 percent of respondents reporting AI adoption say their organizations have established policies governing employees’ use of gen AI technologies in their work. And when we asked specifically about the risks of adopting gen AI, few respondents say their companies are mitigating the most commonly cited risk with gen AI: inaccuracy. Respondents cite inaccuracy more frequently than both cybersecurity and regulatory compliance, which were the most common risks from AI overall in previous surveys. Just 32 percent say they’re mitigating inaccuracy, a smaller percentage than the 38 percent who say they mitigate cybersecurity risks. Interestingly, this figure is significantly lower than the percentage of respondents who reported mitigating AI-related cybersecurity last year (51 percent). Overall, much as we’ve seen in previous years, most respondents say their organizations are not addressing AI-related risks.

2. Leading companies are already ahead with gen AI

The survey results show that AI high performers—that is, organizations where respondents say at least 20 percent of EBIT in 2022 was attributable to AI use—are going all in on artificial intelligence, both with gen AI and more traditional AI capabilities. These organizations that achieve significant value from AI are already using gen AI in more business functions than other organizations do, especially in product and service development and risk and supply chain management. When looking at all AI capabilities—including more traditional machine learning capabilities, robotic process automation, and chatbots—AI high performers also are much more likely than others to use AI in product and service development, for uses such as product-development-cycle optimization, adding new features to existing products, and creating new AI-based products. These organizations also are using AI more often than other organizations in risk modeling and for uses within HR such as performance management and organization design and workforce deployment optimization.

AI high performers are much more likely than others to use AI in product and service development.

Another difference from their peers: high performers’ gen AI efforts are less oriented toward cost reduction, which is a top priority at other organizations. Respondents from AI high performers are twice as likely as others to say their organizations’ top objective for gen AI is to create entirely new businesses or sources of revenue—and they’re most likely to cite the increase in the value of existing offerings through new AI-based features.

As we’ve seen in previous years , these high-performing organizations invest much more than others in AI: respondents from AI high performers are more than five times more likely than others to say they spend more than 20 percent of their digital budgets on AI. They also use AI capabilities more broadly throughout the organization. Respondents from high performers are much more likely than others to say that their organizations have adopted AI in four or more business functions and that they have embedded a higher number of AI capabilities. For example, respondents from high performers more often report embedding knowledge graphs in at least one product or business function process, in addition to gen AI and related natural-language capabilities.

While AI high performers are not immune to the challenges of capturing value from AI, the results suggest that the difficulties they face reflect their relative AI maturity, while others struggle with the more foundational, strategic elements of AI adoption. Respondents at AI high performers most often point to models and tools, such as monitoring model performance in production and retraining models as needed over time, as their top challenge. By comparison, other respondents cite strategy issues, such as setting a clearly defined AI vision that is linked with business value or finding sufficient resources.

The findings offer further evidence that even high performers haven’t mastered best practices regarding AI adoption, such as machine-learning-operations (MLOps) approaches, though they are much more likely than others to do so. For example, just 35 percent of respondents at AI high performers report that where possible, their organizations assemble existing components, rather than reinvent them, but that’s a much larger share than the 19 percent of respondents from other organizations who report that practice.

Many specialized MLOps technologies and practices  may be needed to adopt some of the more transformative uses cases that gen AI applications can deliver—and do so as safely as possible. Live-model operations is one such area, where monitoring systems and setting up instant alerts to enable rapid issue resolution can keep gen AI systems in check. High performers stand out in this respect but have room to grow: one-quarter of respondents from these organizations say their entire system is monitored and equipped with instant alerts, compared with just 12 percent of other respondents.

3. AI-related talent needs shift, and AI’s workforce effects are expected to be substantial

Our latest survey results show changes in the roles that organizations are filling to support their AI ambitions. In the past year, organizations using AI most often hired data engineers, machine learning engineers, and Al data scientists—all roles that respondents commonly reported hiring in the previous survey. But a much smaller share of respondents report hiring AI-related-software engineers—the most-hired role last year—than in the previous survey (28 percent in the latest survey, down from 39 percent). Roles in prompt engineering have recently emerged, as the need for that skill set rises alongside gen AI adoption, with 7 percent of respondents whose organizations have adopted AI reporting those hires in the past year.

The findings suggest that hiring for AI-related roles remains a challenge but has become somewhat easier over the past year, which could reflect the spate of layoffs at technology companies from late 2022 through the first half of 2023. Smaller shares of respondents than in the previous survey report difficulty hiring for roles such as AI data scientists, data engineers, and data-visualization specialists, though responses suggest that hiring machine learning engineers and AI product owners remains as much of a challenge as in the previous year.

Looking ahead to the next three years, respondents predict that the adoption of AI will reshape many roles in the workforce. Generally, they expect more employees to be reskilled than to be separated. Nearly four in ten respondents reporting AI adoption expect more than 20 percent of their companies’ workforces will be reskilled, whereas 8 percent of respondents say the size of their workforces will decrease by more than 20 percent.

Looking specifically at gen AI’s predicted impact, service operations is the only function in which most respondents expect to see a decrease in workforce size at their organizations. This finding generally aligns with what our recent research  suggests: while the emergence of gen AI increased our estimate of the percentage of worker activities that could be automated (60 to 70 percent, up from 50 percent), this doesn’t necessarily translate into the automation of an entire role.

AI high performers are expected to conduct much higher levels of reskilling than other companies are. Respondents at these organizations are over three times more likely than others to say their organizations will reskill more than 30 percent of their workforces over the next three years as a result of AI adoption.

4. With all eyes on gen AI, AI adoption and impact remain steady

While the use of gen AI tools is spreading rapidly, the survey data doesn’t show that these newer tools are propelling organizations’ overall AI adoption. The share of organizations that have adopted AI overall remains steady, at least for the moment, with 55 percent of respondents reporting that their organizations have adopted AI. Less than a third of respondents continue to say that their organizations have adopted AI in more than one business function, suggesting that AI use remains limited in scope. Product and service development and service operations continue to be the two business functions in which respondents most often report AI adoption, as was true in the previous four surveys. And overall, just 23 percent of respondents say at least 5 percent of their organizations’ EBIT last year was attributable to their use of AI—essentially flat with the previous survey—suggesting there is much more room to capture value.

Organizations continue to see returns in the business areas in which they are using AI, and they plan to increase investment in the years ahead. We see a majority of respondents reporting AI-related revenue increases within each business function using AI. And looking ahead, more than two-thirds expect their organizations to increase their AI investment over the next three years.

The online survey was in the field April 11 to 21, 2023, and garnered responses from 1,684 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 913 said their organizations had adopted AI in at least one function and were asked questions about their organizations’ AI use. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

The survey content and analysis were developed by Michael Chui , a partner at the McKinsey Global Institute and a partner in McKinsey’s Bay Area office, where Lareina Yee is a senior partner; Bryce Hall , an associate partner in the Washington, DC, office; and senior partners Alex Singla and Alexander Sukharevsky , global leaders of QuantumBlack, AI by McKinsey, based in the Chicago and London offices, respectively.

They wish to thank Shivani Gupta, Abhisek Jena, Begum Ortaoglu, Barr Seitz, and Li Zhang for their contributions to this work.

This article was edited by Heather Hanselman, an editor in the Atlanta office.

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IBM Study: Banking and Financial Markets CEOs are Betting on Generative AI to Stay Competitive, Yet Workforce and Culture Challenges Persist

ARMONK, N.Y. , June 5, 2024 / PRNewswire / -- New findings from the IBM (NYSE: IBM ) Institute for Business Value revealed that banking and financial markets (BFM) CEOs are facing workforce and culture and challenges as they act quickly to implement and scale generative AI  across their organizations.   

The findings are part of an annual global cross-industry study  that surveyed more than 3,000 CEOs from over 30 countries and 26 industries, which included 297 BFM CEOs representing retail, corporate, commercial and investment banks and financial markets.

The survey found that generative AI is perceived as the key to unlocking competitiveness. 57% of BFM CEOs surveyed stated that gaining a competitive advantage in the sector will depend on who has the most advanced generative AI.

The findings also revealed that CEOs are navigating complex issues around culture in the era of AI . 59% of surveyed BFM CEOs stated that cultural change within a business is more important than overcoming technical challenges when becoming a data-driven business, with 65% also believing success with AI will depend more on people's adoption than the technology itself.

Despite this, 60% of surveyed BFM CEOs say they are pushing for AI adoption more quickly than some employees might find comfortable. Yet 43% acknowledged that their employees do not fully understand how strategic decisions impact them.

Skills also proved to be an area of focus for the CEOs. While 60% of surveyed BFM CEOs say their teams have the skills and knowledge to incorporate generative AI, more than half (53%) of respondents say they are already struggling to fill key technology roles. In addition, 50% of these CEOs said they are hiring for roles that did not even exist this time last year due to generative AI, showing the rapid shift occurring in the workforce.

"Our research reflects the tremendous pressure CEOs are under to keep their competitive edge. Alongside profitability and productivity, getting the right skills remains a persistent challenge, with CEOs now hiring for roles that did not exist until recently," said Shanker Ramamurthy, Global Managing Partner Banking & Financial Markets, IBM Consulting. "Workforce needs are shifting rapidly in the financial services sector and CEOs must ensure that upskilling programs are prioritized as an important element of any financial institution's enterprise strategy for scaling generative AI."

In addition, 66% of BFM CEOs surveyed stated that the potential productivity gains from automation are so great that they would accept significant risks to stay competitive, with 67% saying they would risk more than their competitor to maintain competitive edge.

However, BFM CEOs recognized that trust cannot be sacrificed for innovation. 64% of surveyed BFM CEOs agreed that maintaining customer trust will have a greater impact on success than any specific product or service, and 83% acknowledged that transparency around adopting new technologies was critical for fostering trust among customers and employees.

"CEOs in the banking and financial markets sector are keenly aware of the competitive benefits that generative AI will bring and are eager to move quickly," said John Duigenan, Distinguished Engineer & General Manager, Global Financial Services Industry at IBM. "In their enthusiasm to embrace the benefits of this potent new technology, it's critical that financial services leaders ensure their institutions are taking steps to engineer trustworthy AI designed to reduce risk and win the confidence of their customers, employees and regulators."

Key Study Findings

BFM CEOs are hedging their bets on generative AI to stay competitive and are willing to take risks to achieve this.

• 57% of respondents believe that competitive advantage will depend on who has the most advanced generative AI.
• Two-thirds (66%) of those surveyed agreed that the potential productivity gains from automation are so great that they would accept significant risks to stay competitive and 67% said they would take more risk than their competitors to maintain a competitive advantage.
• However, customer trust was not a sacrifice CEOs are willing to make. 64% surveyed agreed that maintaining customer trust will have a greater impact on success than any specific product, and 83% acknowledged transparency in adopting new technologies is critical for fostering trust among customers and employees.

The workforce is shifting rapidly.

• 50% of CEOs surveyed said they are hiring for roles that did not even exist last year due to the rise of generative AI.
• Yet, more than half (53%) of respondents say they are already struggling to fill key technology roles.
• 60% of respondents said their current team has the knowledge and skills to incorporate new technologies like AI.
• Only 40% of respondents have assessed the potential impact of generative AI on their workforce.
• Surveyed CEOs say 34% of their workforce will require retraining and reskilling over the next three years – up from just 7% in 2021.

Financial institution leaders recognize it takes a cultural shift to scale AI successfully but face collaboration and adoption challenges within their organizations.

• 64% of CEOs surveyed say their organization's success is directly tied to the quality of collaboration between finance and technology, yet half (50%) say competition among their C-Suite executives sometimes impedes collaboration.
• 59% agree that cultural change is more important to becoming a data-driven business than overcoming technical challenges.
• 65% of BFM CEOs say that succeeding with AI will depend more on people's adoption than the technology itself.
• At the same time, 43% acknowledge that their employees do not fully understand how strategic decisions impact them.
• 60% of surveyed CEOs say they push for AI adoption more quickly than some might find comfortable.
• 64% of surveyed BFM CEOs say to win the future, they must rewrite their organizational playbook.
• 72% plan to maintain or accelerate their organization's pace of transformational change in

Productivity is a top priority but focusing on short-term targets may hinder long-term progress.

• BFM CEOs ranked tech modernization as their highest priority for the next three years.
• Productivity, profitability, and scalability were identified as the biggest challenges facing BFM CEOs over the next three years, with 46% agreeing that generative AI will be one of the most useful tools in helping them overcome these challenges.
• However, BFM CEOs identified the focus on short-term performance as their top barrier to innovation.

IBM is a leading provider of enterprise AI, hybrid cloud architecture, security and ESG insights to the global financial services sector. Its deep industry expertise, extensive portfolio of services and solutions, and its robust ecosystem of fintech partners, empower collaboration, innovation, and creation with clients. As a trusted partner to banks, insurers, capital markets and payments providers, IBM guides financial institutions on all stages of their digital transformation journeys through IBM Consulting and delivers the proven infrastructure, software, and services they need through IBM Technology. For more information, visit  www.ibm.com/industries/banking-financial-markets

Methodology The IBM Institute for Business Value, in cooperation with Oxford Economics, conducted interviews with 3,000 CEOs from over 30 countries and 26 industries from December 2023 through April 2024 as part of the 29 th edition of the IBM C-Suite Study series. These conversations focused on business priorities, leadership, technology, talent, partnering, regulation, industry disruption and enterprise transformation.

The IBM Institute for Business Value, IBM's thought leadership think tank, combines global research and performance data with expertise from industry thinkers and leading academics to deliver insights that make business leaders smarter. For more world-class thought leadership, visit www.ibm.com/thought-leadership/institute-business-value

About IBM IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Thousands of government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM's long-standing commitment to trust, transparency, responsibility, inclusivity and service.  Visit  www.ibm.com  for more information.

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