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Publication Manual of the American Psychological Association, Seventh Edition (2020)

Table of Contents    |    Supplemental Resources    |    Introduction (PDF)

Official source for APA Style The Publication Manual of the American Psychological Association, Seventh Edition is the official source for APA Style.

Widely adopted With millions of copies sold worldwide in multiple languages, it is the style manual of choice for writers, researchers, editors, students, and educators in the social and behavioral sciences, natural sciences, nursing, communications, education, business, engineering, and other fields.

Authoritative and easy to use Known for its authoritative, easy-to-use reference and citation system, the Publication Manual also offers guidance on choosing the headings, tables, figures, language, and tone that will result in powerful, concise, and elegant scholarly communication.

Scholarly writing It guides users through the scholarly writing process—from the ethics of authorship to reporting research through publication.

• Spiral-Bound $44.99
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It is an indispensable resource for students and professionals to achieve excellence in writing and make an impact with their work.

7 reasons why everyone needs the 7th edition of APA’s bestselling Publication Manual

Full color with first-ever tabbed version

Guidelines for ethical writing and guidance on the publication process

Expanded student-specific resources; includes a sample paper

100+ new reference examples, 40+ sample tables and figures

New chapter on journal article reporting standards

Updated bias-free language guidelines; includes usage of singular “they”

One space after end punctuation!

What’s new in the 7th edition?

Full color All formats are in full color, including the new tabbed spiral-bound version.

Easy to navigate Improved ease of navigation, with many additional numbered sections to help users quickly locate answers to their questions.

Best practices The Publication Manual (7th ed.) has been thoroughly revised and updated to reflect best practices in scholarly writing and publishing.

New student resources Resources for students on writing and formatting annotated bibliographies, response papers, and other paper types as well as guidelines on citing course materials.

Accessibility guidelines Guidelines that support accessibility for all users, including simplified reference, in-text citation, and heading formats as well as additional font options.

New-user content Dedicated chapter for new users of APA Style covering paper elements and format, including sample papers for both professional authors and student writers.

Journal Article Reporting Standards New chapter on journal article reporting standards that includes updates to reporting standards for quantitative research and the first-ever qualitative and mixed methods reporting standards in APA Style.

Bias-free language guidelines New chapter on bias-free language guidelines for writing about people with respect and inclusivity in areas including age, disability, gender, participation in research, race and ethnicity, sexual orientation, socioeconomic status, and intersectionality

100+ reference examples More than 100 new reference examples covering periodicals, books, audiovisual media, social media, webpages and websites, and legal resources.

40+ new sample tables and figures More than 40 new sample tables and figures, including student-friendly examples such as a correlation table and a bar chart as well as examples that show how to reproduce a table or figure from another source.

Ethics expanded Expanded guidance on ethical writing and publishing practices, including how to ensure the appropriate level of citation, avoid plagiarism and self-plagiarism, and navigate the publication process.

7th edition table of contents

• Front Matter
• 1. Scholarly Writing and Publishing Principles
• 2. Paper Elements and Format
• 3. Journal Article Reporting Standards
• 4. Writing Style and Grammar
• 5. Bias-Free Language Guidelines
• 6. Mechanics of Style
• 7. Tables and Figures
• 8. Works Credited in the Text
• 9. Reference List
• 10. Reference Examples
• 11. Legal References
• 12. Publication Process
• Back Matter

List of Tables and Figures

Editorial Staff and Contributors

Acknowledgments

Introduction (PDF, 94KB)

Types of Articles and Papers

1.1 Quantitative Articles 1.2 Qualitative Articles 1.3 Mixed Methods Articles 1.4 Replication Articles 1.5 Quantitative and Qualitative Meta-Analyses 1.6 Literature Review Articles 1.7 Theoretical Articles 1.8 Methodological Articles 1.9 Other Types of Articles 1.10 Student Papers, Dissertations, and Theses

Ethical, legal, and professional standards in publishing

Ensuring the Accuracy of Scientific Findings

1.11 Planning for Ethical Compliance 1.12 Ethical and Accurate Reporting of Research Results 1.13 Errors, Corrections, and Retractions After Publication 1.14 Data Retention and Sharing 1.15 Additional Data-Sharing Considerations for Qualitative Research 1.16 Duplicate and Piecemeal Publication of Data 1.17 Implications of Plagiarism and Self-Plagiarism

Protecting the Rights and Welfare of Research Participants and Subjects

1.18 Rights and Welfare of Research Participants and Subjects 1.19 Protecting Confidentiality 1.20 Conflict of Interest

Protecting Intellectual Property Rights

1.21 Publication Credit 1.22 Order of Authors 1.23 Authors’ Intellectual Property Rights During Manuscript Review 1.24 Authors’ Copyright on Unpublished Manuscripts 1.25 Ethical Compliance Checklist

Required Elements

2.1 Professional Paper Required Elements 2.2 Student Paper Required Elements

Paper Elements

2.3 Title Page 2.4 Title 2.5 Author Name (Byline) 2.6 Author Affiliation 2.7 Author Note 2.8 Running Head 2.9 Abstract 2.10 Keywords 2.11 Text (Body) 2.12 Reference List 2.13 Footnotes 2.14 Appendices 2.15 Supplemental Materials

2.16 Importance of Format 2.17 Order of Pages 2.18 Page Header 2.19 Font 2.20 Special Characters 2.21 Line Spacing 2.22 Margins 2.23 Paragraph Alignment 2.24 Paragraph Indentation 2.25 Paper Length

Organization

2.26 Principles of Organization 2.27 Heading Levels 2.28 Section Labels

Sample papers

Overview of Reporting Standards

3.1 Application of the Principles of JARS 3.2 Terminology Used in JARS

Common Reporting Standards Across Research Designs

3.3 Abstract Standards 3.4 Introduction Standards

Reporting Standards for Quantitative Research

3.5 Basic Expectations for Quantitative Research Reporting 3.6 Quantitative Method Standards 3.7 Quantitative Results Standards 3.8 Quantitative Discussion Standards 3.9 Additional Reporting Standards for Typical Experimental and Nonexperimental Studies 3.10 Reporting Standards for Special Designs 3.11 Standards for Analytic Approaches 3.12 Quantitative Meta-Analysis Standards

Reporting Standards for Qualitative Research

3.13 Basic Expectations for Qualitative Research Reporting 3.14 Qualitative Method Standards 3.15 Qualitative Findings or Results Standards 3.16 Qualitative Discussion Standards 3.17 Qualitative Meta-Analysis Standards

Reporting Standards for Mixed Methods Research

3.18 Basic Expectations for Mixed Methods Research Reporting

Effective scholarly writing

Continuity and Flow

4.1 Importance of Continuity and Flow 4.2 Transitions 4.3 Noun Strings

Conciseness and Clarity

4.4 Importance of Conciseness and Clarity 4.5 Wordiness and Redundancy 4.6 Sentence and Paragraph Length 4.7 Tone 4.8 Contractions and Colloquialisms 4.9 Jargon 4.10 Logical Comparisons 4.11 Anthropomorphism

Grammar and usage

4.12 Verb Tense 4.13 Active and Passive Voice 4.14 Mood 4.15 Subject and Verb Agreement

4.16 First- Versus Third-Person Pronouns 4.17 Editorial “We” 4.18 Singular “They” 4.19 Pronouns for People and Animals (“Who” vs. “That”) 4.20 Pronouns as Subjects and Objects (“Who” vs. “Whom”) 4.21 Pronouns in Restrictive and Nonrestrictive Clauses (“That” vs. “Which”)

Sentence Construction

4.22 Subordinate Conjunctions 4.23 Misplaced and Dangling Modifiers 4.24 Parallel Construction

Strategies to Improve Your Writing

4.25 Reading to Learn Through Example 4.26 Writing From an Outline 4.27 Rereading the Draft 4.28 Seeking Help From Colleagues 4.29 Working With Copyeditors and Writing Centers 4.30 Revising a Paper

General Guidelines for Reducing Bias

5.1 Describe at the Appropriate Level of Specificity 5.2 Be Sensitive to Labels

Reducing Bias by Topic

5.3 Age 5.4 Disability 5.5 Gender 5.6 Participation in Research 5.7 Racial and Ethnic Identity 5.8 Sexual Orientation 5.9 Socioeconomic Status 5.10 Intersectionality

Punctuation

6.1 Spacing After Punctuation Marks 6.2 Period 6.3 Comma 6.4 Semicolon 6.5 Colon 6.6 Dash 6.7 Quotation Marks 6.8 Parentheses 6.9 Square Brackets 6.10 Slash

6.11 Preferred Spelling 6.12 Hyphenation

Capitalization

6.13 Words Beginning a Sentence 6.14 Proper Nouns and Trade Names 6.15 Job Titles and Positions 6.16 Diseases, Disorders, Therapies, Theories, and Related Terms 6.17 Titles of Works and Headings Within Works 6.18 Titles of Tests and Measures 6.19 Nouns Followed by Numerals or Letters 6.20 Names of Conditions or Groups in an Experiment 6.21 Names of Factors, Variables, and Effects

6.22 Use of Italics 6.23 Reverse Italics

Abbreviations

6.24 Use of Abbreviations 6.25 Definition of Abbreviations 6.26 Format of Abbreviations 6.27 Unit of Measurement Abbreviations 6.28 Time Abbreviations 6.29 Latin Abbreviations 6.30 Chemical Compound Abbreviations 6.31 Gene and Protein Name Abbreviations

6.32 Numbers Expressed in Numerals 6.33 Numbers Expressed in Words 6.34 Combining Numerals and Words to Express Numbers 6.35 Ordinal Numbers 6.36 Decimal Fractions 6.37 Roman Numerals 6.38 Commas in Numbers 6.39 Plurals of Numbers

Statistical and Mathematical Copy

6.40 Selecting Effective Presentation 6.41 References for Statistics 6.42 Formulas 6.43 Statistics in Text 6.44 Statistical Symbols and Abbreviations 6.45 Spacing, Alignment, and Punctuation for Statistics

Presentation of Equations

6.46 Equations in Text 6.47 Displayed Equations 6.48 Preparing Statistical and Mathematical Copy for Publication

6.49 List Guidelines 6.50 Lettered Lists 6.51 Numbered Lists 6.52 Bulleted Lists

General Guidelines for Tables and Figures

7.1 Purpose of Tables and Figures 7.2 Design and Preparation of Tables and Figures 7.3 Graphical Versus Textual Presentation 7.4 Formatting Tables and Figures 7.5 Referring to Tables and Figures in the Text 7.6 Placement of Tables and Figures 7.7 Reprinting or Adapting Tables and Figures

7.8 Principles of Table Construction 7.9 Table Components 7.10 Table Numbers 7.11 Table Titles 7.12 Table Headings 7.13 Table Body 7.14 Table Notes 7.15 Standard Abbreviations in Tables and Figures 7.16 Confidence Intervals in Tables 7.17 Table Borders and Shading 7.18 Long or Wide Tables 7.19 Relation Between Tables 7.20 Table Checklist 7.21 Sample Tables

Sample tables

7.22 Principles of Figure Construction 7.23 Figure Components 7.24 Figure Numbers 7.25 Figure Titles 7.26 Figure Images 7.27 Figure Legends 7.28 Figure Notes 7.29 Relation Between Figures 7.30 Photographs 7.31 Considerations for Electrophysiological, Radiological, Genetic, and Other Biological Data 7.32 Electrophysiological Data 7.33 Radiological (Imaging) Data 7.34 Genetic Data 7.35 Figure Checklist 7.36 Sample Figures

Sample figures

General Guidelines for Citation

8.1 Appropriate Level of Citation 8.2 Plagiarism 8.3 Self-Plagiarism 8.4 Correspondence Between Reference List and Text 8.5 Use of the Published Version or Archival Version 8.6 Primary and Secondary Sources

Works Requiring Special Approaches to Citation

8.7 Interviews 8.8 Classroom or Intranet Sources 8.9 Personal Communications

In-Text Citations

8.10 Author–Date Citation System 8.11 Parenthetical and Narrative Citations 8.12 Citing Multiple Works 8.13 Citing Specific Parts of a Source 8.14 Unknown or Anonymous Author 8.15 Translated, Reprinted, Republished, and Reissued Dates 8,16 Omitting the Year in Repeated Narrative Citations 8.17 Number of Authors to Include in In-Text Citations 8.18 Avoiding Ambiguity in In-Text Citations 8.19 Works With the Same Author and Same Date 8.20 Authors With the Same Surname 8.21 Abbreviating Group Authors 8.22 General Mentions of Websites, Periodicals, and Common Software and Apps

Paraphrases and Quotations

8.23 Principles of Paraphrasing 8.24 Long Paraphrases 8.25 Principles of Direct Quotation 8.26 Short Quotations (Fewer Than 40 Words) 8.27 Block Quotations (40 Words or More) 8.28 Direct Quotation of Material Without Page Numbers 8.29 Accuracy of Quotations 8.30 Changes to a Quotation Requiring No Explanation 8.31 Changes to a Quotation Requiring Explanation 8.32 Quotations That Contain Citations to Other Works 8.33 Quotations That Contain Material Already in Quotation Marks 8.34 Permission to Reprint or Adapt Lengthy Quotations 8.35 Epigraphs 8.36 Quotations From Research Participants

Reference Categories

9.1 Determining the Reference Category 9.2 Using the Webpages and Websites Reference Category 9.3 Online and Print References

Principles of Reference List Entries

9.4 Four Elements of a Reference 9.5 Punctuation Within Reference List Entries 9.6 Accuracy and Consistency in References

Reference elements

9.7 Definition of Author 9.8 Format of the Author Element 9.9 Spelling and Capitalization of Author Names 9.10 Identification of Specialized Roles 9.11 Group Authors 9.12 No Author

9.13 Definition of Date 9.14 Format of the Date Element 9.15 Updated or Reviewed Online Works 9.16 Retrieval Dates 9.17 No Date

9.18 Definition of Title 9.19 Format of the Title Element 9.20 Series and Multivolume Works 9.21 Bracketed Descriptions 9.22 No Title

9.23 Definition of Source 9.24 Format of the Source Element 9.25 Periodical Sources 9.26 Online Periodicals With Missing Information 9.27 Article Numbers 9.28 Edited Book Chapter and Reference Work Entry Sources 9.29 Publisher Sources 9.30 Database and Archive Sources 9.31 Works With Specific Locations 9.32 Social Media Sources 9.33 Website Sources 9.34 When to Include DOIs and URLs 9.35 Format of DOIs and URLs 9.36 DOI or URL Shorteners 9.37 No Source

Reference Variations

9.38 Works in Another Language 9.39 Translated Works 9.40 Reprinted Works 9.41 Republished or Reissued Works 9.42 Religious and Classical Works

Reference List Format and Order

9.43 Format of the Reference List 9.44 Order of Works in the Reference List 9.45 Order of Surname and Given Name 9.46 Order of Multiple Works by the Same First Author 9.47 Order of Works With the Same Author and Same Date 9.48 Order of Works by First Authors With the Same Surname 9.49 Order of Works With No Author or an Anonymous Author 9.50 Abbreviations in References 9.51 Annotated Bibliographies 9.52 References Included in a Meta-Analysis

Author Variations

Date Variations

Title Variations

Source Variations

Textual Works

10.1 Periodicals 10.2 Books and Reference Works 10.3 Edited Book Chapters and Entries in Reference Works 10.4 Reports and Gray Literature 10.5 Conference Sessions and Presentations 10.6 Dissertations and Theses 10.7 Reviews 10.8 Unpublished Works and Informally Published Works

Data Sets, Software, and Tests

10.9 Data Sets 10.10 Computer Software, Mobile Apps, Apparatuses, and Equipment 10.11 Tests, Scales, and Inventories

Audiovisual Media

10.12 Audiovisual Works 10.13 Audio Works 10.14 Visual Works

Online Media

10.15 Social Media 10.16 Webpages and Websites

General Guidelines for Legal References

11.1 APA Style References Versus Legal References 11.2 General Forms 11.3 In-Text Citations of Legal Materials

Legal Reference Examples

11.4 Cases or Court Decisions 11.5 Statutes (Laws and Acts) 11.6 Legislative Materials 11.7 Administrative and Executive Materials 11.8 Patents 11.9 Constitutions and Charters 11.10 Treaties and International Conventions

Preparing for Publication

12.1 Adapting a Dissertation or Thesis Into a Journal Article 12.2 Selecting a Journal for Publication 12.3 Prioritizing Potential Journals 12.4 Avoiding Predatory Journals

Understanding the Editorial Publication Process

12.5 Editorial Publication Process 12.6 Role of the Editors 12.7 Peer Review Process 12.8 Manuscript Decisions

Manuscript Preparation

12.9 Preparing the Manuscript for Submission 12.10 Using an Online Submission Portal 12.11 Writing a Cover Letter 12.12 Corresponding During Publication 12.13 Certifying Ethical Requirements

Copyright and Permission Guidelines

12.14 General Guidelines for Reprinting or Adapting Materials 12.15 Materials That Require Copyright Attribution 12.16 Copyright Status 12.17 Permission and Fair Use 12.18 Copyright Attribution Formats

During and After Publication

12.19 Article Proofs 12.20 Published Article Copyright Policies 12.21 Open Access Deposit Policies 12.22 Writing a Correction Notice 12.23 Sharing Your Article Online 12.24 Promoting Your Article

Credits for Adapted Tables, Figures, and Papers

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Core Values in Education From the Perspective of Future Educators

Introduction, theoretical framework, declaration of conflicting interests, supplementary material, cite article, share options, information, rights and permissions, metrics and citations, figures and tables, values and preservice teachers, schwartz cultural values theory.

Values in Education

Values education in turkey, research approach, participants, the main five values that teachers ought to have according to preservice teachers.

The Main Five Values That School Principals Should Have According to Preservice Teachers

The Main Five Values That Students Should Acquire at Schools According to Preservice Teachers

The Main Values for Teachers According to Preservice Teachers

The Main Values of School Principals According to Preservice Teachers

The Main Values That Students Ought to Acquire at Schools According to Preservice Teachers

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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If you have diabetes, late-night snacks aren't necessarily off-limits — but it's important to make healthy choices.

Late-night snacks add extra calories, which can lead to weight gain. And, if you snack after your evening meal — especially on foods with carbohydrates — you may wake up the next morning with a high blood sugar level.

If you're feeling hungry after dinner, try drinking a glass of water first. Sometimes thirst mimics hunger. If you're still feeling hungry, your best bet is a low-carbohydrate, low-calorie snack, preferably one that's high in protein or fiber, such as Greek yogurt or a small handful of nuts.

Other options include:

• A sugar-free frozen pop
• One light cheese stick
• One tablespoon of peanut butter (15 grams) and celery
• A hard-boiled egg
• Five baby carrots
• Light popcorn, 3/4 cup (approximately 6 grams)
• Salad greens with cucumber and a drizzle of oil and vinegar

If you take insulin or other diabetes medications, you may sometimes need to snack before bedtime to treat or prevent low blood sugar (hypoglycemia) during the night. If this happens frequently, talk to your doctor. He or she may recommend adjusting the dose of your medications to prevent the need for a late-night snack.

M. Regina Castro, M.D.

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