Skip to content

About This Book

Welcome from Lens

Welcome!

Lens welcoming readers I am Lens, your evidence-focused research guide. Together, we will examine what AI personas can reveal, where their answers can mislead, and how to test marketing ideas without mistaking simulation for customer evidence. Bring your questions—and your healthy skepticism. Let's look closer.

Why This Intelligent Textbook

AI-assisted research is moving from experiment to everyday practice faster than most marketing teams can establish reliable methods for using it. The urgent question is no longer whether marketers will work with AI, but whether they can use it to learn quickly without overstating what synthetic customers can prove.

In the United States and North America:

  • As of February 2026, 50% of U.S. employees used AI at work at least a few times a year, while 28% used it at least a few times a week and 13% used it daily.1
  • The U.S. Bureau of Labor Statistics projects 6% employment growth for advertising, promotions, and marketing managers from 2024 to 2034, with about 36,400 openings each year on average.2
  • In the American Marketing Association's 2024 survey of 1,279 marketing practitioners—a sample that skewed toward North America—43% named generative AI as a skill that will grow in importance over the next five years.3

Worldwide:

  • The 2026 Stanford AI Index reports that 88% of respondents said their organizations used AI in at least one business function in 2025, and 79% reported regular generative-AI use in at least one function.4
  • In a Qualtrics survey of more than 3,000 researchers across 17 countries, 95% said they were using AI tools regularly or experimenting with them.5
  • The World Economic Forum reports that 86% of surveyed employers expect AI and information-processing technologies to transform their business by 2030, while 63% identify skills gaps as a leading barrier to transformation.6

These numbers represent the teams your learners work on and the decisions they will be asked to defend. They need more than fast outputs: they need a method for separating customer evidence from AI-generated inference.

This book provides that method. It is built on a validated learning graph of 400 interconnected concepts, introduced in prerequisite order so later work rests on established foundations. Its 82 interactive MicroSims let learners practice research design, persona construction, prompt testing, multi-agent orchestration, evaluation, and reporting in the browser. The book uses plain-language explanations, a 400-term glossary, and frequent links between concepts so specialized terms do not become hidden prerequisites. It is also open source and free to read—there are no paywalls, access codes, or annual-edition fees.

How to Use This Book

This textbook is designed for self-paced study by marketing professionals, brand strategists, product marketers, UX researchers, consultants, and business leaders. No programming experience is required. Because concepts are introduced in dependency order, reading the chapters in sequence is recommended.

The book includes:

  • 20 chapters spanning evidence quality, responsible AI, customer personas, segmentation, prompt engineering, agent workflows, marketing evaluation, knowledge graphs, automation, and reporting
  • 82 interactive MicroSims for hands-on exploration and practice
  • 20 chapter quizzes, each with feedback and explanations
  • A 400-term glossary defining every concept in the learning graph
  • An FAQ that connects common questions to relevant chapters and concepts
  • A learning graph of 400 concepts showing prerequisite relationships
  • Full-text search from any page using the search bar

The Learning Graph shows how concepts connect across chapters. Use it to explore non-linearly or to check the prerequisites for a specific topic. Throughout the book, remember that an AI persona is a tool for generating hypotheses and testing artifacts—not a replacement for research with real customers.

About the Author

Dan McCreary is a semi-retired AI researcher, solution architect, and educator who has spent more than three decades helping Fortune 100 organizations reason over massive datasets. At Optum he founded the Generative AI Center of Excellence and led the team that built one of the world's largest healthcare knowledge graphs—spanning over 25 billion vertices—to unify member, provider, and patient insights. Dan's deep background in knowledge representation and systems thinking underpins the precise learning graphs and intelligent textbook workflows used throughout this course.

He is the co-author of Making Sense of NoSQL (Manning Publications), the founding chair of the NoSQL Now! conference, and a frequent keynote speaker on semantic search, ontology strategy, and AI hardware. Beyond industry, Dan has mentored students as a STEM volunteer since 2014 and now applies the same rigor to building open educational resources. Visit the Intelligent Textbooks Case Studies to see more than 87 textbooks that Dan has created or co-created with other authors.

Selected Credentials

  • B.A. in Physics and Computer Science from Carleton College
  • M.S.E.E. from the University of Minnesota
  • MBA coursework at the University of St. Thomas
  • Patent holder in semantic search and ontology management techniques
  • Advocate for large-scale enterprise knowledge graph adoption across healthcare and education
  • Long-time promoter of accessible, low-cost AI-powered learning experiences

How to Cite This Book

If you reference this textbook in academic work, curriculum proposals, lesson plans, or other publications, please use one of the following citation formats.

APA (7th edition)

McCreary, D. (2026). AI Persona Testing. https://dmccreary.github.io/ai-persona-testing/

Chicago (17th edition)

McCreary, Dan. 2026. AI Persona Testing. https://dmccreary.github.io/ai-persona-testing/.

MLA (9th edition)

McCreary, Dan. AI Persona Testing. 2026, dmccreary.github.io/ai-persona-testing/.

BibTeX

@book{mccreary2026aipersonatesting,
  title  = {AI Persona Testing},
  author = {McCreary, Dan},
  year   = {2026},
  url    = {https://dmccreary.github.io/ai-persona-testing/},
  note   = {Interactive intelligent textbook}
}

To cite a specific chapter, include its number and title. For example:

McCreary, D. (2026). Chapter 1: AI-Powered Customer Research Foundations. In AI Persona Testing. https://dmccreary.github.io/ai-persona-testing/chapters/01-ai-customer-research/

License

This work is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). You are free to share and adapt the material for non-commercial purposes as long as you give appropriate credit and share your adaptations under the same license.

References

  1. Gallup. (2026). Artificial Intelligence: Global Indicator. https://www.gallup.com/699797/indicator-artificial-intelligence.aspx ↩

  2. U.S. Bureau of Labor Statistics. (2025). Advertising, Promotions, and Marketing Managers: Occupational Outlook Handbook. https://www.bls.gov/ooh/management/advertising-promotions-and-marketing-managers.htm ↩

  3. O'Brien, J. (2025). The Skills Marketers Need in 2025 and Beyond. American Marketing Association. https://www.ama.org/2025/01/31/2025-marketing-skills-report/ ↩

  4. Stanford Institute for Human-Centered Artificial Intelligence. (2026). AI Index Report 2026: Chapter 4—Economy. https://hai.stanford.edu/assets/files/ai_index_report_2026_chapter_4_economy.pdf ↩

  5. Webster, W., & Davis, R. (2025). The 4 Market Research Trends Shaping 2026. Qualtrics. https://www.qualtrics.com/articles/strategy-research/market-research-trends/ ↩

  6. World Economic Forum. (2025). The Future of Jobs Report 2025. https://www.weforum.org/publications/the-future-of-jobs-report-2025/ ↩