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About This Book

This website provides resources for teaching systems thinking to a broad variety of audiences, from 8th grade through executives. It introduces systems thinking principles through the lens of artificial intelligence and modern technological systems. Students learn to recognize patterns, feedback loops, and leverage points in complex AI-driven systems while developing practical skills for navigating our interconnected digital world. For advanced students, the book also covers complex adaptive systems and simulations.

Welcome from Sage

Welcome!

Sage the Owl waving welcome Whoo's ready to connect the dots? I'm Sage, and I'll be popping up throughout this book to help you zoom out and see the whole system. Systems thinking isn't about memorizing more facts — it's about noticing the loops, delays, and leverage points that were hiding in plain sight all along. Let's zoom out and see the whole system!

Why This Intelligent Textbook

Artificial intelligence is now woven into how teenagers learn in school, how IT departments design their data platforms, and how executives make billion-dollar decisions. But with that power comes complexity: unintended consequences, rapid feedback loops, and deeply interconnected systems that behave in unpredictable ways. Traditional training teaches facts; it rarely teaches the habit of mind needed to reason about a system as a whole. Systems thinking is that habit of mind.

In the United States (2025):

  • Poor data quality — much of it a direct symptom of teams and departments working from disconnected, siloed information rather than a shared model of the organization — costs the average organization $12.9 million per year, according to Gartner research[^1]
  • McKinsey's 2025 State of AI survey found that 88% of organizations now report regular AI use in at least one business function, up from 78% a year earlier — yet most are still struggling to translate that adoption into measurable value, a gap systems thinking is well suited to diagnose[^2]
  • The World Economic Forum's Future of Jobs Report 2023 named systems thinking among the top 10 skills organizations expect to grow in importance through 2027, as employers look for workers who can reason about interconnected, AI-accelerated systems rather than isolated tasks[^3]

Worldwide:

  • IBM's Institute for Business Value found that 83% of business leaders believe data silos undermine innovation by blocking the cross-departmental sharing of ideas, and 77% say silos hinder real-time analytics[^4]
  • The global enterprise knowledge graph market — the technology this book uses to model how organizational knowledge actually connects — was valued at $2.9 billion in 2025 and is projected to grow to $13.4 billion by 2033[^5]

These numbers describe the same underlying problem from different angles: organizations, schools, and students alike are surrounded by interconnected systems but are rarely taught how to see the connections. This textbook exists to close that gap.

This book takes a different approach than a traditional textbook. It is built on a learning graph of 528 interconnected concepts organized into 13 taxonomy categories, so ideas are introduced only after their prerequisites have been established — no jumping ahead, no assumed background. Throughout the book you will find 56 interactive MicroSims — browser-based simulations that let you manipulate causal loop diagrams, stock-and-flow models, and archetype behaviors instead of just reading about them. The entire textbook is open source and free — no paywalls, no access codes, no expensive annual editions — and it is written at an accessible reading level with every technical term defined on first use.

Who This Book Serves

Today's students will grow into tomorrow's leaders in a world where AI is everywhere. Learning systems thinking early gives them:

  • Critical foresight — the ability to ask "What happens next?" instead of just memorizing facts
  • Collaboration skills — recognizing that problems like misinformation or resource shortages require teamwork across disciplines
  • Resilience — understanding that setbacks are part of larger cycles builds the confidence to respond with creativity instead of despair

Business leaders stand at the helm of organizations being reshaped by AI. Systems thinking helps executives:

  • Anticipate second-order effects — a cost-cutting automation today may damage long-term customer trust tomorrow
  • Balance competing priorities — profit, sustainability, and ethics interact dynamically rather than existing as isolated goals
  • Lead responsibly — weighing not just quarterly returns, but the systemic impact of decisions on employees, communities, and society

This range — from an 8th grader's first causal loop diagram to an executive's enterprise data strategy — is why the book maintains seven separate course descriptions, one per audience, while sharing the same chapter content across all of them.

How to Use This Book

This textbook is designed for self-paced study, though chapters build on one another, so reading in order is recommended for a first pass. The book includes:

  • 27 chapters covering systems thinking foundations, feedback and system dynamics, systems archetypes, leverage points, graph theory and knowledge representation, enterprise knowledge graphs, and AI systems dynamics
  • 56 interactive MicroSims embedded in chapters — browser-based simulations you can manipulate to explore concepts like causal loop diagrams, tragedy of the commons, and stock-and-flow behavior
  • 7 illustrated stories — short graphic-novel-style narratives that dramatize archetypes like Moore's Law, Metcalfe's Law, and the tragedy of the commons
  • A glossary of 528 terms, one for every concept in the learning graph
  • A Learning Graph visualizing how all 528 concepts connect across chapters and 13 taxonomy categories — useful if you want to explore non-linearly or check prerequisites for a specific topic
  • Systems archetypes and case studies — a catalog of recurring organizational patterns (Fixes That Fail, Tragedy of the Commons, Success to the Successful, and more) with real-world applications
  • A curated reference list linking to the books, papers, and tools that shaped this material
  • Search available from any page using the search bar

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. You can visit the Intelligent Textbooks Case Studies to see over 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

Background

I was originally introduced to formal Systems Thinking and Complex Adaptive Systems by my good friend Arun Batchu. Arun and I were always talking about the power of taking a deeper look not just at things, but at the connections between things.

I have been teaching systems thinking courses since the fall of 2015. At the time these courses were small and customized to the needs of individual groups. Most of my work was teaching Systems Thinking to technologists. Many of the examples focused on the need for hidden enterprise data infrastructure such as metadata management and a robust data governance system that made data scientists more productive. However, my content was still basic PowerPoint slides.

In 2017 I started to migrate my systems thinking training content to an online format and began rolling out more formal classes to managers and executives at Optum and United Health Group.

When GPT-3 was released in June 2020 I began aggressively using it to generate causal information. Although it could not create high-quality causal loop diagrams on its own, I was convinced that deep causal knowledge was buried in LLMs. Our job was to figure out a way to get it out and make it useful to students of systems thinking.

Finally, in February 2021, I started sharing my systems thinking training content publicly. The online content was still incomplete, but I started getting positive feedback from a diverse audience.

In 2022 I was asked to deliver a workshop on Graph Systems Thinking. This was one of my first chances to help other graph evangelists build a deeply holistic view of information in an organization.

At UHG/Optum I was also asked to apply systems thinking to break down organizational silos and share data across business areas. I want to give my deep appreciation to John Santelli (then the CIO of Optum) for his support rolling out my Systems Thinking courses to management at UHG and Optum.

Relationship Between Systems Thinking and Enterprise Knowledge Graphs

I believe strongly that many business people in Fortune 500 organizations today only have a pinhole view of information flows in their organization. This prevents them from understanding how their products compete with competitive products and how those dynamics impact their customers and organizational effectiveness.

That is what the new enterprise knowledge graph (EKG) industry is creating for organizations. The hypothesis of this book is that the old ways of problem solving with relational databases will not work at the scale of EKGs. We need new ways to think. Systems Thinking is the foundation of this new generation of problem-solving tools.

Our Values

We value storytelling and the use of metaphors to help us communicate with non-technical staff. There is no programming or math background required for this book, although familiarity with drawing tools and online shared whiteboarding tools is encouraged.

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). Systems Thinking in the Age of AI. https://dmccreary.github.io/systems-thinking/

Chicago (17th edition)

McCreary, Dan. 2026. Systems Thinking in the Age of AI. https://dmccreary.github.io/systems-thinking/.

MLA (9th edition)

McCreary, Dan. Systems Thinking in the Age of AI. 2026, dmccreary.github.io/systems-thinking/.

BibTeX

@book{ mccreary2026systemsthinking,
  title     = { Systems Thinking in the Age of AI },
  author    = { McCreary, Dan },
  year      = { 2026 },
  url       = { https://dmccreary.github.io/systems-thinking/ },
  note      = { Interactive intelligent textbook }
}

To cite a specific chapter, append the chapter number and title — for example:

McCreary, D. (2026). Chapter 1: Foundations of Systems Thinking. In Systems Thinking in the Age of AI. https://dmccreary.github.io/systems-thinking/chapters/01-foundations-of-systems-thinking/

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]: Gartner. (2020). Data Quality: Why It Matters and How to Achieve It. https://www.gartner.com/en/data-analytics/topics/data-quality [^2]: McKinsey & Company. (2025). The State of AI: Agents, Innovation, and Transformation. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai [^3]: World Economic Forum. (2023). The Future of Jobs Report 2023. https://www.weforum.org/publications/the-future-of-jobs-report-2023/ [^4]: IBM Institute for Business Value. What Are Data Silos? https://www.ibm.com/think/topics/data-silos [^5]: Grand View Research. (2025). Enterprise Knowledge Graph Market Size, Share & Growth Report. https://www.grandviewresearch.com/industry-analysis/enterprise-knowledge-graph-market-report