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About This Book

Welcome from Ledger

Welcome!

Ledger waving welcome Hello — I'm Ledger, a fox who's spent a career translating what generative AI does into what it actually costs. This book exists because too many smart teams can tell you that they're using GenAI, but not whether it's paying for itself — and that gap is exactly what we're going to close together, one token at a time. Every token counts. Let's get started.

Why This Intelligent Textbook

Generative AI is now embedded in software development, data science, and customer-facing workflows across nearly every industry — and enterprises are pouring unprecedented amounts of money into it. Yet a striking number of the people writing the checks cannot answer a basic question their own finance leadership will ask: is this actually paying off?

Across the enterprise landscape (2025–2026):

  • MIT's Project NANDA found that 95% of generative AI pilots deliver no measurable impact on the bottom line within roughly six months of deployment, despite an estimated $30–40 billion in enterprise investment1
  • 56% of CEOs report zero measurable revenue or cost benefit from their organization's AI investment, according to PwC's 29th Annual Global CEO Survey of 4,454 chief executives across 95 countries2

Worldwide:

  • Gartner forecasts worldwide AI spending will reach $2.59 trillion in 2026 — a 47% increase year-over-year3
  • The share of companies allocating more than 5% of their total technology budget to AI has nearly doubled in 18 months to 34%, and reaches 52% among companies with more than $10 billion in annual revenue, per McKinsey's State of AI research4

These numbers describe a field spending at an extraordinary rate while, by its own admission, mostly failing to measure whether that spending works. If you're an engineer, data scientist, or technical manager whose organization is part of that spending — this gap is now part of your job, whether or not anyone gave it a name yet.

This book takes a fundamentally different approach than a slide deck of best practices. It is built on a learning graph of 561 interconnected concepts organized into 13 categories, moving from what a token literally is, through the financial vocabulary (ROI, TCO, NPV, payback period) a technologist needs to speak fluently, into concrete cost-optimization techniques across the full software and data science lifecycle, and finally into the mechanics of writing an executive-ready ROI report. Concepts are introduced strictly in prerequisite order — you are never asked to understand something before its foundations have been built. The entire book is open and free to read — no paywalls, no access codes.

How to Use This Book

This textbook is designed for self-paced study by practicing IT professionals. Each chapter builds directly on earlier ones, so reading in order is recommended, especially early on. The book is organized around:

  • 27 chapters spanning GenAI foundations and token economics through cost optimization, vendor economics, risk/governance, and executive financial reporting
  • A Glossary of 561 ISO 11179-style definitions covering every concept in the learning graph
  • Quizzes and Annotated References (Wikipedia, credited textbooks, and verified online resources) attached to each chapter as its content is finished
  • A Learning Graph you can explore interactively to see how any concept connects to the rest of the book
  • Ledger, a mascot who flags key ideas, warns about common pitfalls, and celebrates milestones — never decoration, always a signal worth pausing on

This book is under active development

Chapter structure exists for all 27 chapters, but full content, quizzes, and references are being written and published chapter by chapter. Completed chapters currently include Chapter 1 (Core Concepts of Large Language Models) and Chapter 2 (Prompting, Deployment, and Model Optimization Basics). Check back as more chapters are finished.

The Learning Graph visualizes how all 561 concepts connect across chapters. If you want to check prerequisites for a specific topic before diving in, start there.

About the Author

Katie Jo Schuman — Author.

A fuller author bio is planned for this section.

How to Cite This Book

If you reference this textbook in academic work, internal training materials, or other publications, please use one of the following citation formats.

APA (7th edition)

Schuman, K. J. (2026). Measuring GenAI ROI: Token Efficiency and Executive Financial Reporting. https://Kschuman-aon.github.io/genai-roi/

Chicago (17th edition)

Schuman, Katie Jo. 2026. Measuring GenAI ROI: Token Efficiency and Executive Financial Reporting. https://Kschuman-aon.github.io/genai-roi/.

MLA (9th edition)

Schuman, Katie Jo. Measuring GenAI ROI: Token Efficiency and Executive Financial Reporting. 2026, Kschuman-aon.github.io/genai-roi/.

BibTeX

@book{schuman2026genairoi,
  title     = {Measuring GenAI ROI: Token Efficiency and Executive Financial Reporting},
  author    = {Schuman, Katie Jo},
  year      = {2026},
  url       = {https://Kschuman-aon.github.io/genai-roi/},
  note      = {Interactive intelligent textbook}
}

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

Schuman, K. J. (2026). Chapter 1: Core Concepts of Large Language Models. In Measuring GenAI ROI. https://Kschuman-aon.github.io/genai-roi/chapters/01-core-concepts-llms/

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. MIT Project NANDA. (2025). The GenAI Divide: State of AI in Business 2025. Massachusetts Institute of Technology. Reported in Fortune. ↩

  2. PwC. (2026). 29th Annual Global CEO Survey: Leading Through Uncertainty in the Age of AI. PricewaterhouseCoopers. https://www.pwc.com/gx/en/ceo-survey/2026/pwc-ceo-survey-2026.pdf ↩

  3. Gartner. (2026). Gartner Says Worldwide AI Spending Will Total $2.5 Trillion in 2026. https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026 ↩

  4. McKinsey & Company. (2026). The State of AI. McKinsey QuantumBlack. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai ↩