Most AI projects never ship. Depending on which analyst you read, somewhere between 80 and 90 percent of them die in the gap between a promising demo and anything a business can actually rely on. Today Strongly.AI's Chief AI Officer, Jarrod Vawdrey, is publishing a book about the other 10 to 20 percent - and what they do differently.
Leading Through the AI Revolution: The C.H.A.R.T. Framework for Turning AI Pilots Into Production is out now. It is a leader's field guide, built from real deployments across public and commercial sectors, for getting AI past the pilot and into production - and for keeping it there.
The book's argument fits in a sentence. AI transformation is a leadership discipline, not a technology purchase. Nearly every organization stuck in that 80 to 90 percent already has the licenses. What they lack is a set of decisions: which problems AI should solve, whose workflows change, how success is measured, and who is accountable for the result.
“A license gives your people a tool. A strategy tells them what the tool is for. Confusing the two is the most expensive mistake in enterprise AI today.
The one idea: C.H.A.R.T.
If you take one thing from the book, it is that AI transformation is a leadership discipline, and C.H.A.R.T. is how you lead it. Five dimensions separate organizations that transform through AI from those that just accumulate expensive experiments. Every chapter develops one of them and closes by connecting its evidence back to the framework.
Find the decision in front of you, then go to the dimension that governs it. Appendix B maps every template and checklist in the book to its C.H.A.R.T. dimension.
What's inside
Ten chapters across four parts, plus the tools to act on them. It is built to be used, not just read: every chapter closes with key takeaways, and the back of the book is a working toolkit rather than filler.
The current landscape, AI demystified for executive decisions, and a strategic framework: the three critical questions, the fast-and-slow duality, and the path from pilot to production.
The human-AI partnership model, a 90-day reskilling roadmap, a change-management playbook, and responsible AI and risk management as competitive advantage.
Use cases across industries with documented returns, how to measure and communicate value, build-vs-buy-vs-partner, and a twelve-question board governance guide.
The regulatory landscape, near- and medium-term trends and scenario planning, and a full 180-day action plan from AI-maturity assessment to launch and scale.
Beyond the chapters: a 180-Day Action Plan (Chapter 10) that turns the framework into a calendar, a Board Governance Guide of twelve questions directors should ask, the C.H.A.R.T. Toolkit that indexes every template and checklist by the dimension it serves, and a plain-language glossary of the terms executives keep tripping over.
“Wherever you enter, finish with Chapter 10. Nothing in this book matters until it becomes a calendar.
Built from results, not projections
The case for acting no longer rests on forecasts. It rests on what has already shipped. Each chapter pulls takeaways from real deployments - the wins and the failures - rather than tidy hypotheticals.
The examples are named and sourced. Morgan Stanley equipped 98 percent of its 16,000 financial advisors with an AI assistant that handles retrieval while people keep the judgment and the accountability. Pfizer compressed a knowledge-transfer process from nine months to days, projecting $750 million to $1 billion in annual savings. IBM delivered $4.5 billion in productivity gains through AI-powered automation. Bank of America's Erica passed two billion client interactions.
The cautionary cases are just as instructive. Duolingo's framing of AI as cost-cutting rather than capability triggered consumer backlash and a 41 percent stock decline. The lesson the book draws is not to move slowly. It is to move deliberately - which is a different thing, and the whole reason the framework exists.
Used well, generative AI raises skilled workers' output quality by more than 40 percent on suitable tasks - and leaves them 19 percentage points less likely to be correct beyond its boundaries. The winners design for both numbers. That is what "augmentation, not automation" means in practice.
Who it's for, and how to read it
It is written for the people accountable for AI outcomes, not just the teams building the models: executives, board members, and the P&L owners who have to turn a demo into a number. You do not need to be technical. If you have forty-five minutes, read the introduction, the key takeaways that close each chapter, the board governance guide, and the 180-day plan.
Start with the Board Governance Guide (7.6) - the twelve questions to ask, what to listen for, and what to flag - then the 180-day plan.
Start with Chapter 6: the value spectrum from quick wins to transformative bets, proven use cases by industry, and the ROI board pitch.
Finish with Chapter 10, the 180-Day Action Plan - assess maturity, align, build foundations, launch, and scale on a real timeline.
About the author
“The AI revolution is not something happening to your organization. It is something your organization can lead.
Read the introduction free, then take it from there
The full introduction and first chapter are free to read on the book page - no video, no fluff. When you are ready for the frameworks, the case studies, and the 180-day plan, the book is on Amazon.