The AI Decision Map

The gap between AI's promise and its performance isn't a technology problem. It's a human one.

Most AI projects don't fail loudly. They fail quietly - after months of work, when nothing actually changes.

It doesn't matter whether you run a five-person business or a global enterprise. The question is the same: are you solving a real problem, or chasing a shiny solution?

Our own team once built a chatbot that answered business questions in plain English. In testing, it was magical - 85% accuracy and applause all around. Then we rolled it out to real users, and accuracy fell below 45%. Real people asked entirely different questions than the ones we'd trained for. Months of work. Zero adoption.

Klarna, Duolingo, and IBM all made versions of the same mistake - at far greater scale. The problem was never the technology. It was the question nobody asked before building it.

The AI Decision Map is written for the person who has to make AI work - not the engineer who builds it, but the leader who must answer for it. No jargon. No code.

Drawing on two decades of technology leadership across three continents, doctoral research into why people resist - or embrace - new technology, and candid conversations with hundreds of executives on his podcast, Vin Mitty delivers hard-won clarity about what works, what fails, and why.

Two frameworks. One system.

• The AI Decision Map(TM) - decide where to apply intelligence, what problem to solve, and how to measure risk, before you build anything.
• The AI Value Review(TM) - measure whether it's working, learn from outcomes, and sustain performance long after launch.

This book is for you if you have ever sat in a meeting where someone said "We should be doing more with AI" - and felt a quiet dread.

• For the small business owner wondering whether to invest in AI - or wait
• For the manager who needs to lead a technology initiative without a technical background
• For the executive being pressured by a board that has been reading too many headlines
• For anyone accountable for AI outcomes who was never handed a playbook

Readers who have enjoyed Thinking, Fast and Slow, The Innovator's Dilemma, or Good to Great will find this book speaks the same language: clear thinking, human insight, and ideas that genuinely change how you see a problem.

Built from two decades of practice, doctoral research on AI adoption, and real conversations with leaders across healthcare, financial services, legal, education, manufacturing, and retail - this is the decision-making guide executives and owners actually need.

Excitement isn't a strategy. Frameworks turn energy into real value.