Foreword

I have watched more AI demos than I can count, and almost all of them go well. That is the problem.

A demo is designed to succeed. The question is clean, the data is curated, the answer appears in seconds, and everyone in the room leans forward. Then the meeting ends, and someone has to make that capability work on a Tuesday afternoon with messy records, a skeptical reviewer, an access policy nobody wrote down, and a CFO who wants to know what it costs. That is where most enterprise AI initiatives quietly stall. They don't fail loudly. They just never become anything.

I have seen this pattern across three technology cycles. I founded Nimbo during the early cloud era, and after it was acquired by Equinix, I spent years at AWS. First I led the Global Healthcare and Life Sciences AI/ML team, and later the Private Equity team. Today, at PraxisIQ, I work with private equity firms and their portfolio companies on AI transformation, using forward-deployed engineers who sit inside the business. The technology is rarely the hardest part. Operating it is.

Private equity sharpens that lesson because the questions are unforgiving. An operating partner does not ask whether the model is impressive. They ask whether it moves EBITDA inside the hold period, who owns it after the consultants leave, and whether it will survive diligence by the next buyer.

One recent engagement made this concrete for me. A midstream energy company in Houston did something I rarely see. Before committing to anything, its leadership stood up an AI committee and asked us to present to the full group. Our discovery started where most conversations start, with the ambitious ideas: route optimization, volume forecasting, and weather-dependent logistics. But the first workflow we built was invoice matching in finance. It was less exciting to demo, and it was the right call. An invoice either reconciles to the purchase order and the receipt or it doesn't. The evidence has an owner, the exception has a reviewer, and a mismatch has a clear next step. Starting there gives the company something it can measure and trust before betting on harder problems.

This book is about the gap between a promising demo and a trusted capability, and Vivek Jain names it precisely in his first chapter. A manager asks whether a quarterly reconciliation is complete. The AI answers "yes" and cites a source. The source approves a deadline extension; it says nothing about completion. Retrieval worked. The model copied the date accurately. Every component passed its test, and the business claim was still wrong. The question Vivek asks is the one I wish every executive asked: who owns the gap between the answer and the action? If nobody owns it explicitly, nobody owns it at all.

What I value most about this book is its insistence on measuring outcomes rather than motion. Logins, messages, and abandoned sessions are not adoption. Vivek counts completed work, and he counts an honest "the evidence isn't here, and here's who owns the next step" as a legitimate result. That distinction sounds small. In practice it separates a tool people trust from one they route around.

He is also willing to show the numbers going against him. In the final chapter, he runs his own fictional case through an adverse scenario: lower adoption, more effort per response, and higher operating cost. The modeled return goes from strongly positive to negative. Then he decomposes the shortfall, separates sunk cost from forward decisions, and refuses to double-count the same recovered hours as three different benefits. Few AI business cases I review would survive that scrutiny, and fewer still would volunteer it.

Underneath all of this is an idea experienced operators will recognize. Technical safeguards, business ownership, cost, and trust are not separate workstreams. They form one system. A stricter evidence standard adds reviewer time. Reviewer friction suppresses adoption. Adoption drives return. Return determines whether anyone funds the next use case. Anyone who sees only the model, or only the ROI slide, will be surprised by the rest.

Finally, this book treats stopping as a legitimate outcome. "Expand, revise, or retire" is a leadership decision, and it should be made on evidence rather than on whichever revised spreadsheet looks best. In my experience, the organizations that scale AI well are the ones willing to kill the initiatives that aren't working.

Architects will find the structure to challenge a design before it hardens into a commitment. CIOs, CTOs, and sponsors will find the right questions for their next review. Boards and investment committees will learn to tell a demo from a capability.

As Vivek writes, the demonstration is the invitation to begin the work. This book is a very good guide to the work itself.

John Shaw
CEO and Co-Founder, PraxisIQ
Austin, Texas