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Foreword

I HAVE SPENT THE BETTER part of two decades watching executives make technology bets, and I have learned to be suspicious of books that promise a shortcut through complexity. This is not that book.

What Samuel Conn has written instead is something more rare: a guide that respects how much genuine intellectual work is required to turn artificial intelligence from a line item into a source of durable advantage. He does not ask you to take AI on faith, and he does not ask you to become an engineer. He asks something harder… that you become literate enough in the technology to govern it, strategic enough to prioritize it correctly, and patient enough to survive the trough before the payoff arrives.

That patience is, I think, the book's quiet argument. Long before most executives had heard the term, this book had already internalized what Erik Brynjolfsson's research on general-purpose technologies predicts: that the value of a transformation like this one does not arrive on the schedule of the technology. It arrives on the schedule of the organization, namely its workflows, its data foundations, its willingness to redesign itself rather than simply automate what it already does. Every enterprise now living through the gap between AI's promise and its measured return will recognize this book's diagnosis of that gap, and will find, chapter by chapter, the actual work required to close it.

That work is unglamorous, which is exactly why so few books cover it properly. Data architecture. Cloud economics. The build-buy-partner decision. The question of who, precisely, owns an AI system's failures when it has none of the accountability structures a human employee would have. Dr. Conn does not skip past these questions to get to the exciting part. He treats them as the exciting part, because he understands, correctly, that they are where AI initiatives succeed or quietly die.

I found myself most persuaded by the sections other authors tend to avoid entirely: the honest accounting of why AI projects fail even after the pilot succeeds, the case for treating data and model advantages as genuine competitive moats rather than marketing language, and the argument that governance is not a brake on innovation but the structural capability that lets innovation compound safely over time. Few business books are willing to sit with failure long enough to learn its actual mechanics. This one does.

If you are a leader who has been handed responsibility for your organization's AI strategy (whether that responsibility came with a title like Chief AI Officer or simply arrived on your desk unannounced) you will find in these pages what you actually need: not certainty, which no one can honestly offer you right now, but a map of the terrain and a disciplined way of thinking about it. That is a rarer gift than it sounds, and it is the reason I recommend this book without reservation to anyone standing where I once stood, uncertain how to lead through a transformation that had already begun.

Christopher Markham, Ph.D.
President and CEO
New Jersey Edge.Net, Inc.