Overview

There has never been a more consequential - or more confusing - time to be in the business of Data and AI strategy. When I began working at the intersection of enterprise technology and business strategy, the central challenge facing organizations was straightforward in its diagnosis, if not its remedy: companies had accumulated vast quantities of data. They had no coherent plan for using it. The problem was one of inertia. The data existed. The opportunity was visible. But the organizational will, governance structures, talent, and strategic clarity required to act on it were largely absent. That was a difficult problem. What we face today is more complex. The emergence of generative AI, the proliferation of large language models, and the arrival of agentic systems capable of autonomous multi-step reasoning - these developments have not simplified the challenge of data and AI strategy. They have amplified it. They have raised the stakes, compressed timelines, introduced entirely new categories of risk, and raised the bar for what it means to provide credible, responsible counsel to enterprise organizations navigating this terrain. And yet, despite the explosion of interest in AI - despite the billions of dollars being committed to AI initiatives in boardrooms around the world - the fundamental discipline of data and AI strategy consulting has not kept pace. Too many organizations are still receiving advice that is anchored in yesterday's problems: data lake migrations, BI tool rationalization, and basic ML model deployment. Too few are receiving the kind of rigorous, integrated strategic guidance that the current moment demands - guidance that connects GenAI deployment to data governance foundations, that links AI investment to organizational operating model design, which balances the enormous opportunity of artificial intelligence against its equally enormous risk. This book was written to help close that gap.

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