Provides senior leaders with a practitioner-led framework for turning AI investments into measurable business outcomes, combining three leadership disciplines across seven sequential steps from discovery to production and continuous optimization.

Fewer than 20% of organizations achieve significant financial returns from their AI investments. Eighty-five percent of AI projects fail to move from pilot to production. Seventy percent of AI implementations fall short of their intended objectives. These are not the numbers of a failing technology. They are the numbers of a technology being mis-deployed at scale, and they share a common cause: executives have been taught to admire AI but not to lead it.
From AI Strategy to Business Value offers a fundamentally different approach to AI executive education—one grounded not in technology evangelism or strategic abstraction but in the disciplined practice of value realization. At its center is the Applied Data AI Value Realization Framework, developed across thirty years of leading AI transformations at the highest levels of regulated U.S. financial services. The framework organizes executive learning around three interdependent disciplines: Technical Leadership Excellence (the ability to evaluate and guide AI without being a hands-on practitioner), Business Co-Creation and Ownership (the capacity to engage business stakeholders as genuine partners in value creation), and Empathetic Leadership and Success Coaching (the human change leadership that AI transformation requires). These three disciplines are applied across seven sequential steps from customer-experience discovery through production deployment and continuous optimization.
Drawing on the authors’ combined decades of experience, the book examines the recurring failure modes of existing executive AI training, traces the history of AI for leaders who need historical context, surveys the leading AI maturity models in active use, and develops three strategic approaches to AI transformation—top-down, bottom-up, and hybrid—together with the conditions under which each is appropriate. The closing chapters set out the investment principles executives should apply when allocating capital to AI and the operational principles that must underlie any AI strategy, from data foundations and governance to human–AI collaboration, scaling, and intellectual property.
Written for executives who must allocate capital, govern risk, and lead through AI’s most consequential decisions, this is the practitioner’s reference that closes the gap between AI’s promise and its production. The framework is grounded in experience, not in theory: every element addresses a failure mode the authors have observed in practice, and every practice it recommends has been validated in demanding, high-stakes regulated environments.