Most organizations don't fail at AI because the models are weak. They fail because they mistake technical progress for economic progress. From Models to Money reframes AI as a capital allocation and systems design problem, not a data science challenge. Written for CTOs, engineering leaders, and technical product managers, the book's authors, Davood Shamsi and Robert Luenberger, explain why successful pilots collapse in production, why accuracy improvements often destroy value, and why AI only matters when it changes real decisions. Using concepts like decision audits, risk-adjusted value, real options for learning systems, and minimal credible evidence, you'll learn to govern AI as a durable asset rather than manage it as a technical experiment. It's a rigorous guide for leaders who want AI to strengthen their competitive moat, not just their technology budget.
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