Enterprise AI Management

Enterprise AI is no longer primarily an adoption problem. It is an enterprise decision problem.
A business unit wants to scale. Finance questions the returns. Technology says the system performs. Risk sees exposure. Operations sees dependencies. Each perspective may be valid. Someone still has to make the enterprise decision.
Enterprise AI Management provides executives with a practical framework for making consequential AI decisions across strategy, finance, technology, operations, risk and governance.
The book shows how to manage AI as an enterprise capability rather than as a collection of disconnected pilots, tools and projects. It explains how to identify the capabilities, systems, models, agents and dependencies that make up the AI estate, connect evidence across functions, distinguish expected from demonstrated and realized value, define ownership and decision rights, and respond when material conditions change.
The focus is not simply on where AI can be used. It is on the harder management questions: What should we fund? What can we authorize? Where is value real? What should we scale, constrain, change, reauthorize or stop?
A practical 90-day agenda helps translate the framework into action.
Written for executives accountable for enterprise-level AI decisions, and for AI and transformation leaders responsible for preparing, governing and operationalizing those decisions.
The central question is no longer: Where can we use AI?
It is: How much capital, authority, risk capacity and management attention should we commit, and what evidence should cause us to change that decision?