Measurement on AI Strategy Paper

D1. Metrics and Evaluation

Mon, 01 Jan 0001 00:00:00 +0000

AI succeeds only if its impact is measurable. Without traceable metrics, it stays unclear whether solutions meet their goals, create risk, or justify investment.

What this chapter delivers: the full metrics system for all levels, from technical performance through to business contribution: four metric categories, specific metrics for generative AI and agents, thresholds, and evaluation methods. The starting point is the set of strategic metrics that executive management defined in chapter B1.

D2. Cost and FinOps for AI

Mon, 01 Jan 0001 00:00:00 +0000

AI incurs costs in many places at once, from data preparation through training and operations to personnel. What sets it apart from classic software is inference, the productive model operation: every single request costs compute, so ongoing costs grow with usage. A FinOps approach applies proven cloud cost-management principles to AI: make spending visible, assign ownership, continuously reconcile cost against business benefit.

What this chapter delivers: ongoing cost control for the budget frame from chapter B1. First it introduces the eight cost fields in which AI costs arise. Two templates build on them: the cost model per use case and the unit-economics worksheet for the profitability of a single request. After that come the cost levers for reducing costs and the FinOps practice that keeps costs under control for good.

D3. Risk Management

Mon, 01 Jan 0001 00:00:00 +0000

AI brings risks that can determine success or failure. The opportunity side is assessed at the strategy level in chapter B1; here the risk side is managed.

What this chapter delivers: the tool to identify, assess, treat, and monitor AI risks. These four steps form the cycle in the figure, and the four numbered sections of this chapter are exactly those steps. The core artifact is the risk register, which carries each individual risk entry through all four steps.

D4. Reporting and Stakeholder Communication

Mon, 01 Jan 0001 00:00:00 +0000

Metrics and evaluation (chapter D1) supply the raw data; reporting connects them to strategic steering and makes them usable for different audiences, from the operational team huddle to the oversight body. Without structured reporting, even good metrics remain ineffective because they never reach the people who decide on their basis.

What this chapter delivers: an audience overview that assigns each group its reporting instrument, and then those instruments themselves: three dashboards for internal steering, fixed reporting cadences, the template for the board report, and the transparency evidence for external stakeholders.