3.2. Measuring and Demonstrating AI Business Value
💡 First Principle: Value that is not measured against a baseline is an opinion, and opinions do not survive budget reviews. Demonstrating AI value requires deciding what to measure before you change anything, capturing the starting point, counting all costs, and tracking early signals that predict whether value will arrive. Measurement is designed in at the start, not reconstructed at the end.
Why care? AI initiatives that cannot prove value lose funding, even when they are working. And initiatives that report inflated value (counting time "saved" that is never redeployed, ignoring operating costs) erode trust in every future AI proposal. Credible measurement protects good initiatives and exposes weak ones early.
The mental model is a clinical trial. You define the outcome before treatment, measure the patient's condition before treatment begins, track early indicators during treatment, compare against a control where possible, and count side effects (costs) honestly. Without the baseline, you cannot tell whether the patient improved because of the treatment or because of time.
⚠️ Common Misconception: "You can measure impact after launch without a baseline," "intangible benefits cannot be measured, so leave them out," "model usage cost is the total cost," and "ROI is the best early signal." Each produces a business case that is either indefensible, understated, overstated, or too late to act on.
