How do you measure AI strategy success?

Outcome metrics vs vanity metrics for AI strategy

Outcome metrics connect AI activity to business results. If the AI strategy identified time savings in document processing as a priority, the metric is actual time saved — measured before and after deployment. If the strategy targeted cost reduction in a specific workflow, the metric is cost reduction in that workflow. If the strategy was designed to improve the quality of client reporting, the metric is a quality measure — error rate, revision rate, client satisfaction. Outcome metrics require baseline measurement before AI deployment, which is another reason to develop strategy before deploying tools. Vanity metrics — the number of employees using AI, the number of AI tools in the tech stack, the number of AI projects initiated — are reported by organisations that have not defined what success looks like. They measure activity, not result.

Why governance metrics matter as much as productivity metrics

A complete AI strategy measurement framework includes governance metrics alongside productivity metrics. Governance metrics include: Is an AI use policy in place and up to date? Has the AI inventory been completed and maintained? Is the AI risk register current? Have board members received AI governance briefings? Is EU AI Act compliance on track? These metrics matter because governance failures have consequences that productivity gains cannot offset. An organisation that achieves a 15% productivity improvement from AI but exposes client data in the process, or deploys high-risk AI without a conformity assessment, has not achieved strategic success — it has traded one problem for a larger one. Measuring governance progress is as important as measuring productivity progress.