How do we measure the success of an AI strategy?

The success of an AI strategy is measured against clearly defined business goals. The key question is whether economic impact is being created: are revenue, margin, or productivity increasing? Are costs, lead times, or risks being measurably reduced?

Operational metrics are also necessary. Are processing times shortening? Is forecast accuracy improving? Is AI actually being used?

Sequence matters: goals and KPIs are defined before the project starts, including a baseline. Only then can impact be cleanly assessed and a realistic ROI calculated.

Success also shows at the portfolio level. Individual use cases must work, but the overall strategy must raise the company's performance level.

In short: an AI strategy is successful when it measurably produces economic impact and builds structural capability.