A healthcare project manager is evaluating whether to implement an AI-powered diagnostic tool.

In PMI-CPMAI, realizing apositive ROIfrom AI is not just about an attractive business case at the start; it depends oncontinuous monitoring of value deliveryagainst clearly defined performance and outcome metrics. For a healthcare AI diagnostic tool with a specified ROI target (15% in the first year) and multiple stakeholders (administrators and clinicians), the project manager must ensure the tool is actually achieving the predicted improvements in practice.

The framework recommends definingkey performance indicators (KPIs)aligned to the value proposition—such as diagnostic accuracy for specific conditions, time-to-diagnosis, reduction in unnecessary tests, throughput, and impact on patient outcomes—and thenmonitoring the AI model’s performance against those KPIsover time. By tracking these metrics, the team can identify drifts, bottlenecks, or workflow issues and take corrective action (retraining, process changes, configuration updates) to protect and maximize ROI.

Seamless integration (option A) is important but is a means, not the main mechanism to ensure ROI is realized. Contingency solutions and verbal commitments do not directly drive financial outcomes. PMI-CPMAI’s value-focus makesongoing performance monitoring against KPIsthe most effective method to maximize and protect the expected ROI.