PMI-CPMAI consistently stresses that AI initiatives must be evaluated not just on technical metrics but onbusiness value and outcomes. To ensure the machine learning model provides theexpected business benefits, the project manager must verify that model performance isdirectly aligned with key performance indicators (KPIs)that were defined with stakeholders earlier in the project.
Within the PMI-CPMAI structure, KPIs link the problem statement and objectives (e.g., cost reduction, increased revenue, fewer failures, faster processing) to measurable AI outputs. This means: selecting the right performance metrics, setting thresholds, and confirming that improvements in those metrics correlate with real-world business gains. For example, in a financial, operational, or customer-focused AI system, the model’s precision, recall, or uplift must translate into concrete improvements such as reduced churn, fewer false alerts, more accurate predictions, or improved customer satisfaction.
Maximizing interpretability (A), minimizing human intervention (C), or increasing training data volume (D) may be beneficial in some contexts, but they aremeans, not ends. PMI-CPMAI guidance is clear that decision-makers care primarily about whether the AI solution advances strategic objectives and measurable KPIs. Therefore, the critical factor the project manager should assess is thealignment of the AI solution’s performance with key performance indicators (KPIs).
