A company plans to operationalize an AI solution.

Before operationalizing an AI model, PMI-CPMAI emphasizes confirming whether the modelmeets predefined performance thresholdsusing well-governed evaluation datasets. This is done bytesting against validation (and/or test) datasetsthat are distinct from the training data and representative of real-world conditions. These datasets allow the team to compute agreed metrics—such as accuracy, precision, recall, F1, AUC, or domain-specific KPIs—and compare them directly against acceptance criteria defined earlier with stakeholders.

The PMI framework stresses traceability from business objectives → requirements → metrics → thresholds → evaluation results. Validation testing is where this chain is concretely confirmed: if the model consistently meets or exceeds thresholds on held-out data, it is a strong indicator that it is ready for controlled release. Impact evaluation (option B) is more appropriate once the model is in pilot or production, focusing on business outcomes. End-user acceptance tests (option C) mainly address usability and workflow fit, not detailed model performance. Penetration tests (option D) address security rather than predictive quality.

Thus, toconfirm that model performance meets selected thresholds before release, the most effective method istesting against validation datasets(option A).