An AI project team has prepared the data and is ready to proceed with model...

Once data preparation is complete and the team is ready for model development, PMI-aligned AI lifecycle guidance calls forclear definition and documentation of performance metrics and success criteria before training models. The project manager should ensure that everyone agrees onwhich metrics will be used (e.g., accuracy, precision, recall, F1, AUC, business KPIs)and what thresholds will be considered acceptable. This supports traceability, objective evaluation, and transparent go/no-go decisions in later stages.

Because the question states that the data is already prepared and the team is ready to proceed, it implies that initial data quality activities have already occurred. Repeating a “final assessment of data quality” (option A) is less critical at this specific point than locking inevaluation metrics. Go/no-go questions (option C) and scalability reporting (option D) depend on having those metrics explicitly defined; they are downstream decisions and artifacts. PMI-style AI guidance stresses thatmodel development should be driven by pre-defined, documented performance metrics that connect technical outputs to business value and risk tolerances. Therefore, the next action for the project manager is todocument the performance metrics for the model.