How should CPMAI teams use retrospectives to improve AI projects?

• A. Inspect both process and technical outcomes, identify actions, and ensure follow-through for model improvement.
• B. Focus only on project timelines.
• C. Rely solely on automated metrics without human reflection.
• D. Conduct retrospective only after model retirement.

Answer: (A)

The main idea is to treat retrospectives as a learning loop that covers both how the project is run and how the AI product behaves. In CPMAI, you want to look at process outcomes (things like data handling, labeling accuracy, experimentation, deployment, monitoring, governance) alongside technical outcomes (model performance, robustness, bias, data quality, reproducibility). By examining these areas together, the team can surface concrete actions aimed at real improvements rather than just noting what happened. Then it’s crucial to assign owners and deadlines and actually implement the changes, then recheck their impact in the next cycle. This creates a steady path of model improvement and better project practices over time.

It’s better than focusing only on timelines, which misses valuable learning about how data, models, and workflows are performing. It’s also superior to relying only on automated metrics, which can overlook context, root causes, and practical implications that humans reason through. Waiting until model retirement to reflect misses opportunities to fix issues and learn for the next iteration.