The PMI Cognitive Project Management for AI (CPMAI) methodology is a six-phase framework designed specifically for managing AI and machine learning (ML) projects.

Phase 1: Business understanding
Define the problem. Clarify goals. Determine whether AI is the right solution.

This phase ensures alignment between business needs and technical direction, starting with identifying the most relevant pattern of AI for the problem you’re solving. It sets expectations early and prevents wasted effort.

Phase 2: Data understanding
Explore the data landscape: What data exists? Is it accessible? Does it reflect the real-world context of the problem? Strengthening data governance practices early can help avoid surprises downstream.

This phase helps teams identify data limitations early and make adjustments as needed.

Phase 3: Data preparation
Prepare the data for modeling. This includes cleaning, labeling, standardizing, and deduping as needed.

Data quality has a direct impact on model performance. Remember: garbage in is garbage out. Investing time here reduces downstream risk.

Phase 4: Model development
Select the appropriate model. Train and tune the model using the data you’ve just prepped.

The model development phase should be driven by measurable goals established in earlier phases, not just experimentation for its own sake.

Phase 5: Model evaluation
Evaluate the model against business and technical criteria. Does it perform reliably? Does it align with project goals?

This is the phase where AI quality assurance plays a key role by evaluating performance, fairness, and alignment with a trustworthy AI framework.

Phase 6: Model operationalization
This is where you put the model into the real world. Integrate it into business workflows. Establish monitoring and feedback loops.

This final phase ensures that AI delivers real value and that your AI solution can adapt as data, priorities, or conditions change. It also demands leadership across disciplines, alignment between technical and business stakeholders, and confidence in change management.