PMI CPMAI study plan

Map each of the four CPMAI domains to its exam weight and list the subtopics you personally find weakest

Define AI project management in your own words and distinguish it from traditional PM by listing three concrete differences

Identify the five most common AI risks in project delivery (data quality, model drift, bias, vendor lock-in, unclear ownership) and write a one-line mitigation for each

Build a personal glossary of 20 core terms (model, training data, inference, drift, bias, governance, etc.) with a definition and an example of how each appears in a project

Explain how AI tools (forecasting, resource optimization, risk scoring, automated status reporting) change the project manager's day-to-day decisions

Compare three AI use cases in project management and state the decision each one improves, the data each needs, and the failure mode of each

Draft a stakeholder communication plan for introducing an AI scheduling assistant to a delivery team, including concerns about job displacement

Practice five scenario questions on AI-assisted estimation and resource planning, and record why each wrong option is wrong

Sequence the phases of an AI project lifecycle (problem framing, data readiness, model development, evaluation, deployment, monitoring) and state the exit criteria for each phase

Write acceptance criteria for an AI model that cover accuracy, fairness, latency, and explainability, not just a single performance metric

Identify the delivery risks that appear when an AI project is run inside a traditional waterfall plan, and propose an iterative alternative

Build a RACI for an AI delivery team that names the data owner, model owner, product owner, and risk owner

Design a data governance structure that assigns ownership, stewardship, quality rules, and access controls for a project's training data

Classify ten project data assets by sensitivity and state the handling rule for each class

Draft an AI risk register with at least eight risks scored by likelihood and impact, each with an owner and a mitigation

Explain how model monitoring and drift detection feed back into the project plan and change control process

Apply a recognized AI risk framework to a sample project and map at least ten controls to specific lifecycle stages

Distinguish regulatory, reputational, operational, and ethical risk, and give one project example of each

Write an incident response outline for a model that produces biased or harmful outputs in production

Evaluate a vendor's AI solution against data protection, auditability, and model transparency requirements

Assess organizational readiness for AI adoption across data maturity, skills, culture, and leadership sponsorship

Build a change management plan that names sponsors, champions, training touchpoints, and adoption metrics

Develop an ethical AI checklist covering fairness, transparency, accountability, privacy, and human oversight, and apply it to a project scenario

Identify three sources of resistance to AI adoption and write a response to each

Integrate AI-specific governance into standard project management processes such as chartering, risk management, and stage gates

Draft a communication package that explains an AI project's benefits and limits to executives, users, and regulators

Audit your glossary and notes for gaps in domains 1 and 4, and close at least five gaps with focused reading

Write two full scenario responses under time pressure to practice judgment on adoption and ethics trade-offs

Complete a full-length timed practice exam and score each domain separately

Review every missed question and write the underlying principle, not just the correct answer

Rehearse the three highest-risk topics from your domain map until you can explain each in under two minutes

Finalize a one-page exam-day reference sheet of frameworks, definitions, and decision rules