This course prepares professionals to plan, coordinate, and manage AI initiatives from business concept through operational deployment. It covers business alignment, data readiness, model development, model evaluation, and ongoing operations, with responsible AI considerations integrated throughout.
The proposed outline combines AI project management fundamentals with practical planning activities and a capstone. It supports preparation for PMI-CPMAI certification and collaboration across business, project, and technical teams.
Duration 3 Days – 21 hrs.
Objectives
- Translate business problems into clearly defined AI project opportunities.
- Establish project scope, intended benefits, and measurable success criteria.
- Identify data requirements and readiness gaps.
- Coordinate data preparation and model development activities.
- Interpret model results in relation to business requirements.
- Incorporate privacy, fairness, transparency, and accountability into project decisions.
- Plan solution deployment, adoption, monitoring, and improvement.
- Develop an integrated AI project plan.
Target Audience
- Project and program managers.
- AI product owners and product managers.
- Business analysts and digital transformation professionals.
- Data science, analytics, and engineering team leaders.
- IT managers and solution architects.
- Consultants and business leaders responsible for AI initiatives.
- Professionals preparing for PMI-CPMAI certification.
Prerequisites
- No prior AI project experience is required.
- Basic familiarity with business processes and project terminology is helpful.
- Programming and model-building skills are not required for this proposed course.
- For certification, candidates must complete PMI’s required PMI-CPMAI Exam Prep Course before scheduling the examination; this general outline does not independently establish eligibility.
Course Outline
Day 1: AI Foundations and Business Alignment
Module 1: Foundations of Managing AI Projects
- AI, machine learning, deep learning, and generative AI concepts.
- Differences between AI initiatives and conventional software projects.
- AI project lifecycle, team responsibilities, and stakeholder collaboration.
- Common causes of project difficulty.
Module 2: Business Understanding and Project Definition
- Business problems, user needs, and suitable AI opportunities.
- Feasibility, organizational readiness, and solution alternatives.
- Scope, expected value, costs, and success criteria.
- Assumptions, dependencies, and project risks.
Module 3: Responsible AI and Governance
- Privacy, security, fairness, and transparency.
- Human oversight and decision accountability.
- Governance responsibilities and approval points.
- Documentation and traceability.
Day 2: Data Readiness and Model Development
Module 4: Data Understanding
- Data sources, access, ownership, and suitability.
- Data quality, completeness, and representation.
- Data limitations and project implications.
Module 5: Data Preparation
- Cleaning, transformation, integration, and labeling.
- Training, validation, and test datasets.
- Data leakage prevention and reproducibility.
- Coordination of data preparation deliverables.
Module 6: Managing Model Development
- Selecting appropriate AI approaches.
- Coordinating experimentation and model refinement.
- Resource requirements and technical trade-offs.
- Tracking versions, decisions, and dependencies.
Day 3: Model Evaluation and Operational Delivery
Module 7: Model Evaluation and Business Acceptance
- Technical results and business success criteria.
- Reliability, explainability, and fairness considerations.
- Error analysis and improvement priorities.
- Deployment readiness decisions.
Module 8: Operationalizing AI Solutions
- Deployment planning and system integration.
- User adoption and operational ownership.
- Performance monitoring, drift, and retraining.
- Maintenance, incident response, and retirement planning.
Module 9: Capstone — Integrated AI Project Plan
- Select a business use case and define intended outcomes.
- Develop scope, stakeholder responsibilities, and success measures.
- Document data needs and preparation requirements.
- Outline model development and acceptance criteria.
- Prepare governance, deployment, and monitoring plans.
- Consolidate the deliverables into an AI project proposal.

