AI Project Management (AIPM) Course

Lesson 1 • Key Roles in an AI Project Team

Maps data scientists, ML engineers, data engineers, and AI ethicists to PM coordination needs. Clarifies accountability boundaries across the team.

Lesson 2 • AI Maturity and Organizational Readiness

Introduces maturity models to assess an organization's data, talent, and infrastructure readiness. Guides PMs in scoping realistic AI initiatives.

Lesson 3 • What Makes AI Projects Unique

Contrasts AI projects with traditional software projects on uncertainty, data dependency, and iterative delivery. Sets the conceptual baseline for the entire course.

Lesson 4 • Core AI Concepts for Project Managers

Covers machine learning, deep learning, NLP, and computer vision at a conceptual level. Equips PMs to communicate accurately with technical teams.

Lesson 1 • The AI Project Lifecycle Overview

Traces stages from problem framing through model deployment and monitoring. Provides the structural backbone for all subsequent planning activities.

Lesson 2 • CRISP-DM and Data Science Frameworks

Explains CRISP-DM phases and their PM implications for scheduling and handoffs. Connects data science workflows to project governance checkpoints.

Lesson 3 • Agile and Scrum Adapted for AI

Adapts sprint planning, backlog management, and retrospectives to AI's experimental nature. Addresses how to define done when model performance is the deliverable.

Lesson 4 • Selecting the Right Framework

Provides a decision matrix for matching delivery frameworks to project type, risk, and team maturity. Reinforces framework selection as a scoping decision.

Lesson 1 • Defining AI Success Metrics

Distinguishes model performance metrics from business outcome metrics and links them. Ensures PMs can negotiate meaningful acceptance criteria with technical teams.

Lesson 2 • Feasibility Assessment for AI Projects

Evaluates technical, data, and business feasibility before committing resources. Introduces the build-vs-buy-vs-partner decision for AI capabilities.

Lesson 3 • Data Scoping and Requirements

Defines data requirements as a first-class project artifact alongside functional requirements. Covers data sourcing, labeling, and volume estimation.

Lesson 4 • Problem Framing and Opportunity Identification

Structures techniques for converting vague business needs into precise AI problem statements. Prevents scope creep by anchoring the project to a testable hypothesis.

Lesson 1 • Resource Planning for AI Teams

Maps specialized AI roles to project phases and identifies resource bottlenecks. Covers compute resource planning alongside human resource planning.

Lesson 2 • Scheduling AI Experiments and Iterations

Applies time-boxing and iteration planning to manage open-ended experimentation. Introduces decision gates to stop or pivot experiments on schedule.

Lesson 3 • AI Project Cost Estimation

Breaks down AI cost drivers: data, compute, talent, and tooling. Produces a cost model that accounts for iterative rework and cloud infrastructure.

Lesson 4 • Work Breakdown Structure for AI

Adapts WBS to capture data, modeling, evaluation, and deployment work packages. Ensures all AI-specific tasks are visible in the project plan.

Lesson 1 • Data Quality Management

Applies quality management principles to data completeness, accuracy, and consistency. Introduces data profiling and remediation as PM-tracked activities.

Lesson 2 • Data Privacy and Compliance Oversight

Covers privacy-by-design principles and consent management as PM governance responsibilities. Ensures PMs can identify compliance risks before they become project blockers.

Lesson 3 • Data Governance Frameworks

Introduces enterprise data governance structures and their intersection with AI project delivery. Helps PMs navigate data access approvals and policy enforcement.

Lesson 4 • Data as a Project Asset

Reframes data as a managed deliverable with quality, lineage, and ownership attributes. Establishes PM accountability for data readiness milestones.

Lesson 1 • Risk Mitigation Strategies for AI

Applies avoidance, mitigation, transfer, and acceptance strategies to AI-specific risks. Introduces model cards and datasheets as risk communication tools.

Lesson 2 • AI-Specific Risk Categories

Catalogs technical, data, ethical, and operational risks distinct to AI projects. Extends the standard risk register to capture AI failure modes.

Lesson 3 • Regulatory and Compliance Risk

Maps AI regulatory frameworks by domain and identifies compliance checkpoints in the project lifecycle. Prepares PMs to engage legal and compliance stakeholders proactively.

Lesson 4 • Bias, Fairness, and Ethical Risk

Defines algorithmic bias sources and fairness metrics PMs must track as acceptance criteria. Connects ethical risk to reputational and regulatory project consequences.

Lesson 1 • Communicating AI Concepts to Executives

Translates model performance, uncertainty, and risk into executive-level business language. Covers dashboard design and narrative framing for non-technical sponsors.

Lesson 2 • AI Stakeholder Landscape

Maps stakeholder types unique to AI projects including regulators, data owners, and model users. Identifies influence and interest dynamics that shape project decisions.

Lesson 3 • AI Project Reporting and Status Updates

Designs status reporting templates that capture experiment progress, model health, and risk status. Ensures reporting cadence matches AI project uncertainty levels.

Lesson 4 • Managing Technical Team Dynamics

Addresses collaboration friction between data scientists, engineers, and business analysts. Equips PMs to facilitate cross-functional alignment without micromanaging experts.

Lesson 1 • Model Monitoring and Observability

Defines monitoring metrics for data drift, model drift, and system performance in production. Establishes alert thresholds and escalation paths as PM governance artifacts.

Lesson 2 • Continuous Improvement in AI Projects

Applies retrospective and kaizen practices to AI delivery teams for ongoing performance gains. Links improvement cycles to business value realization tracking.

Lesson 3 • Incident Management for AI Systems

Adapts IT incident management to AI-specific failures such as biased outputs and prediction errors. Defines severity levels and response protocols for model incidents.

Lesson 4 • Model Retraining and Lifecycle Governance

Establishes triggers, approval workflows, and version control for model retraining cycles. Ensures PMs govern model updates with the same rigor as initial deployments.