Curso de AI Project Governance Framework Foundation

Introduction to AI Project Governance

• What AI governance means in project and programme environments
• Why governance matters when AI is used in delivery, reporting, planning, or decision support
• How the AI Project Governance Framework fits with existing project governance practices

Framework Principles and Structure

• Core principles of responsible and practical AI governance
• Human accountability, oversight, and clear ownership
• Using proportional governance based on risk, scale, and project context

AI Across the Project Lifecycle

• Common points where AI may be used from initiation through closure
• Governance checkpoints, reviews, and approvals at each stage
• Recording assumptions, decisions, and exceptions

Roles, Responsibilities, and Decision Making

• Key roles for project teams, sponsors, PMO, risk, compliance, and business stakeholders
• Decision rights, escalation paths, and accountability for AI use
• Working effectively across delivery and oversight functions

Risks, Ethics, and Compliance Considerations

• Common AI-related risks in project work
• Bias, transparency, privacy, security, and reliability concerns
• Aligning AI use with internal policy and relevant legal or regulatory expectations

Controls, Assurance, and Monitoring

• Setting practical controls for approval, use, review, and change
• Verifying outputs, maintaining records, and supporting auditability
• Monitoring AI use and responding to issues, exceptions, or incidents

Applying the Framework in Practice

• Reviewing a realistic AI-enabled project scenario
• Identifying governance actions, roles, risks, and evidence needed
• Creating a simple governance checklist for day-to-day project use

Review and Next Steps

• Recap of key concepts, terminology, and governance practices
• Short knowledge check or sample foundation-style questions
• Planning practical next steps for workplace adoption