AIPGF Foundation Life-Cycle Governance Stages and Deliverables

Life-cycle governance means applying the right governance work at the right stage. AIPGF Foundation does not treat AI governance as a single approval moment. It expects governance aims, activities, and evidence to evolve from early framing through delivery and ongoing use.

What to understand

Although different organizations may describe stages differently, the governance logic is usually consistent:

• early stages clarify why AI is being considered and what constraints already apply
• design or planning stages define roles, controls, boundaries, and review expectations
• delivery stages check that actual use stays inside those controls
• later stages review evidence, issues, lessons, and improvement actions

Deliverables matter because they make governance visible. If no evidence, records, approvals, or review outputs exist, strong governance is difficult to prove.

Example

A team receives approval in principle to use AI on a project, but no checkpoints are defined for later stages. By the time live use begins, no one can show what data was approved, what review standard applies, or what lessons should be captured. The problem is not only missing paperwork. It is missing life-cycle governance.

Common pitfalls

• Treating governance as complete once initial approval is given.
• Waiting until project closure to review AI use.
• Confusing stage activity with stage evidence: both matter.