AI Decommission Plan

Purpose

An AI Decommission Plan governs the controlled retirement of an AI system — ensuring that model artifacts are disposed of appropriately, data obligations are met, dependent systems are transitioned, affected stakeholders are notified, and the regulatory record is formally closed. It is the governance bookend to the Project Charter: the document that confirms the system has been retired responsibly, not just switched off.

Decommissioning an AI system is not the same as decommissioning a traditional software system. AI systems leave behind model artifacts (weights, training pipelines, evaluation datasets) that may carry regulatory, privacy, and intellectual property obligations long after the system stops running. Training data used under license or consent agreements may need to be deleted. Audit logs may need to be retained for regulatory lookback periods even after the model is gone. This template addresses all of it.

This template includes two variants adapted to the team's delivery methodology. Both cover the same decommission requirements — the difference is in how execution is structured: a formal phase-gate process vs. a final sprint-based wind-down.

Two Variants — One Template

Where It Fits in Your Document Pack

The Decommission Plan closes the AI governance lifecycle. When complete and signed off, it provides the organization with a documented record that the system was retired in accordance with its governance obligations — traceability back to the original charter, and forward to the archived governance package.

What This Template Covers

• Plan identification: system name, decommission date, plan author, retirement rationale, methodology selection, and linked governance documents
• System overview at decommission: deployment duration, current status, peak usage, and a summary of governance posture at retirement — what worked, what open risks existed
• Dependent systems and integrations: identification of all systems that consume or depend on the AI system, with transition/migration plan for each
• Stakeholder notification plan: who must be notified, through what channel, by what date, and what confirmation of notification is required
• Data disposition: training data, inference logs, user data, and audit records — with specific retention periods, deletion methods, and accountability for each data category
• Model artifact disposal: model weights, checkpoints, training pipelines, evaluation datasets, and API keys — with disposition decision (delete, archive, transfer) and completion verification for each
• Execution plan with methodology variant: plan-driven phase-gate approach (pre-decommission gate, controlled shutdown, post-decommission sign-off) or agile sunset sprint (sprint goal, exit criteria, lessons learned retro)
• Post-decommission verification checklist: confirms all systems are offline, all data obligations met, all artifacts disposed of, all notifications sent, and regulatory record closed
• Governance archive: where the complete governance package (charter through decommission plan) is stored for regulatory lookback
• Final sign-off with three-tier approval and revision history

Framework Alignment

• EU AI Act Art. 16(f) & Annex IV — Technical documentation obligations include records of the AI system lifecycle, including decommissioning, for high-risk AI systems
• NIST AI RMF — GOVERN 6.1 — AI system retirement policies and procedures, including data disposition and model artifact management
• ISO/IEC 42001 Clause 8.6 — AI system lifecycle management including planned decommissioning with documented controls
• GDPR Art. 17 (Right to Erasure) — Data disposition obligations that survive system retirement, including deletion of personal data used in training
• SR 11-7 (Federal Reserve / OCC) — Model retirement documentation requirements for regulated financial institutions

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