The global public sector has been quick to embrace artificial intelligence (AI). From pilot projects in document analysis to large-scale deployments in service delivery, leaders have focused their energy on adoption, scaling and governance of active systems. Yet almost no one is addressing what happens at the end of an AI system’s life. AI decommissioning and succession planning, how to retire, replace or archive AI responsibly, remains a blind spot at international, federal, state and local levels. This oversight is more than a technical detail. It is a growing vulnerability with significant implications for compliance, risk management, and public trust.
Why This Matters Now
Policy momentum is building. The White House Office of Management and Budget’s latest Memorandums, M-25-21 and M-25-22, emphasize accelerating federal use of AI through innovation, governance and public trust, as well as driving more efficient acquisition practices. These directives require agencies to create and maintain AI use-case inventories and strengthen oversight mechanisms. While the focus is on adoption and responsible scaling, they also implicitly raise critical questions of end-of-life (EoL) procedures, how to document, retire or transition outdated models.
Standards emphasize lifecycle risk. NIST’s AI Risk Management Framework explicitly includes the decommissioning stage as part of AI’s lifecycle, highlighting the need for safe phase-out practices and transparency (NIST, 2023). Similarly, the AI RMF Playbook encourages organizations to treat decommissioning as part of governance, not as an afterthought.
Global compliance deadlines loom. The European Union’s AI Act, entering force between 2025 and 2027, includes documentation, monitoring, and accountability obligations that extend beyond deployment into replacement and retirement. For agencies touching EU citizens or data, compliance will require demonstrating control across the full lifecycle, including decommissioning.
Records management realities cannot be ignored. The National Archives and Records Administration (NARA) has raised concerns about how AI outputs intersect with retention schedules, Freedom of Information Act (FOIA) obligations, and legal discovery. Without clear decommissioning plans, agencies risk losing track of critical records when systems change or sunset. A set of AI use cases depict the challenges in these key areas.
In short, the question is no longer if governments need AI decommissioning strategies but when they will be held accountable for lacking them.
The Questions Leaders Aren’t Asking
Executives and boards often ask whether AI improves efficiency, compliance, or citizen experience. Far fewer ask:
- What is our audit trail? When models are swapped or updated, who documents the change, and what records of configuration, training data and prompts are preserved?
- How will we retire AI safely? If a vendor sunsets a model or contract ends, do we have procedures for export, archiving or rollback without losing continuity?
- Can we prove compliance end-to-end? Regulators, inspectors general, courts, or international partners may demand evidence that AI was governed throughout its lifecycle, not just while active.
These questions matter because in the absence of clear answers, risk compounds across technical, legal and reputational domains.
Executive Moves for the Next 90 Days
Senior leaders and their consulting partners can act now to close the gap. Four pragmatic steps stand out:
- Stand up “AI Change Control & EoL.” Create a formal checklist and governance process covering version diaries, rollback plans, model bills of materials, and successor system cutover procedures.
- Map records and FOIA obligations. Align AI logs, outputs, and training data lineage with retention schedules and legal hold requirements. Plan export formats and discoverability.
- Contract for exit. Embed decommissioning and transition clauses in AI procurements, requiring vendors to provide data export, model escrow, and continuity support.
- Check EU alignment. For agencies with international exposure, gap-assess practices against EU AI Act documentation and monitoring obligations.
These are not merely technical safeguards, they are credibility enablers. Leaders who can demonstrate control over AI’s full lifecycle will be better positioned to reassure boards, regulators, and the public.
Opportunity for Consulting Partners
Consulting firms are uniquely placed to design, implement, and operationalize these strategies. Emerging service lines might include:
- Developing an AI Decommissioning Runbook combining technical, records management and legal guidance.
- Offering AI Lifecycle Assurance Services to certify readiness across adoption, operation, and retirement.
- Building AI Use-Case Inventories and Change Ledgers aligned with OMB and NIST requirements.
In a crowded AI market, firms that help agencies manage not just adoption but responsible retirement will stand apart as trusted advisors.
Conclusion
The rush to deploy AI has dominated headlines and strategies. But resilience in government systems will depend just as much on how AI is retired as how it is adopted. Decommissioning is where compliance, continuity, and trust intersect. Leaders who embrace this reality now will not only avoid crisis later but also set a new standard for responsible innovation.
Dr. Rhonda Farrell is a transformation advisor with decades of experience driving impactful change and strategic growth for DoD, IC, Joint, and commercial agencies and organizations. She has a robust background in digital transformation, organizational development, and process improvement, offering a unique perspective that combines technical expertise with a deep understanding of business dynamics. As a strategy and innovation leader, she aligns with CIO, CTO, CDO, CISO, and Chief of Staff initiatives to identify strategic gaps, realign missions, and re-engineer organizations. Based in Baltimore and a proud US Marine Corps veteran, she brings a disciplined, resilient, and mission-focused approach to her work, enabling organizations to pivot and innovate successfully.
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