4.4 Retirement & Succession
All AI products eventually reach end of life—whether replaced by better solutions, rendered obsolete by changing requirements, or no longer cost-effective to maintain. The Cradle-to-Grave commitment includes managing this final phase with the same rigor as development and operations. Ethical retirement ensures clean transitions, proper data handling, and knowledge preservation for future pods.
Most organizations ignore retirement planning until forced into it by crises. The AI Innovation model treats retirement as a planned, managed phase—not an afterthought. Good endings enable good beginnings: clean retirement frees resources for new initiatives and preserves valuable learnings.
Retirement Triggers
When to Consider Retirement
Multiple signals may indicate an AI product is approaching end of life:
| Trigger Category | Indicators | Questions to Ask |
|---|---|---|
| Business Value | ROI declining, use cases eliminated, strategic direction change | Is this still worth the investment? |
| Technical Obsolescence | Better approaches available, technical debt unsustainable, infrastructure EOL | Can we achieve better results with modern approaches? |
| Performance Degradation | Accuracy decline despite retraining, fundamental drift | Can we restore acceptable performance? |
| Governance Risk | New regulations make use case problematic, unfixable bias issues | Can we operate this responsibly? |
| Replacement Available | New system built that supersedes this one | Is migration complete and successful? |
Retirement vs. Major Rewrite
Before deciding to retire, evaluate whether a major rewrite might be more appropriate:
Retire If...
- Business need has fundamentally changed
- Use case no longer exists
- Cannot be made compliant with new regulations
- Better external solution available
- Cost exceeds any plausible future value
Rebuild If...
- Core use case still valuable
- Problem is technical approach, not need
- Modern architecture could deliver better results
- Investment justified by continued value
- Knowledge from current system informs rebuild
Retirement Planning
Retirement Decision Process
Retirement Proposal
STO documents the case for retirement: triggers, impact assessment, stakeholder analysis, and proposed timeline. Includes alternatives considered.
Stakeholder Review
All affected stakeholders review the proposal. Business owners confirm the use case is no longer needed or will be addressed differently.
Governance Review
Ethics Liaison confirms all compliance obligations can be met through retirement. Identifies any regulatory notification requirements.
Approval
Appropriate approval based on risk tier. Tier 2+ typically requires AI Council sign-off on retirement plan.
Retirement Plan Components
- Timeline: Key dates for deprecation announcement, reduced support, and final shutdown
- User Migration: How will current users transition? Alternative solutions?
- Data Handling: What happens to training data, production data, and logs?
- Downstream Systems: Which systems depend on this? How will they be updated?
- Documentation: What must be preserved for audit/legal/learning purposes?
- Communication: Who must be notified, when, and how?
- Resource Reallocation: What happens to pod members and infrastructure?
Data Handling Requirements
Proper data handling is critical during retirement:
| Data Type | Typical Handling | Governance Requirements |
|---|---|---|
| Training Data | Archive per retention policy, or delete if no longer needed | Follow original consent terms; document destruction |
| Model Artifacts | Archive versioned copies for potential audit needs | Retain per regulatory requirements (often 5-7 years) |
| Production Logs | Archive per retention policy, then delete | Ensure PII handling complies with privacy policy |
| Decision Records | Archive for audit trail and dispute resolution | May need long retention for legal compliance |
| Documentation | Preserve Model Card and key documentation | Lessons learned should be accessible to future pods |
Succession Models
Direct Replacement
When a new AI product replaces the retiring one:
Parallel Operation
Run both systems simultaneously during transition. Validate new system matches or exceeds old system performance.
Traffic Migration
Gradually shift traffic to new system. Monitor carefully for issues. Maintain rollback capability.
Deprecation Period
Announce end-of-life for old system. Provide notice period for any remaining users. Reduce support level progressively.
Final Shutdown
Execute final shutdown checklist. Verify all users migrated. Complete data handling procedures.
Clean Retirement (No Replacement)
When the use case is ending entirely:
- User Communication: Provide clear notice that capability is ending
- Alternative Guidance: If external alternatives exist, guide users toward them
- Gradual Wind-Down: Reduce availability progressively to allow adjustment
- Support Tail: Maintain limited support for questions during transition
Pod Transition
What happens to the pod when their AI product retires:
Pod Rechartering
Pod takes on a new AI product charter, applying accumulated expertise to a new domain.
Pod Absorption
Members distributed to other pods that need additional capacity or specific expertise.
Platform Transition
Pod transitions to a shared services or platform role, supporting multiple pods.
New Formation
Members seed new pods, carrying institutional knowledge to new initiatives.
Lessons Learned
Post-Retirement Retrospective
Every retirement should include a formal retrospective:
- Value Delivered: What business value did this AI product deliver over its lifetime?
- Technical Learnings: What worked well technically? What would we do differently?
- Governance Learnings: What governance practices proved valuable? What was overhead?
- Team Learnings: What organizational practices worked? What didn't?
- Retirement Process: How well did the retirement itself go?
Knowledge Preservation
Critical learnings should be captured for future pods:
- Model Card Archive: Final Model Card preserved with retirement documentation
- Decision Log: Key decisions with rationale, especially architectural and ethical choices
- Failure Analysis: What didn't work and why—often more valuable than success stories
- Code and Architecture: Reference implementation archived for potential reuse
- Stakeholder Learnings: What did we learn about the business domain and users?
Final Closure
Complete the retirement with formal closure:
- All users migrated or notified of shutdown
- All infrastructure decommissioned
- All data handled per retention/destruction requirements
- Documentation archived appropriately
- Retrospective completed and shared
- AI Council notified of formal retirement
- Model Card marked as "RETIRED" with date
- Pod transition plan executed
The Full Circle
With retirement complete, the Cradle-to-Grave lifecycle has run its full course. The pod has carried their AI product from initial concept through production operations to dignified retirement. The knowledge they've gained now seeds future initiatives, creating an ever-improving AI capability across the organization. This is the compounding advantage of continuous ownership—wisdom accumulates rather than being lost at project boundaries.