Section Wireframe[2026-10-16 v1.7]

8. Operations phase: Model retirement

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Why should I do this?

All models eventually reach their end-of-life. Planning for retirement is a sign of a mature AI strategy.

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Target users

  • CTOs and technical leaders
  • Data scientists and ML engineers
  • Product managers
  • Research organizations, think tanks

Checklists in play

  • Checklist 1: Risk Identification & Mitigation (Q22, Q23, Q24, Q25)

Questions to consider

Checklist 1 · Q22

What mechanisms have been established to monitor for conditions that may trigger model retirement (declining performance, model drift)?

Checklist 1 · Q23

For retirement, how do you handle personal data (deleted, archived, or otherwise retained in accordance with the organization's data retention and disposal policies)?

Checklist 1 · Q24

Describe how system components will be decommissioned, with evidence that the process was completed securely

Checklist 1 · Q25

How is retirement notice communicated and a pathway for transitioning users to an alternative solution where necessary?

Investment types

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Every investment project is unique

The application of the six steps will vary accordingly. To provide examples that align with your project, common characteristics of AgDev investments were researched and three ‘investment types’ were developed.

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