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Trace dependencies: What evidence is needed before decommissioning under the organization’s risk-management

AIGP Understanding How to Govern AI Development Medium

Retirement requires dependency mapping, accountable fallback ownership, and tested continuity—not just accuracy comparison or archiving.

The question

A retailer plans to retire a demand-planning model after a newer forecast is approved. The old model still feeds replenishment thresholds and regional exception reports, and no fallback owner is identified. What evidence is needed before decommissioning under the organization’s risk-management policy?

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  1. Confirm the replacement model beats the old model.
    Higher forecast accuracy does not show which live workflows depend on the old model or how operations will continue during transition.
  2. Review current users, interfaces, limitations, and continuity risks.
    This is useful evidence, but without assigned fallback ownership and a tested transition it does not establish that retirement is safe.
  3. Trace dependencies, assign fallback ownership, and test continuity.
    Safe retirement requires identifying affected workflows, assigning responsibility for fallback or recovery, and testing that essential operations can continue before removal.
  4. Archive the old model, data, and configuration records.
    Archiving preserves artifacts but does not identify live interfaces, assign fallback responsibility, or demonstrate operational continuity.
The trap
Look beyond the model itself: identify downstream users, interfaces, responsibilities, and recovery arrangements.

How to remember it

Retirement requires dependency mapping, accountable fallback ownership, and tested continuity—not just accuracy comparison or archiving.

How many of these would you get right?

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