Retiring an AI feature is an engineering change with inputs, outputs and people who still depend on the old behavior. Turning off a model call may leave scheduled jobs, cached drafts, bookmarked links or downstream assumptions behind. A useful retirement plan makes those dependencies visible before the application starts returning confusing results.
This guide proposes an original worksheet for a fictional policy-summary feature that a team has decided to replace with a simpler document view. It is not an account of a real deployment or a legal retention policy. The aim is to preserve understandable application behavior and decision history while removing a capability deliberately.
Write the retirement decision first
Record which feature is ending, why the team selected retirement and what users should do afterward. Separate the decision from an unsupported claim that all AI features of that kind are unsuitable. Your application may have specific quality, maintenance or workflow reasons that do not generalize to every system.
Name an accountable owner and a review date. Describe the replacement experience in plain language. In the fictional example, users will open the approved policy document directly, while existing summary drafts remain identifiable as historical drafts rather than current policy statements.
Keep unresolved questions in the decision note. If the team has not decided what happens to a scheduled job or a saved draft, do not hide that uncertainty under a completion checkbox. The plan should make the missing decision easier to assign and resolve.
Inventory the feature's entry points
List the screens, API routes, scheduled jobs and internal integrations that can start the feature. Include links in documentation and onboarding material. A feature can appear disabled in the main interface while another path continues to call its service.
For each entry point, record the identity it uses, the data it reads and any artifact it writes. Distinguish new work from access to historical results. The team may choose to stop new summaries while preserving an authorized read path for existing records.
Use a fixture environment to inspect the inventory. Create a saved draft, an unfinished job and an old link. These concrete examples help reveal dependencies that a list of source files can miss. Do not infer that production has no remaining consumers merely because a search found no obvious interface button.
Define the transition states
Choose explicit states such as active, new work disabled, historical read-only and removed. Explain which actions are permitted in each state. A user-facing message should identify the current state and the available next step, rather than returning an empty response that looks like a successful analysis.
In the fictional example, new requests can receive a message directing users to the document view. Existing drafts can display their original creation date and a notice that they are not refreshed. Unfinished jobs can be canceled with a recorded reason instead of disappearing from the operator's list.
Keep state changes reviewable. An operator should be able to identify who approved the transition and which configuration became active. A model-generated explanation is not the approval event and should not replace the application record of that decision.
Preserve the right evidence
Identify the artifacts needed to explain previous outputs: source identifiers, prompt or configuration versions, review events and the final draft status. Retain only what the organization's actual access and retention requirements permit. This guide does not prescribe a universal period for storing private material.
Distinguish preserving a decision record from preserving every raw input forever. A narrow audit record may be appropriate where a complete document copy is unnecessary. Conversely, a record that contains only an output without its source version may not support the investigation the team expects to perform later.
Keep historical outputs clearly labeled. A summary produced from an earlier policy version should not be presented as a current policy simply because its URL still works. The replacement experience should make the document version and the status of the old summary understandable.
Drain or cancel outstanding work
Decide what happens to jobs already admitted before the cutoff. Some may be allowed to finish under the old configuration; others may need cancellation. Record the rule and test both cases. A vague instruction to disable the feature does not establish how an in-flight request should behave.
Prevent the scheduler from creating new jobs once new work is disabled. Check manual triggers and retry queues as separate entry points. A failure-retry mechanism can otherwise restart work that the team believed had been stopped.
Make uncertain remote outcomes visible. If a request timed out before retirement, the application may not know whether an external operation completed. Investigate its verified state before repeating or reversing anything. Keep that uncertainty distinct from an operation that was never dispatched.
Review credentials and stored connections
Inventory credentials and service connections used only by the retiring feature. Plan revocation through the appropriate configuration or account process after dependent work is resolved. Do not delete a shared credential merely because one consumer no longer needs it.
Record the difference between removing a secret reference from application configuration and revoking the credential itself. Verify the intended result using the service's supported process. Avoid printing keys in migration notes, issue comments or diagnostic output.
If a connection remains for historical reads, document its limited purpose and owner. An unused integration without an accountable owner can become harder to review over time. The retirement note should explain which connections remain intentionally and which should no longer be available.
Test the user and operator experience
Use fixtures covering an old bookmark, a new request, an unfinished job and a historical draft. Check that each produces the expected message and that denied access remains denied. Retiring the generator should not accidentally make historical results public.
Review documentation and support instructions together with the application change. A user should not be directed from a disabled screen to a guide that still promises the removed behavior. The operator should have a clear way to identify remaining consumers and unresolved jobs.
Keep a rollback decision separate from a rollback command. If the team might restore the feature temporarily, record the conditions, approver and known configuration needed. A reversal should not silently resume every old schedule or grant permissions that were intentionally removed.
Close the retirement with an evidence note
After the transition, review the inventory and record which checks passed, which artifacts remain and who owns the historical read path. Mark incomplete items honestly. Removing the main code path does not prove that every external consumer has stopped depending on it.
NIST's AI Risk Management Framework is a broader voluntary reference for AI risk management; the retirement worksheet here is original engineering guidance, not a quoted NIST requirement. Use the workflow planning guide to define the replacement task and the evaluation worksheet to make the transition checks inspectable.