AI implementation in regulated industries: a production acceptance framework
By LockedIn Labs ·
Accept a workflow in its operating context.
An AI implementation includes the model, its sources, the interface, connected systems, human decisions, and the people who operate it. A useful acceptance record identifies that whole boundary. Specify the user, intended outcome, permitted actions, version under review, and person authorized to release it.
Choose a baseline that reflects the business: time to a reviewed answer, rework, unresolved exceptions, or another observable outcome. Evaluate usefulness alongside the consequences of error. A workflow that assembles a draft and one that changes an account need different evidence, even when they use the same model.
Agree on pass conditions before running the acceptance exercise. Each condition needs an owner, a test, an artifact, and a decision rule. A failed condition produces a bounded response: change the implementation, narrow the authority, or repeat the review with fresh evidence.
- 01
Scope
Outcome and boundary
- 02
Exercise
Behavior and evidence
- 03
Review
Accountable decision
- 04
Release
Approved scope
The review authorizes a specific version and scope. A material change returns to the affected acceptance conditions.
Give each production decision something to stand on.
Use these eight dimensions as a working review structure. Adapt the owners, thresholds, and test population to the workflow and its consequences. The exercises are illustrative acceptance tests to implement in an approved environment.
Scroll across the matrix to compare evidence and acceptance exercises.
| Dimension | Evidence to keep | Acceptance exercise |
|---|---|---|
| AuthorityBusiness and security owners | A workflow charter and permissions map identifying allowed reads, writes, decisions, and escalation owners. | Attempt an action outside the approved scope. Confirm rejection at the system boundary and preserve the event. |
| DataData and privacy owners | A source inventory, processing boundary, access rules, retention schedule, and versioned source references. | Test an unauthorized record, a stale source, and a deletion request across the agreed storage and retrieval path. |
| EvaluationDomain and evaluation leads | A reviewed test set, failure categories, agreed thresholds, version identifiers, and results by consequential case type. | Run normal, ambiguous, adversarial, and rare high-impact cases. Inspect the failures against the operating baseline. |
| IntegrationApplication owner | Interface contracts, transaction traces, retry rules, duplicate protection, and source-to-destination reconciliation. | Interrupt a transaction after the downstream write. Retry it and verify that the business action occurs once. |
| Rollback and recoveryRelease and operations owners | A release manifest, stop mechanism, restore procedure, and reconciliation plan for completed external actions. | Stop new work, recover the approved version, and account for queued and completed transactions. |
| Human approvalAccountable domain reviewer | Named reviewer roles, decision context, approval scope, expiration rules, and a preserved decision history. | Change the proposal after approval. Confirm that execution requires a fresh decision tied to the changed content. |
| OperationsService owner | Quality and service monitors, alert thresholds, incident procedures, support ownership, and capacity assumptions. | Simulate degraded retrieval or provider failure. Verify detection, escalation, and the agreed fallback workflow. |
| TransferReceiving engineering lead | Controlled repositories, configuration inventory, operating instructions, and a record of the receiving team's exercises. | Have the receiving team deploy a reviewed change, investigate a failed case, and recover using the handoff. |
Read failure categories before the average.
Separate source correctness, decision quality, execution behavior, and operator usefulness. Keep a held-out set of representative cases and record how it was selected. Include the people, languages, document conditions, and exceptions that matter to the workflow. Preserve the result by case category so a strong average cannot conceal a consequential failure.
For generative systems, record model and configuration versions and repeat cases where variation affects the decision. Test unsupported questions, conflicting instructions, attempted data access, and malicious content inside retrieved documents. The acceptance owner sets the relevant thresholds with domain reviewers and records both the observed errors and the evidence supporting release.
Follow a document review from source to human decision.
Consider a hypothetical healthcare operations assistant that assembles a prior-authorization review packet. Its approved task is to retrieve permitted records, organize supporting material, and produce a source-linked draft. A qualified reviewer retains the consequential decision. Begin the exercise with synthetic cases in a controlled environment.
Run a complete case, then remove a required document and introduce conflicting dates. Check that the draft exposes the discrepancy with its source references and routes the case to the right reviewer. Attempt to retrieve another member's record and verify that access is rejected before the content reaches the model.
Next, approve a draft and change its content. The earlier approval should cease to authorize execution. Interrupt the downstream write, retry the transaction, and reconcile the destination. Finally, stop the assistant and have the receiving team continue the queue through the agreed manual process.
The record joins the case, source versions, model configuration, proposed action, reviewer decision, and write result. Those artifacts make the exercise reconstructable. Authorized use of representative operational data and the scope of an operational trial are separate decisions for the organization's accountable reviewers.
Tie acceptance to a version and a change policy.
Assemble one release record containing the charter, evidence links, test results, outstanding conditions, approvals, and operating owners. State exactly what the decision permits. A supervised trial, a limited production release, and broader operational use carry distinct scopes.
A software rollback restores code and configuration; completed external actions require their own reconciliation or compensating process. Rehearse both. Name the triggers that reopen review: a model change, new source, broader permission, altered reviewer workflow, or a material shift in observed performance. Carry that decision policy into the receiving team's runbook.
Connect the worksheet to an established risk discipline.
The NIST AI Risk Management Framework 1.0 organizes voluntary risk management through Govern, Map, Measure, and Manage. It provides a useful structure for assigning responsibility, understanding context, assessing behavior, and responding to risk throughout the system lifecycle.
The NIST Generative Artificial Intelligence Profile, AI 600-1, adds suggested actions around generative AI, including governance, content provenance, testing before deployment, and incident disclosure. These are engineering references for this guide. The acceptance matrix is LockedIn Labs' practical synthesis; organizational reviewers determine the requirements and evidence appropriate to their use case.
Run the review with your team.
The CSV worksheet carries the eight dimensions, suggested evidence, and acceptance exercises from this guide. Add the workflow and version, replace suggested owners with named people, set thresholds before testing, and record results and evidence links. Use an explicit decision and reopening trigger for every row.
Keep sensitive evidence in your approved repository and use references in the worksheet. Review the completed record with the business, engineering, domain, security, and operations owners responsible for the release.
Download the acceptance worksheetCSV · eight review dimensions · editable in Excel or Google Sheets