How will we know an AI-enabled workflow is ready?

Executive answer

An AI-enabled workflow is ready for a defined use when evidence supports its allowed actions, its checking mechanisms detect the failures that matter, and someone owns the remaining uncertainty. Readiness applies to a specific scope, version, and operating environment. It should be revisited when those conditions change.

Start with the consequence of being wrong. Then ask whether the system can check its output against trustworthy evidence before acting. Human review of every result may be appropriate during discovery, but it can conceal a design that never becomes economically or operationally useful. The goal is a justified division of work: dependable automated checks for bounded questions, clear escalation for exceptions, and human judgment where the criteria cannot be reduced to reliable tests.

Specify the claim you are testing

“Accurate answers” is too vague to accept. Write an acceptance statement that identifies inputs, output, authority, evidence, and failure behavior. For example: “For an authorized internal user, identify the order status from the current order record, cite that record, and withhold the answer when records conflict.” This excludes predicting arrival, changing orders, and contacting customers.

Break this statement into observable checks. Can the system retrieve the correct order? Is the retrieved record within the user's access? Does the reported status equal the source value? Is the timestamp acceptable? Does the system stop when the source is unavailable? A single fluent-answer score would obscure these separate responsibilities.

OpenAI's evaluation guidance recommends task-specific tests, automated scoring where suitable, and calibration against human feedback. It also treats evaluation as a continuing process. These are useful implementation practices, not ready-made business acceptance thresholds. Official evaluation guidance

Build three kinds of checks

Deterministic checks compare outputs with explicit rules or trusted records. Examples include arithmetic reconciliation, exact identifier matching, permitted-action checks, and timestamp validation. They can be strong evidence for the property they test. A matching identifier alone does not establish that the source record is accurate.

Model-based checks assess properties such as whether a summary omits an important qualification. Define a narrow rubric, compare grader decisions with independently labeled cases, and measure missed errors. Agreement between two models is not independent ground truth. Test the grader with deliberately plausible but wrong outputs.

Human judgment resolves matters such as ambiguous intent, competing commercial priorities, and acceptable residual risk. Give reviewers the relevant evidence and decision authority. If every routine case needs judgment, revisit the proposed operating model and business case before expanding it.

Worked example: hypothetical order-status assistance

Suppose an internal workflow is evaluated on 200 held-out cases: 120 routine requests, 30 ambiguous identifiers, 20 stale records, 20 permission-boundary cases, and 10 source outages. These numbers are illustrative and are not a recommended sample size for every deployment.

In a hypothetical test, 118 of 120 routine answers match the authoritative status. All 30 ambiguous requests escalate, but 4 stale-record cases produce an answer. All permission-boundary cases are blocked, and all outages return the fallback route.

The routine accuracy is 98.3%, but that headline does not settle readiness. The four stale answers violate the stated contract. The team should correct the freshness check and rerun the affected tests and regression suite. It should also inspect why the two routine failures occurred. A narrow initial release might be considered only after the corrected workflow passes its predefined criteria and the accountable owners accept the remaining limits.

Measure the verification layer separately: feed known wrong outputs into it and count how many escape. Also count correct outputs it unnecessarily blocks. A system that rejects everything can appear safe while providing no useful service. Report correct automatic completion, exception rate, missed-error rate, and time to resolve exceptions together.

Design the evaluation and release decision

Keep development examples separate from held-out acceptance cases. Include representative volume patterns, unusual inputs, conflicting evidence, permission boundaries, malicious instructions inside retrieved content, and unavailable dependencies. Repeat suitable cases to observe variability. Record the model, prompt, tools, source snapshot, checker version, and expected outcome.

Classify failures before testing. Unauthorized disclosure or action should block release of the affected capability. A recoverable formatting defect might permit a narrower release with a documented fix. Avoid averaging serious failures away with many easy successes. Sample size, case selection, and rare-event uncertainty should accompany results; observing no severe failures does not prove none will occur.

Then test operations: who receives an escalation, how quickly must they act for this workflow, what happens if nobody responds, and how is a bad release disabled? Training should include both correct use and recognizing when the workflow is outside scope.

Limits and next step

This playbook offers a proposed method, not a safety certification or guarantee. Domain specialists must define requirements for consequential uses. NIST's final Generative AI Profile identifies risks including confabulation and human-AI configuration; its guidance should inform the risk discussion rather than substitute for application-specific evidence. NIST AI 600-1

Next step: complete the evaluation plan and go/no-go worksheet before choosing acceptance thresholds or running a demonstration.

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