How to define acceptable failure modes for AI workflow automation

Define unacceptable failures in the language of the customer workflow. Some errors can be corrected cheaply; others need a stop or escalation even when rare. A blended accuracy percentage can hide that difference.

Start with the decision

List failure categories, consequences, detectability, and recovery ownership. Build evaluation examples for the most consequential categories. Ask the buyer what must never happen automatically, and document where human authorization remains required.

The illustrative scenario

This is an educational workbook. The company or fund and all numerical inputs below are fictional. They are not ADIN results, customer claims, market benchmarks, or a forecast.

The fictional product routes purchase requests through a small operations team. A requester supplies context, a budget owner approves spending, and an administrator maintains the workflow. Exceptions are common enough to affect adoption.

Automating a step can move work to the exception queue rather than remove it. Keep proposed actions, approvals, and completed actions separate in the event history.

An evaluation sample with a denominator

Imagine a permissioned evaluation set with 27 cases and 18 that meet a rubric defined before the test. The resulting percentage describes only this invented sample. Seventy percent is not a recommended target, a safety standard, or evidence about any real system. What matters is the rubric, sample coverage, and consequences of the remaining cases.

Automating a step can move work to the exception queue rather than remove it. Keep proposed actions, approvals, and completed actions separate in the event history. Break the 9 remaining cases into failure categories before interpreting the aggregate. A recoverable wording issue and an unauthorized action may require different product behavior even when both count as a failed case. Keep reviewer disagreement and missing evidence visible.

The assumed time comparison is 18 minutes before and 11 minutes after, including the stated human step. Validate the same start and end points in a real study, then include retries, repair, and handoffs. Compare the full request-to-decision process with the current system. A faster initial classification can hide a slower approval or correction stage. A short average time is not enough if the result cannot be used or trusted.

Use this worksheet

Answer the questions with a source, date, owner, and next action. Mark an unknown as unknown. Use public or permissioned information and involve a qualified specialist when a question requires expertise beyond the team’s competence.

1. What can go wrong?
2. Who bears the consequence?
3. Can the error be detected?
4. How is the action reversed?
5. Which case requires escalation?

Questions specific to this scenario

• Who approves a purchase?
• Which exceptions stop the workflow?
• How are manual overrides recorded?

Inspect request histories, approval timestamps, exception reasons, manual overrides, and changes in process ownership. Include requests that stalled or were withdrawn.

A concrete next test

Follow one request that passes normally and one that needs an exception. Ask the requester, approver, and administrator what each can see and change. Record whether automation removes work or increases the burden on the person managing stalled requests.

Interpret the result

A failure taxonomy should change product behavior and testing priorities. It is not a certificate of safety. Revisit it when the system gains new data, permissions, customers, or the ability to take actions.

Compare the full request-to-decision process with the current system. A faster initial classification can hide a slower approval or correction stage.

Sources and method

These primary resources provide background for the concepts. The fictional scenario, calculations, and worksheet are ADIN educational examples; the sources do not report or validate those invented results. Reviewed October 11, 2026.

• NIST: AI Risk Management Framework