Define the Smallest Useful First Release
Define the smallest useful AI automation release with boundaries, review gates, failure paths, and inspectable acceptance cases.
Small means one useful operating result
A first release should complete a real piece of work for a defined group. A disconnected demo may prove that a model can summarize an email, but it does not prove that the right employee receives the summary, can correct it, and knows what to do next.
Keep one trigger, one primary outcome, a limited set of inputs, named users, and explicit exclusions. Small scope shortens the feedback cycle. Useful scope lets the team observe an operational result.
Write the brief in nine parts
- Trigger: the event and source that start the release.
- Outcome: the observable state that marks completion.
- Included cases: the inputs, users, locations, and volume covered.
- Excluded cases: work that continues through the current process.
- Required data: fields, source systems, and permissions.
- Rules and AI tasks: each decision and its mechanism.
- Human gates: reviewer, evidence, actions, and response expectation.
- Failure paths: what happens when data, integrations, or outputs fail.
- Acceptance cases: examples with expected results.
Write exclusions as operating instructions. “Referrals remain in the current queue during the first release” protects the work. “Referrals out of scope” leaves the employee guessing.
Design failure paths before launch
Assume a required field is blank, two records conflict, an integration is unavailable, the AI output is unsupported, or the owner does not respond. Decide whether the system should retry, hold, notify, route to review, or fall back to the existing process.
The failure path needs an owner and enough context to act. A generic error email moves the burden to another person. A useful alert identifies the case, failed step, available evidence, and next action.
Turn exceptions into acceptance cases
Acceptance cases describe an input and the result the team expects. Include the normal path and cases that expose weak assumptions. For enquiry handling, test a clear fit, incomplete request, duplicate account, existing customer, referral, unsupported geography, conflicting ownership, and unavailable data source.
Write the expected action before the system runs. Then compare the actual result. This prevents the team from accepting any plausible-looking output after the fact.
Worked first-release brief
Trigger: a website form creates a new CRM contact. Outcome: the intake coordinator receives a brief, evidence links, existing-account check, and recommended queue. Included: English-language website enquiries for two service lines. Excluded: referrals and event leads continue through current queues.
Rules create the task and check account ownership. AI extracts the stated need and summarizes approved public evidence. Records with missing, conflicting, sensitive, or strategic context go to coordinator review. If enrichment fails, the task still appears with the original request and a failure reason. The salesperson controls the response.
Your turn: write the first-release brief
Complete all nine fields in Module 5 of the workbook. Add at least six acceptance cases, including incomplete, conflicting, and system-failure examples. Ask another person to read the brief and explain what the release does. Revise any field they interpret differently.
The final brief becomes Section 5 of the Automation Decision Pack.
Quick Check
1. What makes a first release useful?
2. What should an exclusion explain?
3. When should the expected result for an acceptance case be written?
What you have produced
Your first-release brief and acceptance set are Section 5 of the Decision Pack. Module 6 adds the baseline, owner, launch controls, and first review decision.