Create a business-user test script for an AI workflow
In this article (4 sections)
Technical unit tests do not show whether an owner can understand, review and recover the process. Business user acceptance testing should exercise outcomes and exception operations.
Retain a failed scenario
The automation lab compares three authored expected and actual outcomes.
from automation_cases import business_test_case
result = business_test_case()
assert result["failed"] == ["U3"]
assert result["tests"][0]["pass"] is True
assert result["tests"][2]["actual"] == "continue"
assert result["business_signoff"] is FalseThe approval-timeout scenario should stop but continues, so signoff stays false. No platform ran.
Write observable steps
For every scenario, specify role, precondition, fixture input, action, expected visible state, downstream state and evidence to save. Cover normal work, duplicates, invalid data, low confidence, decline, approval timeout, upstream outage, retry, cancellation and recovery.
Use synthetic or sanitized data and a non-production environment. Let real owners execute the script without developer intervention. Record workflow version, run ID, screenshots or exports, actual result, severity, owner and retest result. Do not rewrite the expected outcome after seeing a failure.
Include usability: can the reviewer understand evidence, correct a field, find the exception and resume safely? Signoff is an accountable decision after critical defects close, not an automatic consequence of a pass percentage.
The Generative & Agentic AI course joins business acceptance with deterministic QA.
Exercise
Write ten scenarios for one local workflow and ask another person to run them. Preserve one failed trace, repair it and attach the clean retest without deleting the original.
Continue learning
This article is part of the Business automation with AI sequence. Use the neighbouring tasks when you need the prerequisite or the next application.
- Review the prerequisite or neighbouring task in Measure automation value against a manual baseline.
- Continue with Build a safe research-to-brief workflow.
Reference: Zapier workflow run statuses.
Pankit Kumar has 10 years in Data Science & AI, building and shipping production systems in regulated pharma and clinical environments. He is a freelance trainer at Boston Institute of Analytics, AnalytixLabs and Scaler, and has taught this material to thousands of working professionals.
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