What should we decide before ordering?
What decision is the AI allowed to influence?
AI workflow automation applies this principle to business event, validated input and aI classification or draft. Drafting a reply, finding a policy paragraph and switching a light carry different risks. We classify whether the output is advice, a suggestion, a reversible command or an action with material effect. The delivery timeline surveys business event before design approval, then tests aI classification or draft during commissioning. For this service, the scope records how approval and tool call connects to logged system result, then verifies that relationship during commissioning or recovery testing.
AI workflow automation applies this principle to connector and retry config, logs and alerts and process approval owner. Retrieval, tools and larger models may reduce some errors, but none turns generated text into verified truth. We design citations, confidence cues and human review around the actual consequence. Medical, legal, financial, employment, security and life-safety decisions require qualified owners outside the model. For this service, the scope records how third-party APIs connects to connector and retry config, then verifies that relationship during commissioning or recovery testing.
Where may data travel and remain?
AI workflow automation applies this principle to action reversibility, exception rate and approval threshold. It does not mean every connected feature is offline or that privacy appears automatically. Model downloads, telemetry, speech services, connectors, backups and support tools can cross that boundary. For this service, the scope records how aPI limits connects to duplicate handling, then verifies that relationship during commissioning or recovery testing.
AI workflow automation applies this principle to tool call fails, stop repeated action and queue for review. A knowledge assistant must not reveal a document merely because it indexed the words. Service accounts receive the narrow access needed for their task, secrets stay out of prompts and logs, and retention is set deliberately. The customer decides the lawful basis, staff policy and approved datasets. For this service, the scope records how run manual process connects to reconcile final state, then verifies that relationship during commissioning or recovery testing.
How is accuracy tested after launch?
AI workflow automation applies this principle to normal case, denied action and duplicate event. Acceptance testing uses representative inputs, awkward phrasing, missing information, denied requests and known edge cases. The team records the expected outcome, model response, source citation, tool call and reviewer decision. Maintenance repeats the normal case and denied action checks after material changes, updates or reported faults. For this service, the scope records how delayed API connects to rollback test, then verifies that relationship during commissioning or recovery testing.
AI workflow automation applies this principle to connector and retry config, logs and alerts and process approval owner. Version records and a small repeatable test set make drift visible after an update. There is always a non-AI route for important work: manual control, ordinary search, a queue for staff review or a disabled automation. We prefer a narrow assistant whose limits are obvious to a broad agent with vague authority. A lower-complexity alternative remains valid when third-party APIs can be handled by the documented manual or existing-system route. For this service, the scope records how third-party APIs connects to connector and retry config, then verifies that relationship during commissioning or recovery testing.
