Use four questions for every stage
Ask:
- Does the stage follow a stable rule?
- Can its input and output be checked directly?
- Could a wrong result create a meaningful consequence?
- Does uncertainty require a person to interpret evidence?
Stable, reversible transformations are stronger automation candidates. Ambiguous or consequential decisions should remain owned by a person, even when tools help prepare the evidence.
Preserve ownership across operating modes
The NIST AI Risk Management Framework connects governance, measurement, mapping, and management of AI risk. W3C PROV-O helps relate artifacts to the activities and agents that shaped them. CommonMark keeps stage rules and outcomes readable across different tools.
An automated step still needs a named input, output, stopping condition, and observable record. Assistance should not erase who decides whether the result is fit for use.
Choose the smallest useful amount of automation
This approach fits workflows that mix stable transformations with judgment. It fits poorly when the process owner wants every stage automated regardless of consequence or evidence quality.
Start with the stage that has the clearest rule and easiest inspection. Expanding from a visible success is safer than automating an ambiguous path all at once.
Complete the decision ring
Return to Map an AI Workflow for One Recurring Job for the cluster view. Read AI Workflow Versioning when changing a stage's operating mode creates a new process definition. Revisit AI Workflow Stages if the manual and automated parts do not share a clear boundary.
Split routine movement from judgment
The synthetic task needs judgment at the unsupported claim, while its file comparisons and record updates follow settled rules.