Clarity
An AI system needs a recognizable job. Variation is expected, but the team should be able to identify where the work begins, what a useful end looks like and which differences matter.
Is this workflow ready for useful AI?
Eight practical questions reveal whether to observe, define, validate or begin a controlled first release. The assessment evaluates the work around the model, not enthusiasm for AI.
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Why these conditions matter
Readiness belongs to the workflow.
Ownership
Someone must decide whether the workflow is working, approve changes and take responsibility when the system reaches a boundary.
Evidence
Representative examples make evaluation possible. Include ordinary cases, difficult cases and failures so a convincing demonstration is not mistaken for dependable performance.
Access and recovery
The application needs approved ways to read and act through business systems. Incomplete work also needs a durable status, owner and path back into motion.