What is an AI workflow assessment?
An AI workflow assessment evaluates one specific process—such as lead research, invoice handling or client reporting—to decide whether AI belongs in it, where it belongs and what should remain human.
It is narrower than a company-wide AI opportunity assessment. The goal is to move from a vague idea like “automate reporting” to an implementation boundary you can actually test.
The seven questions to ask
- What triggers the workflow? Define the event that starts the work.
- What output counts as done? Name the artifact, decision or updated record.
- Which steps repeat? Separate stable work from exceptions.
- What information is required? Identify source systems and data quality.
- Where is judgement required? Do not hide human decisions inside an “automation” label.
- What happens when the system is wrong? Consider reversibility and affected people.
- What would success change? Time, quality, throughput, revenue or customer outcome.
A simple scoring model
| Signal | Low fit | Higher fit |
|---|---|---|
| Repetition | Rare, bespoke work | Same pattern repeats frequently |
| Inputs | Unclear or unavailable | Digital, accessible, consistent |
| Output | Subjective or undefined | Clear expected format or action |
| Exceptions | Most cases are edge cases | Exceptions are identifiable |
| Risk | Irreversible, high-impact | Reviewable and reversible |
Design the smallest useful AI boundary
The strongest first pilot usually does not automate the whole workflow. It automates one repeatable middle step and keeps the trigger, approval or exception handling human.
What a good assessment should produce
At the end you should have a one-page decision: current workflow, proposed workflow, required data, approval points, expected value, measurable success criteria, implementation effort and known risks. If you cannot state those clearly, the use case is not ready to build.