A good first AI workflow proves more than a model can produce plausible text. It shows that intelligence can improve a real operation inside acceptable controls.
Look for repetition with judgement
Tasks that involve repeated classification, extraction, summarisation or routing may benefit when rules alone are too brittle.
Prefer visible outcomes
Define what better means: shorter handling time, fewer hand-offs, more complete preparation or easier access to trusted knowledge. Avoid measures based only on model activity.
Design for uncertainty
The workflow needs a place for confidence thresholds, human review and exceptions. A system that cannot say “this needs attention” is difficult to operate safely.
Choose representative evaluation data
Test against the variety, ambiguity and edge conditions found in real work. Evaluation should continue after deployment as inputs and expectations change.
Map the work before selecting the model
Write down the trigger, inputs, decisions, hand-offs, exceptions and final outcome of the current process. Include the quiet work that experienced people do: checking context, resolving ambiguity, finding a trusted source and knowing when to stop. A useful automation boundary usually appears only after the real workflow is visible.
A strong first candidate is bounded, frequent and measurable. It has enough volume to learn from, but a clear beginning and end. Its mistakes can be detected before they create disproportionate harm. Avoid beginning with a task whose success depends on undefined judgement or where nobody owns the final decision.
Establish data and permission boundaries
List the information the workflow needs, where it comes from and what the model or tool is allowed to retain. Separate public, internal, confidential and regulated material. Give an assistant the minimum tool permissions needed for the chosen step; reading a knowledge source does not automatically justify editing records or sending messages.
Decide how prompt content, retrieved documents, outputs and feedback will be logged. Confirm what suppliers may use for service operation or model improvement. These decisions should be made before representative information is uploaded, not after a successful demonstration.
Build an evaluation set
Create examples that represent routine work, awkward edge cases, incomplete inputs and situations where the correct response is to escalate. Define acceptance criteria that people can apply consistently. Depending on the workflow, these may cover factual accuracy, source support, classification quality, completeness, tone, processing time or safe refusal.
Evaluation is not a single benchmark. Keep a versioned set of important cases and run it whenever the prompt, model, tools, data source or business rule changes. Review failures by category so that an apparent average improvement does not hide a serious regression.
Put people at the right control point
Human review is most useful where consequence or uncertainty is high. Define what reviewers see, what they can correct and how exceptions return to the process. A vague instruction to “keep a human in the loop” is not an operating control unless responsibility and response time are clear.
Measure the operation
Compare the pilot with the current process. Track outcome measures such as completed cases, correction rate, time to resolution, unresolved exceptions and user confidence. Include the cost of review, monitoring and supplier usage. Model output volume is activity, not evidence of value.
Run a staged pilot
- Observe the workflow and agree a baseline.
- Test offline with representative cases.
- Run in shadow mode without changing the live outcome.
- Introduce a limited user group and explicit review.
- Expand only when quality, control and ownership remain acceptable.
Go or no-go questions
- Can success and material failure be recognised?
- Are data use, retention and tool permissions understood?
- Is there a named owner for exceptions and ongoing evaluation?
- Can the workflow be paused or rolled back safely?
- Does the measured benefit justify its operating cost and risk?
If those answers are unclear, the next step is better workflow design—not a larger model.