Decision guides
How to evaluate an AI automation pilot before rollout
A fast demo does not show whether a workflow saves work. Evaluate the complete task, including corrections, human review, integration failures and operating cost. Use a bounded pilot before connecting AI to consequential actions.
Planning guide. Examples are illustrative; they are not customer case studies or measured results.
Choose a repeatable task and a baseline
An illustrative task is extracting fields from incoming supplier documents into a proposed record. Define the input, the output, the person who accepts it and the current method. Record present task time and errors on representative cases. Do not assume all administration should be automated or that every task needs a model.
Build a test set that includes awkward cases
Include the normal documents and the ones that cause trouble: missing fields, unreadable scans, different languages, duplicates, conflicting values and unexpected instructions inside a document. Keep evaluation cases separate from examples used to tune the workflow. Use material you are permitted to process and agree where it may be sent.
Define acceptance before seeing the result
Specify which fields must match the source, which errors require human review and which actions the system may take. A low-confidence result should not silently become an approved business record. Define the manual fallback, escalation owner and what happens if a connected application is unavailable.
Measure the whole task
Record accepted outputs, corrections, review time, failed jobs and total elapsed effort. Keep model usage and infrastructure costs alongside staff time. Useful measures include the share of outputs accepted without correction and review minutes per completed task. Set acceptable thresholds from the risk and current workload; a universal accuracy percentage is not a substitute for this decision.
Make a proceed, revise or stop decision
Proceed only when agreed acceptance conditions are met and an operating owner can handle failures. Revise when the task is useful but a known weakness can be tested in a bounded next iteration. Stop when simpler rules or existing software solve it more reliably, or the review burden outweighs the benefit. Keep retries and duplicate prevention in scope before allowing production writes.
| Measure | Record for each method | Why it matters |
|---|---|---|
| Completed tasks | Input cases, completed outputs and exclusions | A fast partial result is not a finished task |
| Quality | Accepted outputs, corrections and consequential errors | Different errors carry different business costs |
| People’s effort | Preparation, review, correction and exception time | Generation speed alone can hide extra work |
| Operations | Usage cost, failed jobs, retries and recovery | The workflow must remain usable after the demo |
Plan the next step
- A named process owner and an agreed manual fallback.
- Representative normal cases and exceptions.
- Data boundaries, permitted actions and human approvals.
- Baseline, acceptance conditions and a dated decision record.
AAA Labs can scope an AI workflow pilot for a Malaysian business around one task and an agreed evaluation. Start with the work and the decision you need to make; avoid sending sensitive example documents through the public form.
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