Evaluate an AI tool against a specific workflow rather than a general promise of productivity. Define the input, the output your team needs and the mistakes that would matter. Then use representative examples to decide whether the tool is useful enough to adopt.
1. Define the task and baseline
Describe how the work is done today and record the effort involved. Decide whether the aim is faster drafting, more consistent classification or another observable change. Keep the first evaluation narrow.
2. Use realistic examples
Try ordinary cases and awkward ones: incomplete information, conflicting documents and requests outside the task. Check the result against the original input. A polished demo is not the same as performance on your own material.
3. Examine the data requirements
Identify what the tool receives, where the information goes and who has access. Review the provider’s current documentation and contract for the exact product you are considering. Do not assume all accounts or models have the same data terms.
4. Include human review
Estimate the effort to check, correct and approve the output. Define which actions need approval and how uncertain results are escalated. A tool that generates more material than the team can review may add work.
5. Check integration and running costs
List the systems involved, required permissions and usage charges. Decide who handles failures and changes in the connected tools. Compare the complete operating process with the manual baseline.
6. Decide whether to proceed
Document the cases the tool handles acceptably and the cases it should not handle. Digital Revo’s AI automation service starts with a workflow assessment so the implementation follows that decision.
