Choose an AI workflow you can measure.

Pick a bounded task

Start with a repeated task whose inputs and expected output can be described clearly. Drafting a response from approved material or extracting fields from a familiar document can be easier to evaluate than asking an assistant to run an entire department. Name the person who will use the result and the decision it should help them make.

Understand the information involved

Identify where the necessary information comes from and whether your team has permission to use it. Decide which information the system may access, what it should retain, and what should stay outside the workflow. Poor source material can produce misleading answers even when the interface feels polished.

Define a good answer before development

Collect representative examples, including incomplete inputs and unusual cases. Have the people who know the work describe acceptable outcomes and common mistakes. Evaluate the proposed system against those examples. Track whether it completes the task correctly, when it needs review, and whether it actually saves useful time.

Design for uncertainty

The interface should let a person inspect, edit, or reject an output. When evidence is missing, a useful workflow can ask for clarification or hand the task back instead of inventing an answer. Actions with consequences need an appropriate review step. Treat these behaviors as product requirements from the beginning.

Expand after learning from use

Launch with a limited audience, gather feedback, and examine failures. Compare the complete workflow with the previous process, including time spent checking and correcting results. That evidence helps you decide whether to improve the task, connect another information source, or expand to a new use case.