Designing AI workflows that save time

Start an AI project with a recurring task, not a model or agent. Look for work that consumes time, follows a recognizable process, and has an outcome someone can check—such as sorting incoming requests, preparing a report, or finding answers in approved internal documents. Measure how the task works today so you have a baseline for improvement.

Map the workflow before automating it. Identify what information comes in, what decisions are required, which steps are predictable, and where a person needs to use judgment. Automate deterministic steps with ordinary software where possible; use AI for the parts that involve language, classification, or summarization. A smaller, well-defined workflow is easier to test than an open-ended agent.

Design for review and recovery. Show users the source material behind an answer, make uncertain results visible, and provide a clear way to correct or escalate them. Limit the data the system can access to what the task requires, and define what it must do when information is missing or a service is unavailable.

Test with representative examples—including edge cases—before expanding access. Track accuracy, completion time, correction rates, and the cases handed back to people. Roll out gradually and keep a human in the loop until results are dependable. The best automation is not the most autonomous one; it is the one that measurably reduces friction without hiding risk.