Look for repeatable decisions
Good candidates often involve reading, sorting, summarizing, comparing, or routing information at a predictable point in a process. Think intake queues, customer requests, internal knowledge, reports, or documents that require the same first-pass review every day.
These workflows give teams a concrete way to measure whether a solution is helping: less time spent, fewer handoffs, faster response, or more consistent output.
Keep people in the loop where judgment matters
Automation does not need to replace a decision to be useful. In many cases, the right design prepares context, recommends an action, or completes routine steps while a person handles exceptions and final approval.
This approach protects accountability and helps teams build trust in a system before expanding its role.
Build around the workflow, not the model
A capable model is only one part of an operational solution. The surrounding data, integrations, permissions, error handling, and evaluation process determine whether it can be used reliably.
Start with a narrow workflow, define what good performance looks like, and plan for review. That creates a practical foundation for broader AI work.