Start with a workflow, not a tool.
The most useful AI projects start with a process your team already runs every week. Write down the steps, the people involved, and where time is lost before you choose any technology.
Look for repetitive work with clear inputs and outputs.
Good candidates have predictable inputs, a recognizable result, and enough volume that small gains add up quickly.
Check data quality, access, and exception cases.
Confirm that the information the workflow needs is available, allowed, and reasonably consistent. List the exceptions people handle today—they will shape the design.
Define a baseline and a measurable success metric.
Measure how the work performs today so you can tell whether the new system actually helps. Pick one or two metrics the people doing the work care about.
Keep human review where the risk or judgment calls for it.
Automation should back up good judgment, not hide accountability. Add approval steps wherever decisions are sensitive.
Pilot with a small group, learn, and improve before expanding.
Launch with a small team, gather feedback, and refine. Expand once the workflow is dependable.
Have a workflow in mind? Talk to Brackenwire about a sensible first step.
