AI5 min read
How to choose your first AI automation project
A simple framework for picking an AI project that delivers measurable results, with examples of good first candidates.
Most companies do not struggle to find ideas for AI. They struggle to choose the first one. A good first project builds confidence, produces numbers you can show and teaches your team how to work with the technology.
What makes a good first candidate
- The task is repetitive and happens many times each week.
- Inputs and desired outputs are clear enough to check.
- A mistake is inconvenient, not catastrophic, and a person can review results.
- You can measure the current cost in hours, delays or errors.
Examples that tend to work well
- Extracting fields from invoices, forms or emails into your systems.
- Drafting first replies to common support questions for an agent to approve.
- Searching and summarizing internal documents and policies.
- Classifying and routing incoming requests to the right team.
Plan for quality from the start
Collect a set of real examples before building, and agree how you will score results. Include a human review step for low-confidence cases. These two decisions matter more than the choice of model.
Start small, then expand
A pilot with a few users for four to six weeks is usually enough to prove value. Once the numbers are in, extending to new teams or processes is far easier to justify.