Most organizations feel pressure to “do something with AI.” The risk is choosing a first project because it sounds impressive rather than because it will work. A good first project builds confidence, delivers visible value and teaches your organization what AI is actually good at.
1. It happens often
Look for work that repeats daily or weekly: reading invoices, answering the same internal questions, summarizing activity, routing requests. Frequency is what turns small time savings into meaningful ones.
2. The inputs already exist digitally
AI needs material to work with — documents, emails, records. If the information lives on paper or in someone’s head, start by fixing that.
3. A person can easily check the result
The best early projects keep a human in the loop: AI drafts, extracts or suggests, and a person approves. This keeps quality high while your team builds trust in the output.
4. You can measure it
Decide in advance what success means: hours saved, turnaround time, error rate. Without a measure, even a successful project is hard to justify extending.
Start small, then expand
One well-chosen project, delivered and measured, is worth more than a broad initiative that never ships. Once it works, extend the same pattern to the next process.