Where should a 50-person company start with AI?
A practical way to pick your first use case without a data science team.
Most small and mid-size companies don’t struggle to find AI ideas. They struggle to choose one. Here is the filter we use.
Look for work that is repetitive, text-heavy and rule-bound
Email triage, document intake, report drafting, data entry between systems: these tasks follow patterns, involve reading and writing, and have a clear definition of “done”. They are where current models are most dependable.
Score each candidate on three things
- Impact: hours saved, errors avoided, or revenue unlocked per month.
- Feasibility: is the data accessible, and can you describe the correct output?
- Risk: what happens if the AI is wrong, and can a person review it first?
Start with a human in the loop
Let the AI draft and a person approve. You get most of the time savings, you build trust, and you collect the examples you will later use to evaluate or fine-tune a model.
Define success before you build
Pick two or three measurable outcomes, such as turnaround time or error rate, and capture a baseline now. Without it, nobody can say whether the pilot worked.
If you’d like help ranking your own list, that is exactly what our AI Assessment is for.