How to Choose Your First AI Project (and Know If It Worked)

Many AI pilots stall because they start too big, too vague or with no way to measure success. A better approach is small, specific and measurable.

List tasks that are:

• Repetitive: done weekly or daily.
• Rule-based or pattern-based: the steps are fairly predictable.
• Time-consuming: at least a few hours a week.
• Low-risk if imperfect: errors are easy to catch and fix.

Rate each from 1 to 5 on time saved, ease of setup, risk (lower is better) and data availability. Start with the highest overall score.

Pick one or two measures:

• Hours saved per week.
• Error or rework rate.
• Turnaround time.
• Customer satisfaction.

Record today's baseline so you can compare honestly.

• Limit it to one team or process.
• Keep a human approving output.
• Run it for four to six weeks.
• Collect feedback from the people using it.

• Worked: expand carefully and document the process.
• Mixed: adjust prompts, data or scope and retry.
• Didn't work: stop, note why, and try the next candidate. That's a cheap lesson, not a failure.

• Choosing a flashy project over a useful one.
• Skipping the baseline.
• Ignoring the staff who will use it.
• Forgetting ongoing review once it's "live".

Treat AI like any business investment: pick a clear problem, measure results, and scale what proves itself.