How to choose your company's first AI project

Your first AI project sets the tone for everything that follows. If it delivers clear savings, budget for the next one is easy. If it drags on with fuzzy results, AI becomes "something we tried". Choosing well matters more than choosing something ambitious.

Score each idea on four questions

List five to ten candidate processes, then score each from 1 to 5:

• Hours: how many staff hours does it consume each month?
• Data: is the information it needs already digital and accessible?
• Risk: what happens if the AI makes a mistake? Lower risk scores higher.
• Visibility: will management notice and care if it improves?

Add the scores and start with the highest. In practice the winners are usually internal back-office tasks: preparing monthly reports, extracting data from invoices or forms, drafting standard replies, or logging and following up sales enquiries.

Keep the scope to one workflow

A good first project fits in eight to twelve weeks and touches one team. "Automate accounts payable invoice entry" is a project. "Use AI across finance" is a strategy, and strategies are hard to measure.

Decide what success looks like before you start

Agree on the baseline and the target with the department head: for example, invoice processing time from three days to same day, or 60 staff hours a month returned to the team. Write it down. This turns the project review into a simple comparison rather than a debate.

What to leave for later

Customer-facing chatbots, credit or underwriting decisions and anything touching sensitive personal data can deliver large gains, but they need stronger governance and testing. Tackle them once the team has one proven workflow and a working review process.

Getting it done

A first project needs someone with hands-on implementation experience. Many companies bring in an AI implementation specialist on a project basis for the first three months, then decide whether to extend or hire permanently.