Start with one question
"Use AI on our data" isn't a project. "Which past customers are most likely to buy again this quarter?" is. A home services company, for example, might look for customers with ageing equipment and no recent visit. One clear question tells you which data matters and how to judge the result.
Bring the data together
The useful picture is usually spread across systems, and the same customer may appear under three spellings. Connect the sources you need for your question, and clean up duplicates before anything else. This is often most of the work, and it pays off even without AI.
Score, then explain
A model can rank customers by how likely they are to respond, based on what similar customers did before. Show the reason next to each score, such as "no visit in 18 months", because people act on a list they understand.
Put it where your team works
A report nobody opens changes nothing. Put the list and a suggested next step inside the CRM or field tool your team already uses, then let routine follow-ups go out automatically once you trust the results.
Prove that it worked
Some of those customers would have bought anyway. Hold back a comparison group that you don't contact, then compare the two. That's the only honest way to know how much revenue the work really added.
Check consent first
Being able to contact someone is different from being allowed to. In Canada, CASL covers commercial emails and text messages, and PIPEDA covers how you use personal information. Check your obligations, with legal advice where you need it, before the first campaign goes out.