Skip the strategy deck. Pick one process that already hurts, check your data, and run a small pilot with a pass mark agreed up front.
Start with a process, not a tool
Most AI projects that stall start with a tool: someone sees a demo and asks where it could fit. The ones that work start with a process that already hurts. Look for work that is repetitive, text-heavy and done by people who would rather be doing something else, like answering the same customer emails, copying data out of PDFs, or writing first drafts of quotes and reports.
Write down how often it happens, how long it takes and what a mistake costs. If you can’t put rough numbers on it now, you won’t be able to tell later whether AI helped.
Check your data before you compare vendors
AI is only as good as the information you give it. Before you compare vendors, find out where the information for your chosen process actually lives. Is it in one system or five? Is it typed, or scanned? Who is allowed to see it?
You don’t need perfect data to start. You do need to know what you have, because the answer decides whether your first project is a four-week pilot or a two-month clean-up.
Run a pilot with a pass mark
A good first pilot takes four to six weeks, uses your real data and has a pass mark agreed before any work starts. For example: staff send the assistant’s draft replies without edits at least 60% of the time. Keep a person in the loop for every output during the pilot. It protects your customers and gives you the data to judge the result.
At the end you should be able to make a plain go or no-go decision. If the numbers don’t hold up, you have spent a few weeks, not a year.
Plan for the people side
The most common reason a working pilot never reaches production is that nobody changed how the team works. Decide early who owns the tool, who checks its output and what happens when it gets something wrong.
Train the people who will use it, and ask them what slows them down. They usually know exactly where AI should help next.
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