AI is being attached to almost every product on the market. Some of it is genuinely useful. Much of it is a feature looking for a problem. A short checklist separates the two before you commit budget.
1. Name the problem without using the word AI
If you cannot describe the problem in plain language, the tool is unlikely to fix it. “We answer the same forty customer questions every week” is a problem. “We should be using AI” is not.
2. Check where your data goes
Understand what the tool sends off your systems, where it is stored, how long it is kept, and whether it is used for training. This matters more if you handle customer records, health information or anything under contract.
3. Decide what a wrong answer costs
Drafting a first-pass email is low risk. Quoting a price, giving medical guidance or acting on a financial figure is not. Match the level of human review to the cost of being wrong.
4. Test on your own work, not the demo
Vendor demos are built to succeed. Run the tool against a sample of your real, messy inputs and judge it on that. A two-week trial with genuine work tells you more than any sales call.
5. Agree how you will measure it
Pick one number before you start: hours saved, response time, error rate, conversion. Review it after a month. If nothing moved, the honest answer is to stop paying for it.
Used deliberately, AI removes friction from work people already do. Used reflexively, it adds a subscription and a new thing to maintain.
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