A practical checklist before you bring AI into your business

A lot of AI initiatives start from the wrong direction: “we should do something with AI” instead of “here is a specific task AI would genuinely help with.” The order matters more than it sounds like it should.

Start with the task, not the technology

Name one repetitive, well defined task that costs real hours today. If you cannot name one, that is useful information too. It might simply not be the right time yet, and that is a fine answer.

Check your data before you check the model

Does the answer to that task actually live somewhere in your existing systems? AI grounded in your own information is far more reliable than AI asked to guess from general knowledge. If the data is scattered, messy, or does not really exist yet in usable form, that is the real first project, not the AI feature itself.

Decide where a human stays in the loop

For anything with real consequences, decide upfront where a person checks the output before it goes anywhere. This is not a temporary training wheel to remove later. For most tasks worth automating, it is a permanent design decision.

Start smaller than feels satisfying

The instinct is to solve everything at once. The version that actually ships, and actually gets used, is the one narrow task done well that earns the next one, not the ambitious platform that never quite launches.

The checklist

• A specific, repetitive task is named, not a vague ambition
• The data needed to ground an answer already exists, or is a known first step
• Someone is responsible for reviewing output wherever it matters
• The first version is scoped small enough to actually ship