Choose a useful pilot
- Industry term
A useful pilot is a small trial of an AI workflow, chosen because it delivers a result people need and tests the assumption a bigger build would rest on.
Guesses that look safe on paper, such as whether the data is clean enough or whether people will use the result, can fail on ordinary work. A narrow trial finds the failure while little rests on it, so it gets fixed before other work is built around the mistake.
Where you’ll meet it
You run a small online store and want an AI assistant to help with the customer inbox. Most emails ask where an order is, which the tracking records answer. Others ask for refund exceptions, which you decide one case at a time.
What to do
Start with the where-is-my-order emails. Have the AI draft each reply, compare it with the tracking record, and count the drafts you had to correct, including replies to unclear emails. Write down beforehand how many corrections a week would still let drafts go out after a quick glance.
Easy to mix up
| Choose a useful pilot | Demo |
|---|---|
| People who need it for their everyday work | Nobody, since it runs once for an audience |
Check yourself
You want an AI to enter supplier invoices into your accounting software. Your largest supplier emails PDFs, and about thirty smaller ones mail paper invoices that you scan. Which first trial tells you more?
Go deeperThe idea comes from lean startups, and testers can skew a trial
The advice echoes the minimum viable product from Eric Ries's The Lean Startup, the least a team can build to test the leap-of-faith assumptions its plan rests on. Ries wrote about products and their customers, and the same reasoning applies to a workflow and its outputs.
Some workflows produce nothing usable until several linked steps work, such as reading an email, finding the order, and updating it, so the trial includes them all. The people who chose the tool can work around its quirks without noticing, so a count from its everyday users comes closer to what a rollout will see.