You don't need an AI strategy. There, we said it. What your business needs, if AI tempts you at all, is one bounded experiment with an honest ending: a pilot small enough to be safe and rigorous enough to be believed.
Because the expensive failure mode isn't trying AI and finding it wanting. It's the vague, sprawling "AI initiative" that never quite works, never quite dies, and salts the ground for every sensible attempt that follows. Here's how to run the version that produces a decision instead.
The sweet spot is work that's frequent, fuzzy and checkable: reading, sorting, extracting, drafting. If the task follows fixed rules, ordinary automation will beat AI on cost and reliability, so save the pilot for genuine judgement-adjacent drudgery, like the document and email work we've written about.
"AI will correctly categorise 90% of incoming enquiries, cutting first-response time by half." One sentence, numbers included. Now the pilot has a finish line, and everyone can recognise success or failure when it arrives. A pilot without a hypothesis isn't an experiment. It's a subscription with optimism attached.
One task. Controlled data, checked against your privacy obligations before anything leaves the building. A fixed time window. A named owner. And integrate lightly at first: the AI's output can land in a queue for a person rather than being wired into your live systems on day one. You're testing a hypothesis, not committing an operation.
Collect a representative set of real cases, including the awkward ones, and decide in advance what a right answer looks like. This evaluation set is the heart of the pilot: it's what turns "seems pretty good" into "89% correct, and here's where it fails". Define the escalation too: below what confidence, or for which categories, does a human take over entirely?
A person reviews the output, with the context to judge it and the standing to reject it. Then measure the complete process, review time included, against your baseline. An AI that's 90% right but doubles the checking effort has answered your hypothesis, just not the way the vendor hoped.
The pilot closes with one of three honest outcomes: adopt it and integrate it properly, adjust it and re-test, or retire it with the evidence filed for a year's time when the technology has moved. All three are wins, because all three are decisions, made with your data on your task. That's what a few weeks and a modest budget buys, and it's the approach behind our AI integration work: evidence first, commitment second, hype nowhere.
If there's a task in your business you suspect AI could take off someone's plate, let's design the experiment that finds out properly.
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