UK AI Adoption: Turning Vendor Demos Into Evidence You Can Trust
Most UK founders and operators meet AI tools through polished demos. The presenter types a prompt, the output looks impressive, and the room nods. The problem is that a demo shows what a tool can do under ideal conditions, not what it will do with your messy inbox, your inconsistent customer records, or your team's habits. Turning that impression into a sound buying or adoption decision requires a different kind of evidence. This article sets out a method for converting vendor claims into testable questions and then into a go, no-go or pilot decision, without relying on the vendor's own numbers or anyone's recommendation.
Separate the claim from the conditions
Every AI claim has two parts: the outcome and the conditions under which it was reached. A statement such as 'drafts replies twice as fast' tells you the outcome but hides the conditions. Who was doing the drafting before? What kind of enquiries were used? Were the replies reviewed, and by whom? Before you spend any time on a tool, write each claim on a single line and add three columns beside it: the task it applies to, the baseline you are comparing against, and the input data the result depends on.
This exercise often reveals that a claim describes a narrow case. A summarisation feature that performs well on clean meeting transcripts may struggle with overlapping speakers, heavy jargon or mixed-language threads. You are not trying to disprove the vendor. You are trying to find the boundary of the claim so that you know which of your workflows it actually covers.
Write down what success looks like before you test
The most common failure in AI evaluation is moving the goalposts. A team starts a trial, sees something interesting, and quietly redefines success to match what the tool happens to do well. Prevent this by writing a short success statement before any testing begins. It should name the task, the measure, the minimum acceptable performance and the cost of a wrong output.
For example: 'Triage inbound supplier invoices into the correct approval queue. Measure the percentage routed correctly on a sample of 200 real invoices from the last quarter. Acceptable is 95 percent or higher, and any misrouted invoice above a set value must be flagged for human review.' This is specific enough that a second person could run the same test and reach the same verdict.
Build a small, representative test set
Use your own material, anonymised or redacted where needed, rather than examples supplied by the vendor. Pull a sample that reflects real variation: the easy cases, the awkward ones and the cases where people on your team already disagree. A test set of 50 to 200 items is usually enough to reveal patterns without consuming weeks of effort.
Keep the test set fixed. Run every candidate tool against the same items, and record the outputs in a simple sheet with columns for the input, each tool's output, a pass or fail, and a short note on the type of failure. Patterns in the failure notes are often more useful than the headline percentage. If errors cluster around a particular supplier format or a particular kind of request, you have learned where human oversight must stay in place.
Handle personal and commercial data with care during testing. Check where data is processed, what the vendor says about retention, and whether your own policies permit the upload. Where you are unsure, test with synthetic or redacted data first and escalate to your data protection lead before using live records.
Measure the time cost, not only the output quality
A tool that produces good output but requires lengthy checking may save no time at all. Time your baseline process, then time the same task with the tool, including the review and correction steps. Include setup, prompt adjustment, and the time spent fixing errors. Run this over several days rather than a single session, because novelty can inflate early results.
Compare the two totals honestly. If the assisted process is faster only when the reviewer accepts outputs without checking, the saving is an illusion that carries risk. Record how much checking is needed and whether that checking is sustainable for the person who will actually do it.
A short checklist before any adoption decision
Before approving a pilot or a purchase, confirm the following in writing. Does the success statement name a measurable outcome and a minimum threshold? Was the test run on your own representative data rather than vendor samples? Is the baseline timed under the same conditions? Have the failure types been reviewed by the people who will live with them? Do you know who in your organisation owns the output, and who can switch the tool off? Have you confirmed where data is stored and for how long, and checked that this fits your own obligations? Has someone outside the buying group read the results?
If most of these answers are yes, you have grounds for a limited pilot with a review date. If several are unknown, the right next step is more testing, not a larger contract. The discipline is modest but protective: it converts an exciting demo into a decision you can explain to your team, your advisers and your future self.