UK AI Adoption: Setting Evaluation Criteria Before You Compare Model Providers
Most UK founders who start evaluating AI model providers begin with the wrong list. They open a dozen browser tabs, read the headline benchmarks, watch a polished demo and then try to decide which system feels most impressive. The problem is that impressiveness is not a criterion. A model can write fluent summaries and still be the wrong choice if your data cannot go where it needs to go, if your team cannot predict what it will cost at volume, or if its outputs cannot be checked by anyone on your side. This article sets out a different starting point: write down what your business needs from an AI provider in plain terms first, then test each candidate against that list. The approach is deliberately unglamorous, and that is its strength.
Start With the Job, Not the Model
Before you look at any provider, describe the job in one sentence that a non-technical colleague could understand. For example: draft first-pass replies to inbound support emails, with a human approving each reply before it is sent. Or: extract invoice line items from supplier PDFs into the accounting system for review. The sentence should name the input, the output, and who checks the output. If you cannot write that sentence, the pilot is not ready, no matter how capable the model looks.
Write down what a bad output would cost. A misworded marketing blurb is an inconvenience. A wrongly extracted payment detail is a much bigger problem. The cost of error determines how much checking you need and therefore how much the workflow is worth to you.
Define Data Handling Questions Before Anything Else
Your evaluation should begin with questions about what happens to the data you send. Ask the provider in writing where data is processed and stored, whether inputs are used to train models by default, how long prompts and outputs are retained, who within the provider can access them, and what options exist for opting out or deleting data. Read the actual terms rather than the summary page, and note the document version and date you reviewed.
Check whether the answers apply to the plan you are considering. Enterprise and consumer tiers often differ. Also confirm whether any third-party tools in your chain, such as a browser extension or workflow platform, sit between your staff and the model and what they do with the content passing through them. Do not assume a provider's security reputation answers these questions for your specific setup.
Build a Comparison Grid Using Plain Criteria
Once the job and data questions are clear, build a simple grid. Use plain column headings such as: data terms in writing, cost at expected volume, output accuracy on our own samples, ease of human review, integration effort, exit route, and who internally owns the decision. Score each candidate with short notes rather than numbers that look precise but hide judgement.
Testing accuracy on your own samples matters more than any published benchmark. Collect twenty to fifty real, anonymised examples of the task. Run each candidate on the same set. Have the person who will actually review outputs mark the results. Record where the model was wrong, not just where it was right, because the error pattern tells you how much oversight the workflow needs.
Cost deserves careful modelling. Calculate expected monthly usage in units the provider charges for, then multiply by a realistic growth assumption. Check whether pricing depends on context length, output length, or request volume, and whether there are minimum commitments. Do not present any figure to the board until it has been reconstructed from the provider's own published rates.
A Practical Checklist for the Final Decision
Before you sign, confirm that you can answer these questions with evidence rather than memory: What exact job will this tool do, and who approves its output? Which data types are permitted, and have we confirmed the terms for our plan? What did the pilot show on our own samples, including errors? What will this cost at the volume we expect in twelve months? Who on our team can switch it off, and how quickly? If the provider changed its pricing or terms next quarter, what would we do? What would migrating our prompts, logs and workflows to another provider involve?
Record the answers in a short decision note with the date, the people involved and the documents reviewed. This note becomes the reference point when someone later asks why you chose this provider, and it makes the next evaluation faster.
The method is simple: define the job, pin down the data terms, test on your own material, model the real cost and keep a written record. Founders who follow it tend to make fewer expensive reversals, and they spend their attention on the workflow itself rather than on whichever provider had the most persuasive demo this month.