Evaluating AI Tools for Your Team

Why most evaluations go wrong

The typical AI tool evaluation starts backwards. A team sees a compelling demo, gets excited about the technology, and then looks for a problem it might solve. This approach almost always leads to disappointment — either because the tool does not perform as well on real tasks as it did in the demo, or because the problem it solves was never a real priority.

A better approach starts with the work itself: where are the bottlenecks, the repetitive tasks, the places where quality is inconsistent? Only then should you ask whether an AI tool can genuinely help.

A practical evaluation framework

We recommend a four-stage process: Define, Shortlist, Pilot, Decide.

In the Define stage, you articulate the specific workflow problem you want to address, the success criteria you will use to judge a tool, and the constraints you are working within (budget, integration requirements, data sensitivity).

In the Shortlist stage, you identify candidate tools and assess them against your criteria on paper — looking at documentation, case studies, and independent reviews rather than relying solely on vendor claims.

Running a meaningful pilot

The Pilot stage is where most organisations either skip steps or lose rigour. A good pilot has a fixed duration, a defined set of tasks, clear metrics, and a control group or baseline for comparison. It should involve the people who will actually use the tool day to day, not just the most technically confident members of the team.

Document everything: what worked, what did not, where the tool needed human oversight, and how much time the supervision itself required. The goal is not to prove the tool works — it is to understand honestly whether it adds enough value to justify the cost and complexity of adoption.