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Recruitment

Performance review summaries from year-round signals

A people team could use AI to summarise a year of feedback, project notes and one-to-ones into a fair first-draft performance review.

People team supporting 50-500 staff A couple of months to value

The challenge

Managers either rush reviews the night before or rely on the last month's memory. Recency bias creeps in, feedback feels inconsistent across the team, and staff lose trust in the process.

The approach

  • 1Pull structured signals from one-to-one notes, project retros and peer feedback with consent.
  • 2Use AI to summarise themes, wins and growth areas per person.
  • 3Generate a first-draft review for the manager to refine, never to send unedited.
  • 4Keep employees in control with full visibility of what feeds the summary.

Potential outcome

  • More balanced, evidence-based reviews.
  • Less recency bias and a fairer process across managers.
  • Reviews completed on time, not in a panic.

Tools used

One-to-one tooling Private LLM with retrieval Performance management system

How a sensible pilot could look

The fastest way to test a use case like this is a tightly scoped 30-day pilot rather than an open-ended rollout. The shape we recommend in almost every UK SMB is the same: one workflow, one owner, one success metric, one decision date. The point is to learn quickly and cheaply, not to transform the business in month one.

In week one, map the current workflow end to end and time it. This baseline is non-negotiable - without it, you can't tell whether the AI made things better, worse, or about the same. In week two, set up the tool and train two or three people deeply rather than rolling it out widely. In week three, run the new workflow alongside the old one and capture friction in writing. In week four, review the data, decide go or no-go, and write up what you learned.

Even a no-go is a successful pilot if you understand why. The worst outcome is a 'maybe' that drags on for another month and quietly absorbs the budget.

What to watch out for

  • Picking too broad a workflow. Narrow it until it almost feels too small - you can always widen later.
  • Skipping the baseline. If you don't know what 'before' looked like, you'll argue about whether 'after' is better.
  • Removing the human review too early. Almost every successful AI rollout in an SMB keeps a person on the final decision for far longer than the vendor suggests.
  • Letting the success metric become a feeling. 'The team likes it' is not a metric. Time saved, error rate, response time, conversion - pick something measurable.
  • Pasting client or customer data into a public tool. Use the approved one, or don't paste it at all.

Questions worth asking before you start

  • Who owns this? Not a steering group - one named person whose job it is to make this work, with the authority to change the workflow rather than just observe it.
  • What does success look like in numbers? Pick one metric, write it down on day one, and don't change it mid-pilot.
  • What data is the AI allowed to see? Be explicit about what's in scope, what's out of scope, and where outputs are stored. Document it before the pilot starts, not after.
  • What happens at day 30? Diary the go / no-go meeting now. Invite the people who can actually decide, not just the people who'll be in the room anyway.
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