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Recruitment

Engagement survey themes from thousands of free-text answers

A people team could use AI to cluster engagement survey free-text answers into themes, with quotes and sentiment, in hours instead of weeks.

People team supporting 100+ staff A few weeks to value

The challenge

Engagement surveys produce hundreds or thousands of comments. HR either skims them, reads only the loudest, or pays a consultancy to code them. Insight arrives months later, when momentum has gone.

The approach

  • 1Strip personally identifying information from comments before analysis.
  • 2Use AI to cluster comments into themes, with sentiment and example quotes.
  • 3Compare results against the previous survey to highlight what has moved.
  • 4Hand the structured output to HR for narrative and action planning.

Potential outcome

  • Survey insight available in days, not months.
  • Action plans grounded in what people actually said, not a summary of the summary.
  • Leaders can see trends across cycles without paying for a fresh analysis each time.

Tools used

Survey platform export Private LLM with clustering PII-stripping pipeline

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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