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Recruitment

Personalised onboarding packs for every new joiner

An HR team could use AI to generate a tailored onboarding pack per role, manager and location instead of sending the same generic PDF to everyone.

HR team supporting a growing business A few weeks to value

The challenge

Generic onboarding packs miss the things a new joiner actually needs - their team's quirks, the right systems, the right people to meet first. New hires take longer to get productive and HR spends week one on follow-up questions.

The approach

  • 1Capture structured intake from the hiring manager: role, systems, team, first projects.
  • 2Use AI to assemble a personalised week-one pack from approved building blocks.
  • 3Include suggested meetings, recommended reading and a clear 30-60-90 plan.
  • 4Hand to HR and the manager for a quick review before send.

Potential outcome

  • New joiners productive faster.
  • Lower volume of week-one questions hitting HR and managers.
  • More consistent onboarding quality across teams.

Tools used

HR system integration LLM with template library Document assembly

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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