Recruitment

Drafting employee letters in minutes, not hours

An HR practitioner could use AI to draft offer letters, contract variations, leaver confirmations and references from a short structured intake.

In-house HR team A few weeks to value

The challenge

HR spends a surprising share of every week writing letters - offers, salary changes, flexible working outcomes, leavers, references. Each one is formulaic but legally sensitive, so it gets done by hand.

The approach

1Build a small library of approved letter templates with clear merge fields.
2Capture the specific case facts in a structured intake form, not a freeform email.
3Use AI to assemble the draft letter, with the right tone and clauses for the case.
4Always require a human HR sign-off; never auto-send to the employee.

Potential outcome

Letters turned around the same day instead of sitting in a queue.
More consistent wording across the team and across cases.
Lower risk of the wrong clause sneaking into a sensitive letter.

Tools used

Letter template library LLM with retrieval HRIS integration

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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