Recruitment

Personalised learning plans for every team member

An L&D lead could use AI to draft a personalised learning plan per employee, mapped to their role, level and stated growth goals.

People or L&D team A couple of months to value

The challenge

Learning plans are a great idea that rarely gets done well at SMB scale. Building one per person by hand is unrealistic, so most staff get a generic course catalogue and pick whatever looks fun.

The approach

1Capture each employee's role, level and growth goals through a short structured form.
2Use AI to map those goals to the firm's approved learning library and external resources.
3Draft a 6-12 month plan with milestones, owners and review dates.
4Have the line manager and employee review and agree the plan together.

Potential outcome

Every employee has a real, personalised plan instead of a generic catalogue.
Better uptake of approved learning resources.
Clearer signal at review time about who is progressing and how.

Tools used

LMS integration LLM with role library 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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