Accountancy

Cutting client onboarding from hours to minutes

An independent accountancy firm could use AI to extract data from messy client documents, draft welcome packs, and pre-fill HMRC forms.

Small to mid-sized practice A few weeks to value

The challenge

New client onboarding can eat up most of a working day per client. Junior staff often re-key details from PDFs, scanned letters, and bank statements into the practice management system, then draft bespoke welcome packs by hand.

The approach

1Map the existing onboarding workflow end-to-end and identify the most repetitive steps.
2Use a document-extraction AI to pull structured data from client PDFs and scans.
3Set up an AI assistant trained on the firm's tone of voice to draft welcome letters and engagement summaries.
4Keep a partner-in-the-loop review on every draft before it goes out.

Potential outcome

Onboarding time could drop from several hours to under an hour per client.
Junior staff freed up to handle more advisory conversations.
Quality stays consistent thanks to human review at the final step.

Tools used

Document extraction AI Custom GPT for tone of voice Practice management API

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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