Running Your First AI Pilot in a High-Street Firm

At some point, every managing partner in a small firm hears the same pitch:

“We should run an AI pilot.”

The idea is attractive — but it raises immediate questions:

What exactly are we piloting?
Who will use it?
How do we keep risk under control?
How will we know if it worked?

This article offers a practical template for running an AI pilot in a high-street firm (think 3–20 fee-earners) without turning it into a never-ending science project.

1. Pick one or two tightly defined use cases

The biggest mistake is to pilot “AI” in general. Instead, choose one or two concrete workflows such as:

drafting weekly client updates in litigation matters;
summarising email threads for supervision;
building and maintaining matter chronologies;
time capture from email and documents.

Good pilot candidates are:

frequent (happen every week);
annoying (people already grumble about them);
low-to-medium regulatory risk when supervised; and
easy to measure (time saved, clarity improved, fewer write-offs).

Write down each pilot use case in one paragraph so everyone shares the same picture.

2. Choose a small, mixed pilot group

In a high-street firm, you do not need complicated governance structures. You do need the right mix of people, for example:

one or two partners who will actually use the tool;
2–4 fee-earners (mix of senior and junior);
one or two PAs or secretaries where workflows affect them.

Look for:

people who are open-minded but not starry-eyed about technology;
at least one sceptic whose concerns you take seriously;
someone who will act as a simple “product owner” — often a partner or practice lead.

Give this group explicit permission and time to experiment.

3. Set simple rules and guardrails before you start

Before anyone logs in, agree:

Approved tools: name the specific AI tool(s) in scope — for example, “the AI features inside OrdoLux plus Microsoft 365 Copilot”.
Data limits: what can and cannot be sent to AI (no private family WhatsApp screenshots, no entire bundles pasted into consumer tools, etc.).
Verification duties: fee-earners must check all authorities, facts and figures before sending to clients or courts.
Logging: where AI-assisted work will be recorded (for example, saving outputs into the matter file with a simple “AI-assisted” flag).

This can be captured in a one-page pilot protocol, linked to your wider AI policy and risk assessment.

4. Decide what success (and failure) look like

An AI pilot should have clear, boring metrics, such as:

average time to produce a first draft of a client update;
number of summaries or chronologies produced per month;
fee-earner satisfaction scores (“does this save you time?”);
qualitative notes on errors, hallucinations or awkward outputs.

Set expectations in advance:

“If we do not see any real time saving or quality improvement after three months, we will stop.”
“If we encounter serious hallucinations that are hard to control, we will pause and adjust prompts or workflows.”

The goal is not perfection, but useful, measurable improvement.

5. Run the pilot in normal work, not in a sandbox

Training exercises are helpful, but the real test is live matters, under supervision.

Practical approach:

ask pilot users to apply AI on a few chosen matters each week;
keep usage within the defined use cases;
require them to save AI-assisted outputs in OrdoLux (or your main system), with a short comment if something went notably right or wrong.

Supervisors should:

review key AI-assisted documents as part of normal file review;
hold short check-ins (“What worked? What failed? What did you stop using?”);
treat problems as learning points, not reasons to blame juniors.

6. Capture lessons and evolve prompts

Over the pilot period, patterns will emerge:

prompts that consistently produce good drafts;
situations where AI struggles (for example, very fact-heavy disputes, emotional client emails);
small workflow tweaks that make adoption easier.

A simple way to capture this is to maintain:

a shared “prompt library” in OrdoLux or your intranet; and
a running list of “do’s and don’ts” updated as you go.

At the end of the pilot, you should be able to say:

“Here are the 4–5 prompts we actually use” rather than a vague “we use AI sometimes”.

7. Decide your next move deliberately

After 2–3 months, bring the pilot group and key partners together to decide:

Scale: do we roll this out to more people for these specific use cases?
Refine: do we keep it but narrow or tweak the scope?
Pause: do we shelve this tool or workflow for now?

Document your reasoning, including:

risk incidents (if any) and how they were handled;
benefits you observed (time saved, clarity, better records);
any open questions for future pilots.

This makes it much easier to talk to regulators, insurers or clients about how you approached AI adoption.

Where OrdoLux fits

OrdoLux is being designed as a natural home for AI pilots in high-street firms:

common pilot use cases — email summaries, chronologies, time capture, drafting internal notes — already exist as workflows inside the matter file;
prompts and outputs are stored against matters, giving you a built-in audit trail;
you can start small with a handful of users and expand as confidence grows.

That way, “running an AI pilot” stops being a vague innovation project and becomes a controlled, well-documented experiment in how you already work.

This article is general information for practitioners — not legal advice, regulatory guidance or specific consulting advice for your firm.

Looking for legal case management software?

OrdoLux is legal case management software for UK solicitors, designed to make matter management, documents, time recording and AI assistance feel like one joined-up system. Learn more on the OrdoLux website.