UK top 20 law firms race ahead in AI as strategy lags and next tier plays catch-up

Big UK law firms out in front in the AI tech race

Top UK firms are moving fast on artificial intelligence. New data indicates the leaders are widening the gap through tool rollouts, training, and visible client-facing initiatives.

What the numbers say

Almost eight in ten of the top 20 firms have deployed third-party AI tools. Nearly half have built their own systems or partnered on custom versions.

More than half are putting lawyers through AI training. Six firms have invested in AI start-ups or launched in-house incubators.

It's visible to clients too: all of the top 20 have published reports or hosted AI-themed events, and all but one are actively marketing their tech. In the next 20, only 60% are promoting AI externally and just half have invested in third-party tools.

Across the wider top 40, 78% now advertise AI use, up from 60% last year. Among the elite, firms with a head of AI rose from 35% to 45% in a year, and over three-quarters have digital transformation teams. Lower down the list, only a third have a dedicated AI function.

The strategy gap

Adoption is rising faster than planning. Only 24% of UK law firms have a formal AI strategy, while 43% are pushing ahead without one.

"An increasing number of law firm customers recognise AI's potential to enhance both productivity and client service. We expect that organisations will increasingly pick their panel law firms based on the firm's strengths in delivering AI-powered legal services."

"The UK's very largest law firms are responding to this pressure to adopt and integrate AI tools - but the next tier of law firms appears to be adapting more slowly. This is creating a perceived gap in AI-expertise that slightly smaller firms will want to close."

What this means for your firm

Clients will compare firms on measurable AI capability, not talk. Treat this like any core service line: set goals, fund it, assign owners, and report outcomes.

A practical plan to close the gap

• Set your AI strategy: Define where AI will reduce cycle time, improve accuracy, or open new offerings. Identify target matters (e.g., DD, disclosure, research, drafting) and success metrics.
• Governance and accountability: Appoint a head of AI and a cross-functional committee (IT, risk, KM, pricing, BD). Approve tools, data use, and quality controls.
• Training at scale: Provide baseline training for all fee earners and staff, with advanced tracks for practice champions and KM. Tie completion to matter permissions for AI-enabled workflows.
• Vendor and model due diligence: Assess data security, privilege, audit trails, and indemnities. Set usage guardrails and human-in-the-loop review for client work.
• Pilot, measure, productise: Run short pilots with clear KPIs (cycle time, accuracy, write-offs). Convert wins into repeatable playbooks and matter templates.
• Client communication: Update engagement letters and service descriptions to explain AI use and review processes. Co-develop pilots with key clients and cite results in RFPs.
• Pricing and economics: Align AFAs with AI-enabled delivery. Capture efficiency as margin, not blanket discounts; be explicit about pass-through tech costs.
• Knowledge and data: Maintain clean clause libraries, precedents, and playbooks to feed AI systems. Establish versioning and approval workflows.
• Risk and compliance: Address confidentiality, conflicts, and data protection. Maintain logs, sampling, and QA for AI-assisted outputs. See the UK ICO's guidance on AI and data protection for practical controls here.
• Talent: Hire or upskill KM engineers, solution architects, and data specialists. Incentivise practice leaders to embed AI into delivery.
• Investment approach: Decide where to build, buy, or partner. Use incubators or vendor partnerships where it accelerates time-to-value.

Signals clients will look for

• Named leadership (head of AI) and a visible governance framework.
• Documented use cases with metrics and client-safe case studies.
• Clear policies on confidentiality, data handling, and human review.
• Evidence that AI reduces turnaround time and improves consistency without added risk.

Recommended resources

• Thomson Reuters insights on legal tech and AI legal research hub.
• Structured AI training paths for legal teams Complete AI Training: Courses by job.

The takeaway is simple: the top firms are turning AI from experiments into disciplined delivery. If you want to stay on a client's panel, build the plan, show the metrics, and make AI a reliable part of how your teams work.