Case Study: The Use of AI in the Legal Sector

This case study will explore how legal organisations and teams are embracing AI today and what that means for the for the future of legal practice.

AI is rapidly reshaping the legal sector. This traditionally cautious industry has now embedded it into the day-to-day 2operations of law firms and legal departments around the world. From automating time-consuming document reviews to conducting complex legal research in a matter of seconds, AI is enabling legal professionals to work more efficiently than ever before.

This case study will explore how legal organisations are embracing AI today and what that means for the future of legal practice. We begin by examining the key areas where the sector is currently deploying AI- including legal research, contract analysis, document drafting and firm administration. We then cover the challenges that come with adopting AI, such as data privacy and hallucinations, and how firms can resolve them.

What’s driving this change? To put it simply, legal work involves an enormous volume of reading, writing, and analysis. These are exactly the kinds of tasks where AI can alleviate the workload, and where we’re seeing the biggest changes within the industry

The Current State of AI Adoption:

AI adoption in the legal profession is growing but uneven. Recent research suggested that 62% of UK practitioners are ‘active, regular users of integrated AI (https://www.legalfutures.co.uk/associate-news/uk-law-firms-lead-the-world-in-ai-adoption). The UK is not merely keeping pace with global trends; it is leading them, with adoption rates and reported efficiency gains consistently ahead of other regions. British firms such as Linklaters, Freshfields and Charles Russel Speechlys have become standard-bearers, investing heavily in AI strategies and dedicating specialist teams to embedding technology into their practice.

What firms are currently doing with it:

Legal Research

What once took hours of database searching, AI powered research tools from the likes of Thomson Reuters, LexisNexis, Harvey and Deep Judge can accomplish tasks in a fraction of the time. By finding relevant case law, legislation, and judicial opinions, these tools help lawyers to focus their attention on strategy rather than searching for materials.

Contract Review & Analysis

Contracts structured, rule-bound nature makes them a natural fit for AI use. AI Tools can now review contracts at speed, flag unusual clauses, identify risk, and extract key terms automatically. It is one of the most mature use cases in legal AI, and the numbers reflect that as contract analysis usage within legal teams rose by 17% between 2024 and 2025, while case law summarisation jumped by 34%.

Document Drafting & Correspondence

More than half of legal professionals now use AI to help draft correspondence. Whether it’s producing a first draft, adjusting tone, or proofreading a finished letter, AI has become a reliable writing partner for day-to-day legal communications.

Predictive Analytics

Some firms are going further by using AI to analyse historical case data and forecast outcomes, estimate timelines, or build a picture of how a particular judge or opposing counsel tends to behave. The technology is still maturing, but forms that are investing now are finding that legal data can be a genuine strategic asset.

Firm Operations & Administration

Beyond the legal work itself, AI is also reducing friction behind the scenes — optimising scheduling, streamlining billing, and helping firms set more competitive pricing. Less time on admin means more time on clients.

The Risks That Come With It

Adopting AI in legal work comes with real pitfalls, and law firms must approach them with clear eyes.

The most talked-about issue is hallucination — AI models confidently fabricating case citations that don’t exist. AI hallucinations have already led courts to sanction attorneys for submitting invented references, which places real strains on judicial resources. Any serious legal AI tool needs robust human oversight baked in.

Data privacy is the other big concern. Legal professionals handle some of the most sensitive information imaginable, and sharing client data with third-party AI software can put it at risk and jeopardise client confidentiality. It’s no surprise that 43% of legal professionals say integration with trusted software is their top priority when evaluating AI tools — and 33% say it’s critical that any tool genuinely understands their firm’s specific workflows.

These aren’t reasons to avoid AI. They’re reasons to do it carefully.

A recent Fidra Build: “Legal Assistant Tool”

Customer Case Study: Contract Review times slashed from weeks to hours!

For one of our recent customers in the Technology Reseller sector, contract review had become a time-consuming and knowledge-intensive process. An overburdened legal team were manually comparing third-party contract amendments, reviewing tracked changes, and relying on individual experience or fragmented document repositories to determine whether specific clauses or concessions had been accepted in previous negotiations.

This created a significant operational challenge: valuable legal precedent was difficult to access, risk assessment was inconsistent, and legal professionals were spending too much time on document comparison rather than strategic negotiation and decision-making.

To address this, we designed and built an AI-powered “Precedent Engine” that combines document analysis, semantic search, and knowledge graph technology to provide instant insight into contract changes and associated risks.

Historical contracts, negotiation drafts, and signed agreements were ingested into a secure Azure environment and transformed into a searchable knowledge base. The solution uses a hybrid Retrieval-Augmented Generation (RAG) architecture, combining vector search to identify similar clauses and a knowledge graph to track how contractual language evolved through negotiations.

Through a Microsoft Word integration and supporting web interface, legal users can upload a contract and immediately receive clause-level analysis, matched precedents, risk assessments, and AI-generated recommendations directly within their existing workflow.

Given the sensitive nature of legal documentation, data privacy and governance were central to the solution design. All contract data remains within the client’s Azure tenant, ensuring complete data sovereignty and preventing information from being used to train public AI models.

To minimise hallucinations, the AI is grounded in retrieved evidence from historical signed agreements and baseline contract templates, with every recommendation linked back to specific precedents rather than relying solely on model-generated reasoning.

Human oversight remains a critical control point throughout the process; the system acts as a decision-support tool rather than an autonomous legal reviewer. Legal professionals retain full responsibility for approving recommendations, validating risk assessments, and determining negotiation strategy, ensuring that AI accelerates legal review while maintaining professional judgement and accountability.

The Bottom Line

AI isn’t replacing lawyers – it’s giving them more time to do the work that actually requires a lawyer. The firms and legal teams that are pulling ahead aren’t the ones chasing the most cutting-edge tools; they’re the ones being thoughtful about where AI genuinely fits into their workflows, and building or choosing tools that earn the trust of the people using them. AI will continue to shape the legal sector in years to come, but good progress is already being made.

Talk to us to find out more.

Fidra Analytics – helping organisations deploy AI that delivers real business value.

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