Generative AI in Legal Industry
Use Cases, Risks & Real Examples
For decades, the legal industry has run on endurance. Endless hours parsing dense contracts, scanning discovery documents, and keeping pace with shifting regulations. It’s meticulous work. Essential, but often exhausting.
Now, the pressure has only intensified. Clients expect faster turnarounds at lower costs, while regulators introduce new mandates before old ones are fully absorbed. At both global firms and lean in-house departments, teams are stretched thin, trying to balance accuracy with efficiency.
Traditional legal tech—once an efficiency booster—can no longer keep pace with the complexity of today’s legal landscape. This is where generative AI in legal industry is transforming workflows. It is helping legal teams manage complexity, reduce manual burden, and deliver value faster.
Of course, the age-old concern remains: can AI ever reflect a lawyer’s judgment, or will it replace their perspective?
The answer is no.
Generative AI doesn’t replace expertise. It rather accelerates groundwork cutting hours so lawyers can focus on client strategy, risk navigation, and high-stakes decision-making.
Let’s understand the what, how, and why of the role of gen AI in the industry.
Key Highlights
- Generative AI in legal industry streamlines contract drafting, research, e-discovery, compliance, and client communication.
- Real-world examples: Linklaters (AI Sandbox), Allen & Overy (Harvey), Relativity (aiR), Thomson Reuters (Westlaw Precision).
- Key risks: hallucinations, confidentiality breaches, IP ownership issues, and ethical responsibility.
- Adoption best practices: start small, train lawyers in prompting, choose legal-specific tools, and keep humans in the loop.
- Payoff: efficiency gains, faster client service, cost advantages, and talent leverage.
What is Generative AI in Legal Industry?
Generative AI in the legal industry refers to advanced AI systems. Most notably, large language models (LLMs) can create new content from vast amounts of legal data. Unlike traditional automation tools that handle repetitive clicks or form-filling, generative AI, in many ways, mimics the first-pass work of a junior associate.
The only difference is that AI does it in seconds rather than hours.
Think of it this way:
- Traditional automation helps lawyers move faster by taking over repetitive clicks—routing emails, filling templates, or pulling data.
- Generative AI helps lawyers think faster by creating first drafts of documents, condensing a 40-page contract into a two-page summary, or surfacing relevant precedents from thousands of cases.
This distinction matters a lot. Automation speeds up the process.
Is speed enough? Again, no.
Legal practice requires more than speed. Trusted data, confidentiality, and professional oversight remain non-negotiable. A generative model trained on uncontrolled sources can hallucinate or produce biased outputs.
That’s why leading firms use curated, secure datasets and enforce a “human in the loop” approach. AI proposes, lawyers validate. It ensures that accountability stays squarely with the legal professional. It’s a new tool and a layer of intelligence woven into the way legal teams research, draft, and advise.
Generative AI Use Cases in the Legal Industry?
Generative AI is already reshaping how lawyers work every day by accelerating precision tasks that consume most of their time. Here are the most impactful applications in practice:
Contract Drafting & Review
Instead of starting from a blank page, AI can generate first drafts of contracts, NDAs, or clauses aligned with standard templates. More importantly, it can flag unusual terms, missing obligations, or risky language within seconds.
Result:
Lawyers spend less time chasing boilerplate and more time negotiating the details that actually matter.
Legal Research
Generative AI can sift through thousands of cases, statutes, and rulings to surface the most relevant precedents in plain language. Unlike keyword search, it summarizes context, highlights reasoning, and explains applicability.
Again, cutting research time from hours to minutes. Great as a junior associate who never tires or misses a citation.
Result:
Lawyers still validate the findings, but the heavy lifting is done.
E-Discovery & Document Review
In litigation, the volume of documents can be overwhelming for human reviewers.
AI models can categorize, cluster, and summarize datasets, helping teams quickly identify privileged communications, relevant evidence, or patterns across terabytes of files. What once took armies of junior associates can now be closed in days.
Result:
Legal teams experience reduced review time and minimum oversight errors.
Compliance & Risk Management
For in-house legal teams, Generative AI is no less than a compliance analyst.
It can monitor regulatory updates across jurisdictions, automatically map them against existing policies, and highlight gaps before audits do.
Result:
Shift in compliance—from reactive firefighting to proactive governance.
Litigation Support
Be it drafting arguments ot summarizing witness statements, generative AI helps trial teams organize complex case materials into digestible briefs.
It can simulate alternative positions, test reasoning against precedent, or create outlines for courtroom presentations.
Result:
Legal firms save valuable prep hours without replacing human advocacy.
Client Communication
Lawyers are trained in precision, but clients want clarity.
Generative AI can translate complex legal terms into plain-English summaries, create FAQs for common queries, or even draft status updates on case progress.
Result:
Better-informed clients and fewer hours spent on routine correspondence, freeing lawyers for higher-value discussions.
Pro Tip:
Generative AI delivers maximum ROI when integrated into existing case management and document systems rather than as a standalone tool.
Take a look at our Generative AI development services to understand how we design, build, and deploy Gen AI solutions for enterprises.
Generative AI Adoption in Law Firms vs In-House Teams
Not all legal environments face the same pressures. A global law firm advising on cross-border M&A operates very differently from a lean in-house team managing hundreds of contracts a month. The adoption paths for generative AI reflect those differences.
Gen AI in Law Firms
For law firms, generative AI offers a way to scale expertise without scaling headcount. They can use it as a more strategic advantage. Deliver more work in less time while freeing partners and associates to focus on bespoke, high-value advice.
- Document automation: Drafting first versions of contracts, pleadings, or due diligence reports at speed.
- Knowledge management: Turning decades of case notes, memos, and internal know-how into searchable, conversational insights.
- Client-facing value: Faster response times and cost savings that can be directly passed on in fee structures.
Gen AI in In-House Legal Teams
Corporate legal departments face a different challenge—volume and velocity. Here, generative AI acts as a force multiplier for small teams under resource constraints. The payoff is agility-–legal teams can keep pace with the business instead of acting as a bottleneck.
- Contract lifecycle management: Reviewing NDAs, vendor agreements, and procurement contracts that arrive daily.
- Compliance monitoring: Keeping up with new regulations across multiple jurisdictions and industries.
- Quick-turnaround tasks: Drafting policies, summarizing risks for executives, and responding to internal queries.
Hire Our Generative AI Developers for Your Legal Firm.
Real-Life Examples of Gen AI in the Legal Industry
You need not just take our word. Here is a deeper look at some successful implementations by popular companies in the legal sector:
Linklaters
Linklaters has established an AI Sandbox to accelerate the development and delivery of AI-based solutions across its business. The goal of the sandbox is to rapidly build out Gen AI solutions, many of which originate from ideas suggested by the firm's personnel.
- Implementation Strategy: Ideas generated through the firm's ‘AI ideas campaign’ are evaluated by a cross-functional team. Shortlisted concepts are then developed into working prototypes.
- Delivery Mechanisms: These prototypes and tailored solutions are built within the firm’s homegrown Gen AI chatbot, Laila, or developed in partnership with external vendors.
- Scale of Use: Over the last 18 months, Linklaters has consistently improved Laila, which is currently handling over 60,000 prompts in a week.
- Focus Areas: Ideas progressing through the innovation process include solutions focused to automate initial steps in regulatory analysis and build credentials based on lawyer profiles and deal experience. They are also considering the automatic generation of matter metadata and credentials from documents. The firm is also working to use AI for search, extraction, analysis, and drafting.
- Development Support: The Linklaters team is assembling a "workbench of best-in-class AI solution developers" to build these ideas consistently. They have started with tech consultancies, like Simplexico and Springbok AI, to support the design, testing, and delivery of prototypes.
Allen & Overy
Allen & Overy (A&O) partnered with Harvey AI on February 16, 2023. Harvey is designed to automate and enhance various aspects of legal work using natural language processing (NLP), machine learning, and data analytics.
- Technology Foundation: Harvey is a verticalized version of what is understood to be GPT-4, further trained using legal sector-specific data.
- Functionality and Scope: Harvey assists lawyers to conduct research and ensure due diligence using natural language instructions. It has been rolled out across A&O’s 43 offices.
- Key Capabilities: Harvey can automate aspects of legal work, such as contract analysis, litigation, due diligence, and regulatory compliance. It generates insights, recommendations, and predictions based on large volumes of data.
- Efficiency and Intelligence: A&O suggests that Harvey enables lawyers to deliver "faster, smarter, and more cost-effective solutions". During the trial, approximately 3,500 A&O lawyers utilized Harvey for around 40,000 queries related to client work. Harvey can work in multiple languages and across diverse practice areas, delivering unprecedented efficiency and intelligence.
- Drafting and Oversight: Harvey is integrated into workflows and can generate legal documents, not just text. However, safeguards are in place at the model level, and crucially, a lawyer is always in the loop, reviewing all output to screen for potential errors, such as "hallucinations" (producing inaccurate or misleading results).
Relativity
Relativity introduced air for Review to transform the eDiscovery process. AI in eDiscovery generally aims to enhance the accuracy and efficiency of managing document review, internal investigations, and regulatory compliance.
- Application Focus: aiR for Review is an advanced AI-driven tool deployed for document review. It applies precise and intuitive document categorization, improving upon traditional eDiscovery methods.
- Efficiency and Cost Savings: AI significantly speeds up the review process by processing millions of documents in a fraction of the time required by human reviewers. This capability boosts cost efficiency by automating routine tasks, allowing human reviewers to concentrate on more complex, high-level analysis.
- Accuracy and Consistency: By applying uniform criteria to every document without fatigue, AI minimizes the risk of human error and overlooked information.
- Guidance and Customization: Subject matter experts can guide the aiR process using natural language prompts. The tool is implemented to meet the specific needs of each case, regardless of whether it involves a small number of documents or millions.
- Transparency and Defensibility: AI in eDiscovery improves the transparency of review by providing clear, data-driven justifications for document classification. With aiR for Review, each AI decision includes a clear explanation, enabling legal teams to verify and trust the outcomes. The standardized, explainable process strengthens the overall integrity and defensibility of discovery.
Thomson Reuters
Thomson Reuters has incorporated generative AI technology into Westlaw Precision to revolutionize legal research. This integration utilizes its ability to generate human-like text and improve natural language processing.
- Enhanced Legal Research: The generative AI updates harness the technology to provide more in-depth and contextually relevant search results, saving legal professionals valuable time.
- Document Analysis: The AI technology significantly improves Westlaw Precision's capacity to analyze and summarize legal documents. It makes accessing critical information easier and quicker for legal professionals, particularly for case law research and review.
- Predictive Analytics: Westlaw Precision uses Gen AI to help predict legal outcomes by analyzing historical data and case law. It offers insights that enable legal professionals to make more informed decisions and counsel clients effectively.
- Document Creation: Gen AI streamlines document creation by generating templates, clauses, and even complete legal documents, which improves document accuracy and saves drafting time.
- Natural Language Interaction: The platform allows more natural and conversational interactions. Legal professionals can use plain language queries to access information, thereby reducing the learning curve for new users.
Adopting Generative AI in Legal Workflows
The biggest mistake legal leaders may make with generative AI is treating it like an all-or-nothing leap. In reality, adoption works best in small, controlled pilots that prove value without disrupting existing processes.
Here are some helpful tips when adopting Gen AI in your legal workflows:
Start Small, Prove Value
Begin with high-volume, lower-risk tasks—like first-draft contract reviews or summarizing case law. These areas deliver quick wins, showing teams what AI can (and can’t) do well.
Train Lawyers, Not Just Models
Generative AI is only as effective as the prompts it receives. Train teams on prompting skills. How to ask, refine, and validate AI outputs helps lawyers catch hallucinations early and build trust in the technology.
Choose Trusted Partners
Select vendors with a strong legal pedigree (e.g., Thomson Reuters, Relativity, LexisNexis) rather than generic AI tools. Domain-specific solutions are built with legal data, confidentiality, and compliance in mind.
Keep Humans in the Loop
AI can accelerate drafting, review, and research, but professional responsibility stays with the lawyer. Establish a clear workflow where AI suggests, and humans verify. The balance reduces risk while preserving accountability.
Let’s get you answers.
Risks and Ethical Considerations
As aforementioned, generative AI promises speed and efficiency, but in the legal industry, accuracy and accountability are non-negotiable. Every adoption conversation must therefore address the risks head-on:
Hallucinations and Unreliable Outputs
Gen AI can produce confident answers that are factually wrong or legally irrelevant. In law, even a small error in citing precedent or interpreting a clause can have significant consequences. That’s why AI should draft, but humans must decide—the model accelerates work, but final accountability belongs to the lawyer.
Data Privacy and Confidentiality
Legal work involves highly sensitive data, including client communications, trade secrets, and financial information. Feeding this into public AI models risks breaches or unintentional exposure. Trusted deployments use private, secure environments with strict data-handling protocols, ensuring no client data leaks into external training sets.
Intellectual Property Ownership
When AI generates text, who owns it—the firm, the client, or the tool provider?
The answer varies by jurisdiction and contract terms. Law firms need clear policies on ownership and licensing of AI-generated content, especially when drafting client-facing work.
Human Oversight and Professional Responsibility
Ethics rules make it clear: lawyers cannot outsource judgment. Generative AI may analyze, summarize, or propose, but the professional remains responsible for ensuring accuracy, fairness, and compliance with the law. The best implementations follow a human-in-the-loop model, where AI augments expertise rather than replacing it.
Aegis Softtech: Balancing Gen AI Innovation with Responsibility
While many fear that artificial intelligence (AI) will replace lawyers, it is not the case. It, in fact, is reshaping how legal work gets done. Drafting, reviewing, and researching may no longer require hours of manual effort, yet the judgment, strategy, and client trust that define legal practice remain firmly human.
The firms and legal departments that act now are already seeing a competitive edge:
- Efficiency gains from automating first drafts and research.
- Strategic advantage in pricing, responsiveness, and client service.
- Talent leverage by freeing up lawyers for higher-value, complex matters.
Now is the time to start building your Gen AI adoption roadmap.
Identify the workflows where AI creates measurable value, select trusted solutions, and train teams to partner with the technology effectively. Generative AI can raise the bar for legal practice, but without governance, it introduces.
You need a partner who believes responsible adoption is the foundation for trust.
Because, innovation must move hand-in-hand with responsibility, deploying AI where it accelerates work, while ensuring human oversight, ethical safeguards, and client confidence never slip.
At Aegis Softtech, we help legal organizations achieve that balance. With two decades of experience and full-stack Gen AI development expertise, we design secure, compliant, and ethical AI solutions tailored to your legal workflows.
Frequently Asked Questions
Generative AI in the legal industry is used for contract drafting and review, legal research, e-discovery, compliance monitoring, and client communication. It reduces manual work while improving speed and accuracy.
Law firms and in-house teams use legal-specific AI platforms such as Harvey, Thomson Reuters’ Westlaw Precision, Relativity aiR, and Lexis+ AI. These tools are designed for confidentiality, accuracy, and legal workflows.
The key legal issues include data privacy, confidentiality, hallucinations, and intellectual property ownership. Human oversight is critical to ensure compliance and professional accountability.
Firms can grow by automating routine work, improving response times, and offering cost-efficient services. Generative AI lets lawyers focus on high-value strategy while scaling everyday tasks.