AI Agent Use Cases for Legal Industry: Cut Costs, Work Smarter

Lawyers lose thousands of dollars in revenue due to inefficiency. Documents pile up, research drains hours, and contracts sit in endless review cycles.

Meanwhile, your competitors move faster.

AI agent use cases for the legal industry are reshaping how law firms operate by automating tedious and repetitive tasks, freeing your lawyers to focus on strategy and client relationships. Rather than hiring more staff, forward-thinking firms deploy AI agents that scan contracts in seconds, analyze case law in minutes, and automatically recover lost billable hours. The shift is seismic.

Discover the seven AI agent use cases transforming legal practices today and how your firm can capture a competitive advantage without replacing lawyers.

How AI agents are transforming legal workflows

Legal work used to mean a lot of paperwork with contracts piling up, research taking days, and billing errors costing thousands. However, nowadays, AI agents in the legal industry are making lawyers' jobs much easier. 

While traditional legal tech performed single tasks where you'd run a search, get results, then do the next thing manually, an AI agent functions as a collaborative team member and handles end-to-end processes without constant supervision. 

They can plan, reason, and execute across multiple applications.

With the enterprise AI agents market projected to grow at a 45.82% CAGR through 2034, bar associations now permit the use of supervised agents. This decision marks a new era of innovation in the legal landscape, driven by the use of AI.

With Agentic AI, agents can make decisions and complete legal workflows after receiving initial instructions. These systems break down complex tasks into subtasks, execute them, and evaluate progress, much like a human would.

Additionally, an AI agent can also take care of document review, when it doesn't just flag relevant documents; it also:

• Identifies them
• Categorizes by privilege
• Extracts key information
• Organizes evidence
• Tracks deadlines

All without stopping to ask what comes next.

With AI in the picture, legal teams handle more cases without proportionally increasing staff. Firms can expand services or tackle more complex work without straining resources. More time is devoted to nuanced decision-making, creative strategy, and relationship-building with clients, rather than doing repetitive tasks.

Prepare Your Firm for the Future

Delay adoption and competitors will gain a decisive advantage.

Book a Call

AI agent use cases for the legal industry

Use case 1: Contract review and analysis automation

Contract review used to eat up 40-60% of a lawyer's time, but now, AI agents can scan hundreds of pages in seconds. It can reduce review time for 80-page MSAs to under 5 minutes and save 99% of time when responding to business queries.

Here’s how it works:

• AI ingests and digitizes documents in various formats, extracting critical information such as party names, dates, and monetary amounts.
• It recognizes and organizes sections such as indemnities, liabilities, terminations, or confidentiality terms. In a vendor contract, the agent may highlight an indemnity clause that limits liability to the total value of the contract.
• Next, it evaluates terms and highlights red flags or unusual provisions that deviate from standard practices. For example, an employment contract may trigger concerns about overly restrictive non-compete clauses.
• Compliance checks follow, with AI cross-referencing contract terms against applicable regulations. For example, in a data processing agreement, AI checks for GDPR-compliant clauses, such as data subject rights and breach notification timelines.
• It can also compare multiple versions of contracts to detect changes, additions, or deletions.
• Lastly, it generates concise summaries of lengthy contracts, outlining critical terms and obligations.

Use case 2: Legal research and case law analysis

Traditional legal research consumed 17-28 hours per matter, but AI agents now reduce that to 3-5.5 hours. AI revolutionizes legal research by searching vast databases, identifying relevant precedents, analyzing case patterns, and producing synthesized summaries in minutes rather than hours. 

Some firms report reductions of 70% in research time, and legal professionals increased productivity by up to 50% due to faster access to relevant legal information.

Here's how it works:

• AI searches databases using context-aware algorithms that understand legal language, not just keywords. It identifies case relationships and extracts insights that traditional keyword searches miss.
• It analyzes case patterns to uncover trends and correlations, spotting connections across jurisdictions and highlighting how courts have treated similar arguments over time.
• The agent recognizes legal concepts beyond surface-level terminology, understanding that a case discussing "frustration of contract" is relevant even if the exact phrase isn't used.
• It generates structured summaries with facts, issues, arguments, court analysis, and conclusions automatically organized into clear sections.
• The technology tracks citations and shows how precedents have evolved, marking them as cited positively, negatively, or neutrally, which helps lawyers instantly gauge their legal weight.
• It integrates evidence with case law, connecting factual circumstances to applicable precedents and suggesting optimal litigation strategies based on comparable outcomes.

This frees lawyers from tedious archive searches and manual cross-referencing, enabling them to build sharper arguments with confidence.

Use case 3: Document drafting and generation

Document drafting used to mean starting from scratch or hunting through old files for templates. AI agents automate the entire process. 

From automating contract creation to correspondence by drawing from firm templates and past precedents, AI generates first drafts in minutes. This way, lawyers can edit templates rather than draft from blank pages, reducing workload by 80% while preserving firm-specific quality and legal standards.

Here’s how an AI agent works:

• Ingests your firm's templates and past documents to learn your preferred language, structure, and clauses.
• Suggests relevant language from clause libraries and adapts drafts based on specific case parameters, such as jurisdiction, parties, deal structure, or litigation context.
• Maintains consistency with firm standards by applying your style guides, numbering conventions, definitions, and formatting automatically.
• Customizes tone and language for various audiences, including judges, clients, and opposing counsel.
• Performs compliance and risk assessments, scanning documents for language that violates regulations such as GDPR or CCPA, and flags inconsistent terms or noncompliant clauses.
• Detects missing provisions, prevents gaps, and maintains version control across multiple drafts.

With these agents, you can convert any contract into a reusable template in minutes, with variables like names, dates, and jurisdictions detected automatically.

Use case 4: E-discovery and document management

AI agent for e-discovery and document management:

• Uses technology-assisted review (TAR) and predictive coding to automate document classification.
• Learns from attorney decisions and continuously improves accuracy as more documents are reviewed.
• Categorizes documents into responsive and non-responsive sets, identifying potentially privileged materials and significantly narrowing the scope of review before human review begins.
• Files documents into matter folders, automatically applies metadata tags, and tracks deadlines, thereby eliminating manual filing and reducing the risk of missed deadlines.
• Extracts key information from documents and organizes evidence by themes, parties, dates, or topics, making it easy for lawyers to locate critical materials.
• Performs timeline reconstruction, transforming disparate documents into coherent chronological narratives that help attorneys understand case development.
• Correlates evidence with applicable legal precedents, helping lawyers understand how similar cases were decided and what evidence proved most persuasive in those cases.

Experts predict document review, which comprised 64% of e-discovery spending in 2024, will drop to 52% by 2029 due to AI-driven efficiencies. Additionally, Generative AI-assisted review pricing ranges from $0.26 to $0.50 per document, offering significant cost savings compared to manual review. 

Use case 5: Compliance monitoring and regulatory tracking

AI agents serve as continuous watchdogs, enabling firms to adopt a proactive approach to minimize compliance violations and associated penalties. This is particularly critical for heavily regulated industries such as finance, healthcare, and energy, where regulations are constantly evolving. 

AI agents automate labor-intensive regulatory monitoring by:

• Continuously monitoring regulatory bodies, government websites, and industry publications in real-time.
• Detecting changes to laws and policies relevant to your practice areas.
• Comparing new regulations against your internal policies and existing contracts, identifying gaps or non-compliant language that requires attention.
• Scanning contracts to identify clauses violating privacy laws or data protection regulations, such as GDPR and CCPA
• Flagging issues like missing data, subject rights or inadequate breach notification timelines.
• Performing automated risk assessment, continuously scanning communications and documentation to identify potential compliance violations before they escalate into regulatory breaches.
• Generating actionable alerts that explain regulatory changes in plain language and recommend specific policy or contract updates needed for compliance.
• Translating complex regulatory language into compliance protocols tailored to your operations and risk profile.
• Providing audit trails and documentation, ensuring you can demonstrate compliance efforts if regulators inquire.

Use case 6: Predictive analytics and case outcome forecasting

Firms using AI’s predictive analytics capabilities report 70-80% improvement in case outcome forecasting accuracy. It empowers legal teams to anticipate case trajectories, adjust strategies proactively, and avoid costly surprises. 

To provide increasingly accurate forecasts, AI:

• Analyzes thousands of historical cases to identify patterns like:
  • Which arguments succeed before specific judges
  • What evidence proves most persuasive
  • How similar fact patterns typically resolve
• Evaluates judge behavior by studying their prior rulings, reversals, and settlement patterns, providing insight into how an individual judge is likely to rule on your case.
• Assesses opposing counsel's track record, including settlement tendencies, trial outcomes, and negotiating patterns, to inform your strategic decisions.
• Weighs case-specific factors, such as jurisdiction, claim type, damages sought, and evidence strength, to generate probability-based predictions about the likelihood of settlement versus trial success.
• Provides confidence levels on predictions, helping you understand the reliability of forecasts for different scenarios.
• Flags risk factors and suggests mitigation strategies based on patterns identified in comparable cases.
• Enables lawyers to model various case strategies, displaying the likely outcomes for each approach.

Note: Predictions provide probabilities, not guarantees; human judgment remains essential for informed strategic decisions.

Use case 7: Billing and time management optimization

Forward-thinking firms use AI to deliver more value in less time, differentiating on outcomes rather than hours. Nearly 40% of lawyers lose billable hours due to poor time management and timekeeping. 

AI agents recover missed time, helping firms capture 20% more billable hours while streamlining invoicing and reducing administrative burden. Here’s how:

• AI monitors lawyer emails, document edits, calendar events, and system activity to automatically identify billable work and log time entries without manual intervention.
• It categorizes time entries by matter, practice area, task type, and expense code, ensuring accurate billing and compliance with client-specific billing requirements.
• The agent generates invoices automatically, reconciling time entries with expense records and applying appropriate billing rates.
• It analyzes historical billing patterns to identify efficiency opportunities, such as tasks taking longer than comparable matters, underutilized resources, or services offered at below-market rates.
• The technology tracks budget utilization against client estimates, alerting lawyers when matters are approaching budget caps.
• It provides insights on pricing optimization, suggesting adjustments to service delivery models or billing structures based on profitability analysis.
• The agent captures previously unbilled time, such as work that occurred but was never tracked, recovering lost revenue from activities like research, drafting, and client communication.
• It flags opportunities to shift from hourly billing to value-based pricing for routine, well-defined deliverables.

Benefits of implementing AI agents in law firms

If the advantages of using AI agents in law firms weren’t clear through these use cases, let us point them out for you. Here are eight clear benefits AI agents offer law firms:

• Cuts costs: Automates routine and complex tasks so firms spend less on manual operations and outside services.
• Saves time: Processes documents, research, and drafts quickly, allowing lawyers to move faster through their caseloads and reduce daily bottlenecks.
• Recovers missed billable hours: Tracks lawyer activities automatically, ensures billable work is captured, and streamlines invoicing for better revenue management.
• Reduces human error: Uses consistent review and analysis in legal tasks, checks for risks, and flags inconsistencies that might be missed otherwise.
• Speeds up client communication: Improves response times by promptly handling queries and providing updates, creating a smoother experience for clients.
• Frees legal teams for complex work: Handles repetitive jobs, so lawyers focus on high-value tasks like strategy and client relations.
• Positions your firm as a leader: Demonstrates to clients and competitors that your firm adopts the latest technology, enhancing its reputation in a tech-driven market.
• Scales easily: Adapts to growing caseloads and new practice areas without needing to hire more staff, supporting firm growth and flexibility.

Real-life examples of AI agents in the legal industry

Enough about theoretical ways to implement AI agents in the legal industry. Let us show you how real law firms are using AI agents. 

Allen & Overy's Harvey AI implementation

Allen & Overy was the first major law firm to adopt Harvey, a ChatGPT-style legal AI tool. During its trial, 3,500 lawyers used it for around 40,000 queries in day-to-day client work. 

Harvey applies natural language processing and machine learning to tasks such as: 

• Contract analysis
• Due diligence
• Litigation
• Regulatory compliance. 

David Wakeling, head of A&O’s markets innovation group, called it a “game-changer,” noting strong results. 

Baker McKenzie's machine learning practice

Baker McKenzie launched its AI initiative in 2017, well before the boom in generative AI. The firm focused early on cloud migration, big data, and deep-learning tools for due diligence, litigation support, and system reviews. 

It established its own machine learning practice, BakerML, and partnered with SparkBeyond to assess client demand, exceeding all performance targets. Now in its second phase, the firm has expanded into generative AI, hiring attorneys, data scientists, and architects to streamline workflows. 

Baker McKenzie also built proprietary tools for data breach notifications and cybersecurity compliance, and trained due diligence models on extensive M&A data. The firm emphasizes AI as augmentation, not replacement.

Our AI agent implementation plan

Anglara delivers end-to-end AI agent implementation, with a strong focus on compliance, process quality, and measurable results. You get a structured rollout plan, proven legal industry know-how, and expert support at every step. 

Firms choose Anglara for quick wins and scalable AI solutions that deliver tangible results.

Our 12-week implementation timeline

• Week 1–2: Pinpoint your most impactful use cases, assess compliance needs, and map integrations with your current systems.
• Week 3–10: Run a pilot in shadow mode, validate outputs with your team, and use real feedback to fine-tune performance.
• Weeks 11–12: Transition to live use, finalize compliance, train your staff, and establish ongoing optimization to sustain the gains.

Choose Anglara for legal AI that delivers rapid results and facilitates future growth. Schedule a free 30-minute consultation to see how your law firm can get started.

Frequently asked questions

How much does it cost to implement AI agents at a law firm?

Costs depend on your firm’s needs, complexity, features, and firm size. They include development, integration, compliance, training, and ongoing maintenance. Anglara can provide you with a custom quote and detailed cost breakdown tailored to your specific requirements. Book your free consultation to learn what AI will mean for your law firm.

Will AI agents replace lawyers and reduce our headcount?

No, AI mainly automates repetitive tasks, freeing lawyers to focus on high-value legal work, strategic advising, and client relationships. It enhances productivity without reducing staff.

How do we ensure AI agents don't violate attorney-client privilege?

Anglara deploys AI in secure, private environments with encrypted data access limited to authorized users, safeguarding attorney-client privilege. AI outputs are lawyer-validated, ensuring confidentiality and accuracy. Client consent is prioritized, and strict vendor contracts prevent data sharing or unauthorized access to sensitive information, thereby preserving privilege.

What happens if an AI agent makes an error or "hallucinates" false information?

Our AI agents are thoroughly trained to minimize errors. To ensure continuous learning and accuracy, every AI output undergoes review and validation by qualified legal professionals. Using verified data sources and maintaining strict audit trails, we provide human oversight that guards against mistakes and improves AI performance over time.

How long does it take to implement AI agents and see ROI?

Typically, ROI appears within one to three months. Early benefits include time savings, increased billable hours, and cost-effective workflows. Longer-term gains are driven by continuous optimization.

Do we need to hire AI specialists or data scientists to use AI agents?

No, you don't need in-house AI specialists. Anglara's specialized team handles technical complexity while your lawyers focus on legal work. We provide expert guidance, training, and ongoing support so your firm can adopt AI agents smoothly without hiring data scientists.