How AI is Used in Legal Technology: A Practical Guide

AI in legal technology refers to the application of machine learning, natural language processing, and large language models to automate, augment, and improve legal work across contract management, research, compliance, and operations.

That is the short version. The longer reality is that "AI" has become the most overloaded term in legal technology marketing. Some vendors use it to describe genuine breakthroughs in how legal work gets done. Others apply it to keyword search features that have existed for a decade. Understanding what AI actually does in legal technology, where it delivers real value, and where it falls short is essential for anyone evaluating tools, building a business case, or simply trying to keep up with how the profession is changing.

This guide covers the full landscape. It goes beyond contract management to examine how AI is applied across legal research, compliance, document review, intake, billing, and operations. The goal is practical understanding, not hype.

AI Adoption in Legal: The Numbers

The legal profession was historically slow to adopt technology. That has changed rapidly. Generative AI in particular has accelerated adoption timelines from years to months.

That number from Goldman Sachs does not mean 44% of lawyers will be replaced. It means 44% of the tasks lawyers perform today are susceptible to automation. The distinction matters. AI handles the repetitive, pattern-based elements of legal work so that lawyers can focus on the judgment-intensive elements that require human expertise.

Thomson Reuters' 2025 Future of Professionals report found that 78% of legal professionals believe AI will have a high or transformational impact on their work within five years. Meanwhile, a 2024 Wolters Kluwer survey found that 67% of corporate legal departments were actively experimenting with or deploying AI tools, up from 22% in 2022.

The adoption curve is steep, and it is accelerating.

Where AI is Used in Legal Technology

AI in legal technology is not a single application. It spans at least six distinct domains, each with its own set of tools, techniques, and maturity levels.

1. Contract Management and CLM

Contract lifecycle management is the most commercially mature application of AI in legal technology. This is where the largest number of vendors compete and where the most investment has flowed.

AI-powered drafting allows users to generate complete contracts from natural language descriptions. Rather than selecting a template and filling in fields, a user describes what they need and the system produces a structured, legally sound document. Platforms like Bind have built their entire interface around this conversational approach, enabling users to create contracts by describing them in plain language.

Automated review analyzes incoming contracts against organizational playbooks. The system flags clauses that deviate from approved language, identifies missing provisions, and highlights risk areas. This does not replace lawyer review. It focuses lawyer attention on the clauses that actually need human judgment rather than requiring line-by-line reading of every page.

Negotiation assistance suggests alternative language when counterparties propose unfavorable terms. The AI draws from a library of pre-approved fallback positions and industry-standard alternatives. Some tools can even conduct initial rounds of negotiation autonomously, escalating to a human only when terms fall outside predefined parameters. For a detailed look at this capability, see our guide on AI contract negotiation.

Contract analytics extracts structured data from unstructured agreements. Party names, financial terms, renewal dates, governing law, termination provisions: all of this can be extracted automatically and organized into searchable databases. For organizations managing thousands of contracts, this transforms a static document repository into a dynamic intelligence layer.

Obligation tracking identifies commitments embedded in contract language and monitors them against deadlines. Payment schedules, delivery milestones, reporting requirements, and renewal windows are extracted and tracked automatically.

For a deeper look at this category, see our guide on what AI contract management is and how it works.

2. Legal Research and Case Analysis

Legal research has been one of the earliest and most impactful domains for AI in law. The volume of case law, statutes, regulations, and legal commentary is too large for any individual to navigate efficiently. AI changes the economics of research fundamentally.

Semantic search goes beyond keyword matching to understand the legal concepts a researcher is looking for. A search for "employer liability for remote worker injuries" returns relevant results even when the source materials use different terminology. This is a meaningful advance over Boolean search, which requires researchers to anticipate the exact words used in the documents they are looking for.

Case law analysis identifies relevant precedents, maps citation networks, and surfaces cases that support or undermine specific legal arguments. Tools like Westlaw Edge, LexisNexis, and Casetext (now part of Thomson Reuters) use AI to rank results by relevance rather than recency, identify distinguishing factors between cases, and flag cases that have been overruled or questioned.

Brief analysis reviews draft briefs and identifies weaknesses in legal arguments, missing citations, and counter-arguments the opposing side is likely to raise. This capability has matured significantly since the introduction of LLM-powered tools in 2023.

Regulatory monitoring tracks changes in statutes and regulations across jurisdictions and alerts legal teams when changes affect their practice areas or clients. For firms operating across multiple states or countries, this automated monitoring replaces what was previously a labor-intensive manual process.

Major players in this space include Thomson Reuters (Westlaw AI-Assisted Research, CoCounsel), LexisNexis (Lexis+ AI), vLex (Vincent AI), and Harvey, which has raised significant venture funding for its legal-specific LLM platform.

3. Document Review and Due Diligence

Document review in litigation and M&A transactions involves examining thousands or millions of documents for relevance, privilege, and key information. This has been one of the largest cost centers in legal practice and one of the earliest targets for AI automation.

Technology-assisted review (TAR) uses machine learning to classify documents as relevant or irrelevant based on a small set of human-reviewed examples. First-generation TAR (TAR 1.0) required an upfront training phase. Current continuous active learning (CAL) systems learn throughout the review process, getting more accurate as reviewers code more documents.

Privilege detection identifies documents that may be protected by attorney-client privilege or work product doctrine. AI models trained on privilege determinations can flag potentially privileged documents for human review, reducing the risk that privileged material is inadvertently produced.

Due diligence automation in M&A transactions extracts key provisions from target company contracts, identifies change-of-control clauses that could be triggered by the transaction, and flags unusual or problematic terms. What once took teams of junior associates weeks can now be completed in days, with the AI handling the initial extraction and humans focusing on the analysis.

Vendors in this space include Relativity (with its aiR suite), Reveal, Everlaw, and Luminance, which has expanded from document review into broader contract intelligence.

4. Compliance and Regulatory Monitoring

Regulatory compliance is inherently complex. Organizations must track requirements across multiple jurisdictions, agencies, and regulatory bodies. The volume of regulatory change is staggering: Thomson Reuters has estimated that financial institutions alone face more than 200 regulatory alerts per day globally.

Regulatory change management uses AI to monitor regulatory publications, identify changes relevant to an organization's activities, and map those changes to internal policies and procedures that may need updating.

Compliance risk assessment analyzes organizational data against regulatory requirements to identify potential gaps. AI models can review communications, transactions, and internal policies to flag areas where practices may not align with current regulations.

Anti-money laundering (AML) and know-your-customer (KYC) processes use AI to screen entities against sanctions lists, identify suspicious transaction patterns, and reduce the false positive rates that plague traditional rules-based screening systems. Financial institutions have been among the earliest and most aggressive adopters of AI for these use cases.

Privacy compliance tools use NLP to analyze data processing activities against requirements like GDPR, CCPA, and other privacy regulations. They can identify personal data in unstructured documents, map data flows, and generate compliance documentation.

5. Legal Intake and Triage

Legal departments in large organizations receive hundreds or thousands of requests per month from business teams. Routing these requests to the right person, prioritizing them appropriately, and handling routine matters without senior lawyer involvement is a significant operational challenge.

Intelligent intake systems use NLP to classify incoming requests by type, complexity, and urgency. A request to review an NDA gets routed differently than a request for advice on a potential regulatory investigation. AI handles the classification; humans handle the work.

Self-service portals powered by AI allow business teams to answer routine legal questions, generate standard documents, and complete simple processes without submitting a formal request to legal. If a sales representative needs a standard NDA, an AI-powered portal can generate it, have it reviewed against the playbook, and deliver it without any lawyer touching it. Our step-by-step guide on automating NDA creation shows how this works in practice.

Chatbots and virtual assistants handle frequently asked questions about company policies, contract terms, and legal procedures. These systems reduce the volume of routine inquiries that reach the legal team, freeing lawyers for work that actually requires their expertise.

6. Billing and Matter Management

Legal operations teams manage budgets, outside counsel relationships, and matter workflows. AI is increasingly applied to these operational functions.

Legal spend analytics uses AI to analyze invoicing data from outside counsel, identify billing anomalies, benchmark rates, and flag potential overcharges. Guideline-compliance checking, which historically required manual review of invoices against billing guidelines, can now be largely automated.

Matter prediction uses historical data to forecast the likely cost, duration, and outcome of legal matters. If your organization has handled 500 employment disputes over the past five years, ML models can identify patterns in which cases settle quickly, which ones escalate, and what factors drive costs.

Resource allocation tools analyze workload data to identify capacity constraints, predict future demand, and optimize staffing decisions. For legal departments that need to balance internal work with outside counsel spend, these insights inform strategic resource decisions.

AI Copilots vs. AI Automation

Not all AI in legal technology works the same way. A useful distinction is between AI copilots, which assist lawyers in real time, and AI automation, which handles workflows independently.

Most legal AI tools combine both modes. A contract management platform might automate metadata extraction (no human needed) while using a copilot approach for contract review (AI flags issues, human decides). The right balance depends on the risk profile of the task. Routine, high-volume, low-risk work is a strong candidate for automation. Complex, high-stakes, judgment-intensive work benefits from the copilot model.

The Technology Behind Legal AI

Understanding the underlying technologies helps you evaluate vendor claims and ask better questions during product evaluations.

Natural Language Processing (NLP)

NLP enables software to read, interpret, and generate human language. In legal technology, NLP powers clause identification (recognizing that a paragraph is an indemnification clause even when the word "indemnification" does not appear), entity extraction (pulling party names, dates, and financial terms from unstructured text), and semantic search (understanding that "termination for convenience" and "right to cancel without cause" mean similar things).

NLP has been used in legal technology since the early 2010s. The technology has matured significantly, and extraction accuracy for well-defined tasks now exceeds 95% in most commercial tools.

Machine Learning (ML)

Machine learning models improve as they process more data. In legal technology, ML powers document classification in e-discovery, risk scoring for contracts, predictive analytics for matter outcomes, and anomaly detection that identifies unusual patterns in contract portfolios or billing data.

The key advantage of ML over rules-based systems is adaptability. A rules-based system does exactly what it is told. An ML system learns from examples and can handle variations it was not explicitly programmed for. The trade-off is that ML models require training data, and their decisions can be harder to explain than simple rules.

Large Language Models (LLMs)

LLMs like GPT-4, Claude, and Gemini have transformed legal AI since 2023. These models can generate, summarize, and reason about text at a level that earlier NLP systems could not approach.

In legal technology, LLMs power conversational interfaces (describe what you need in plain language), document summarization (condense a 100-page agreement into key points), question answering (ask questions about your contract portfolio in natural language), and content generation (draft contracts, briefs, memos, and correspondence).

The legal-specific applications of LLMs have matured rapidly. Platforms like Harvey have built legal-specific LLMs. Thomson Reuters integrated LLM capabilities into CoCounsel. And contract management tools like Bind use LLMs to enable conversational contract creation, where users describe what they need and the system generates a complete agreement.

Knowledge Graphs

Knowledge graphs represent relationships between legal concepts, entities, and documents. In legal research, a knowledge graph might connect a statute to the cases that interpret it, the regulations that implement it, and the secondary sources that analyze it.

Knowledge graphs are particularly valuable for compliance and regulatory monitoring, where understanding the relationships between rules, requirements, and organizational obligations is essential. They are also used in contract management to map relationships between related agreements, parent companies, and subsidiaries.

What AI Does Well in Legal

Honest assessment requires acknowledging both strengths and limitations. Here is where AI delivers consistent, measurable value in legal technology today.

Processing volume that humans cannot match. A single AI system can review thousands of contracts, millions of documents, or continuous streams of regulatory updates. Humans cannot scale this way without proportional increases in headcount and cost.

Maintaining consistency across repetitive tasks. The five-hundredth contract reviewed by AI receives the same level of analysis as the first. Human reviewers experience fatigue, distraction, and variation. AI does not.

Extracting structured data from unstructured text. Legal work is overwhelmingly text-based. Contracts, briefs, regulations, and correspondence are all unstructured documents. AI can read these documents and extract structured, searchable data far faster than manual review.

Pattern recognition across large datasets. Identifying that 30% of your vendor contracts contain an unusual limitation of liability clause requires reviewing every contract. AI makes this kind of portfolio-level analysis practical.

Accelerating routine workflows. Generating standard NDAs, routing intake requests, screening for compliance issues, and dozens of other routine tasks can be completed in minutes rather than hours or days.

Enabling non-experts to handle standard processes. When AI guides the process and enforces compliance with organizational standards, business teams can handle routine legal tasks (like generating approved contracts) without involving a lawyer for every step.

What AI Struggles With

Equally important is understanding where AI falls short. Overpromising and underdelivering has damaged trust in legal AI, and honest acknowledgment of limitations is essential.

Novel legal arguments and first-impression issues. AI excels at pattern matching. Truly novel legal questions, where no precedent exists, require creative reasoning that current AI systems cannot reliably provide. A lawyer crafting a novel constitutional argument is doing work that AI cannot replicate.

Jurisdiction-specific nuance. Legal rules vary enormously across jurisdictions. A contract clause that is enforceable in Delaware may be unenforceable in California. AI models trained primarily on one jurisdiction's law may not accurately account for these differences, especially in less common jurisdictions where training data is sparse.

Ethical judgment and professional responsibility. Lawyers operate under ethical obligations that AI cannot navigate independently. Conflicts of interest, duties of candor to the court, obligations to opposing counsel, and the boundaries of zealous advocacy all require human professional judgment.

Confidentiality and privilege management. Legal work involves some of the most sensitive information in any organization. Ensuring that AI tools do not inadvertently expose privileged communications, share confidential data across matters, or retain information that should be destroyed requires careful architecture and governance.

Understanding business context. AI works with the text it is given. It does not know the strategic importance of a deal, the relationship dynamics between parties, or the organizational politics that influence legal decisions. A contract clause that looks unfavorable in isolation might be entirely acceptable given the broader business context. AI cannot make that determination.

Explaining its reasoning. Many AI models, particularly deep learning systems, operate as black boxes. They produce outputs without transparent explanations of how they reached their conclusions. In legal work, where reasoning matters as much as results, this opacity is a significant limitation.

How to Evaluate AI Legal Tools

Whether you are evaluating a contract management platform, a legal research tool, or a compliance system, these criteria apply broadly.

Define the problem before evaluating solutions. Start with the specific workflow or pain point you want to address. "We need AI" is not a use case. "Our legal team spends 20 hours per week reviewing incoming vendor contracts" is a use case. Clear problem definition prevents you from buying capabilities you do not need.

Demand specificity about AI capabilities. Ask vendors exactly what the AI does, what models it uses, and what tasks require human involvement. "AI-powered" is marketing language, not a feature description. You need to know whether the AI drafts contracts, reviews them, extracts data, or simply improves search results. The specifics determine the value.

Test with your own data. Generic demos use ideal documents. Your contracts, your regulatory environment, and your workflows have idiosyncrasies that may challenge AI systems trained on different data. Insist on a pilot with real documents from your organization.

Evaluate accuracy and error handling. Ask for accuracy benchmarks on specific tasks. More importantly, ask what happens when the AI is wrong. Look for confidence scores, mandatory human review steps, and easy override mechanisms. A system that is 90% accurate and flags the other 10% for human review is vastly more useful than one that is 95% accurate but presents everything with equal confidence.

Understand the data security architecture. Legal data is sensitive. Ask where data is stored, whether it is used to train models, who has access, and what certifications the vendor holds (SOC 2, ISO 27001, GDPR compliance). If the vendor uses third-party AI models, ask how data is transmitted to and from those models.

Assess integration requirements. AI tools that operate in isolation create data silos. Evaluate how the tool integrates with your existing systems: document management, email, CRM, billing, and matter management platforms. The value of AI increases when it operates within your existing workflows rather than requiring users to switch to a separate interface.

Calculate total cost of ownership. AI tool pricing often includes implementation fees, per-user charges, volume-based pricing, and premium support costs. A tool that appears affordable at first glance may become expensive at scale. Ask for a complete cost breakdown at your expected usage level.

The Future of AI in Legal

Several trends are shaping where legal AI is headed over the next two to five years.

AI Agents and Autonomous Workflows

Current AI tools handle individual tasks. The next evolution is AI agents that manage multi-step workflows end to end. An agent could receive a request for a vendor agreement, draft it, route it for internal review, send it to the counterparty, manage the negotiation, collect signatures, extract obligations, and set up monitoring, all with minimal human intervention on routine matters.

The building blocks for this exist today. What remains is the integration work to connect these capabilities into reliable, end-to-end workflows with appropriate human oversight at critical decision points.

Predictive Legal Outcomes

Historical legal data contains signals about future outcomes. Which contract terms correlate with disputes? Which types of litigation settle quickly versus proceeding to trial? What regulatory enforcement patterns predict future actions?

ML models trained on large datasets of legal outcomes will increasingly help organizations make more informed decisions about risk, strategy, and resource allocation. Early applications are already available in litigation analytics (tools like Lex Machina and Premonition), and the approach is expanding into contract management, compliance, and regulatory strategy.

Cross-System Intelligence

Most legal AI today operates within a single tool or platform. The next frontier is intelligence that spans across systems: connecting insights from contract management with billing data, compliance monitoring, and matter management to provide a unified view of legal operations.

For example, correlating contract terms with actual payment patterns, dispute history, and compliance incidents would give legal teams a far richer understanding of their risk exposure than any single system can provide today.

Specialized Legal LLMs

General-purpose LLMs perform well on many legal tasks, but specialized models trained specifically on legal data are beginning to outperform them on domain-specific tasks. Harvey, for example, has built a legal-specific LLM platform that incorporates firm-specific knowledge and legal reasoning patterns. Thomson Reuters' CoCounsel integrates legal-specific training with access to Westlaw's case law database.

This trend toward specialization is likely to continue. General models will remain useful for broad tasks, but specialized models will increasingly handle the tasks where legal precision matters most.

Regulation of AI in Legal Practice

Courts and bar associations are beginning to establish rules for AI use in legal practice. Several U.S. federal courts now require attorneys to disclose when AI tools were used to prepare filings. Bar associations are issuing guidance on ethical obligations when using AI. The European Union's AI Act classifies certain legal AI applications as high-risk, imposing requirements around transparency, accuracy, and human oversight.

These regulatory developments will shape how AI tools are designed and deployed in legal contexts. Vendors that build compliance with these emerging requirements into their products will have a significant advantage.

Frequently Asked Questions

Is AI replacing lawyers?

No. AI is changing what lawyers spend their time on. Tasks that are repetitive, pattern-based, and high-volume are being automated. Tasks that require judgment, creativity, strategic thinking, and human relationships are not. The lawyers who will thrive are those who learn to use AI as a tool to amplify their expertise rather than viewing it as a threat. The analogy to spreadsheets replacing accountants is apt: spreadsheets automated calculations, but the need for accounting judgment increased rather than decreased.

How accurate is legal AI?

Accuracy varies significantly by task and vendor. For well-defined extraction tasks (identifying party names, dates, financial terms), leading tools achieve 95% accuracy or higher. For more complex analytical tasks (risk classification, argument analysis, clause categorization), accuracy typically ranges from 85% to 95%. The relevant comparison is not AI versus perfection but AI-assisted work versus purely manual work. Studies have shown that the combination of AI analysis and human oversight generally produces better results than either approach alone.

What is the difference between AI copilots and AI automation?

AI copilots assist humans in real time. They suggest, flag, and recommend, but a human makes every decision. Examples include AI that highlights risky clauses during contract review or suggests relevant case law during research. AI automation handles tasks independently, without requiring human input for each step. Examples include automated document classification in e-discovery, regulatory monitoring, and standard contract generation from predefined parameters. Most legal AI tools combine both modes, using automation for routine steps and copilot assistance for judgment-intensive steps.

Is legal AI secure?

Security depends on the specific vendor and architecture, not on AI as a category. Key questions to ask: Where is data stored and processed? Is client data used to train models that serve other customers? What encryption standards are used in transit and at rest? Does the vendor hold SOC 2, ISO 27001, or equivalent certifications? How is data handled when using third-party AI models? Reputable legal AI vendors use enterprise-grade security, maintain strict data isolation between customers, and comply with relevant regulations including GDPR and data residency requirements.

Where should a legal team start with AI?

Start with the pain point that offers the clearest ROI and the lowest risk. For most legal teams, contract management is the strongest starting point. Our guide on what is legal automation provides a practical framework for choosing where to begin. Contracts are high-volume, involve repetitive workflows, and produce measurable outcomes (cycle time, error rates, missed deadlines). The risk of AI errors in routine contract tasks is manageable with appropriate review processes. Once you have demonstrated value in one area, expand to adjacent use cases: legal research, compliance monitoring, or intake automation. Avoid the temptation to deploy AI everywhere simultaneously. Focused implementation with measurable results builds organizational confidence and secures budget for further expansion.

Bind's Approach to AI-Powered Contracts

Curious how this plays out in practice? Bind CEO Aku Pöllänen walks through Bind's conversational approach to contracts and why it changes the way teams handle agreements:

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