What is happening to lawyers
- Impact
AI tools are increasingly used for tasks like e-discovery, contract analysis, legal research, and case outcome prediction, shifting lawyers' focus towards strategy, client counsel, and complex argumentation.
- Risk
Significant role augmentation by AI; premium on human judgment.
The legal profession is undergoing significant augmentation. AI will automate many routine research and document processing tasks, requiring lawyers to become proficient with legal tech and focus on strategic advisory, advocacy, and nuanced legal interpretation.
- Sector readiness
Actively Adopting & Regulating
Law firms and legal departments are adopting AI tools for efficiency and enhanced capabilities, while the profession also grapples with the ethical and regulatory implications of AI in law.
Where you stand
The legal profession is at a significant inflection point due to AI. Many routine, time-consuming tasks will be automated or heavily augmented.
The core value of lawyers will shift further towards strategic thinking, complex legal analysis, client counseling, advocacy, ethical judgment, and a deep understanding of how to leverage AI tools responsibly.
Adaptability and a commitment to continuous learning about legal tech and AI will be crucial for career longevity and success. New legal specialties related to AI will emerge.
What this means for you
Concrete changes to how the work gets done, in the order you are likely to meet them.
- 01
AI-Powered Legal Research. Utilize AI platforms to rapidly search and analyze vast databases of case law, statutes, and legal precedents, identifying relevant information much faster than manual methods.
- 02
Automated Document Review & E-Discovery. Employ AI tools for e-discovery to sift through large volumes of documents, identify relevant evidence, and flag privileged information, significantly reducing review time and cost.
- 03
Contract Analysis & Management. Leverage AI to analyze contracts for standard clauses, identify risks or deviations, assist in due diligence, and manage contract lifecycles.
- 04
Predictive Analytics for Case Outcomes. Use AI tools that analyze historical case data to provide insights into potential case outcomes, litigation risks, or settlement valuations (use with ethical caution).
- 05
Enhanced Due Diligence. AI can assist in rapidly processing and analyzing information for M&A due diligence, compliance checks, and background investigations.
- 06
Drafting Assistance for Legal Documents. Generative AI can help draft initial versions of standard legal documents, briefs, or client communications, which lawyers then review, edit, and finalize.
- 07
Shift to Strategic Advisory & Client Counseling. With AI handling routine tasks, lawyers can focus more on providing strategic legal advice, complex problem-solving, client relationship management, and courtroom advocacy.
- 08
New Legal Specializations in AI Law. Emergence of new practice areas focusing on AI ethics, data privacy, AI regulation, and legal issues arising from autonomous technologies.
- 09
Managing AI-Related Legal Risks for Clients. Advising clients on the legal implications of their own AI adoption, data usage, and algorithmic bias.
- 10
Need for AI Tool Proficiency & Validation Skills. Lawyers will need to be skilled in using legal tech and critically evaluating the outputs of AI tools for accuracy and bias.
- 11
Focus on Negotiation and Persuasion. Core human skills of negotiation, persuasive argumentation, and empathy in client interactions become even more valuable.
- 12
Global Legal Practice Facilitation. AI tools for translation and cross-jurisdictional research can assist lawyers working on international cases.
- 13
Continuing Legal Education (CLE) on AI. A growing need for ongoing education to stay updated on AI tools, their applications, and ethical considerations in law.
- 14
Potential for New Service Delivery Models. AI may enable law firms to offer more accessible or fixed-fee services for certain types of legal work by increasing efficiency.
- 15
Ethical Oversight of AI in Legal Practice. Ensuring responsible use of AI, maintaining client confidentiality, and addressing issues of algorithmic bias in legal decision-support tools.
What is pushing this change
- 01
Vast Volumes of Legal Data (Case Law, Statutes). AI is uniquely suited to process and analyze the enormous corpus of legal texts for research and precedent identification.
- 02
Demand for Increased Efficiency & Cost Reduction in Legal Services. Clients and firms are seeking ways to make legal services more affordable and efficient; AI can automate time-consuming tasks.
- 03
Advancements in Natural Language Processing (NLP) & ML. These technologies enable AI to understand legal language, analyze documents, and even generate draft text.
- 04
Globalization of Business & Legal Matters. AI can assist in managing cross-border legal issues, translation, and understanding different legal systems.
- 05
Rise of Legal Tech Startups & Innovation. A growing ecosystem of companies is developing AI-powered tools specifically for the legal industry.
- 06
Client Expectations for Faster Turnaround. AI can help accelerate tasks like document review and research, meeting demands for quicker legal advice.
- 07
Complexity of Modern Regulations. AI can assist in navigating and ensuring compliance with increasingly intricate national and international laws.
- 08
Need for Proactive Risk Management in Litigation. AI tools are being developed to predict litigation outcomes or identify high-risk clauses in contracts.
- 09
Data Privacy Regulations & E-Discovery Mandates. AI is crucial for efficiently managing large-scale electronic discovery and ensuring compliance with data privacy laws like GDPR.
- 10
Pressure to Improve Access to Justice. AI has the potential to power tools that make basic legal information and services more accessible to the public.
Impact by sector
The headline figure is an average. Where you work changes the picture.
- Litigation & Dispute Resolution
AI for e-discovery, case law research, predictive analytics for case outcomes, and deposition analysis. Human advocacy and trial skills remain central.
- Corporate Law & M&A
AI for due diligence, contract review and analysis, compliance checks, and managing deal documentation.
- Intellectual Property Law
AI for patent searches, trademark monitoring, prior art research, and potentially identifying IP infringement.
- Real Estate Law
AI for title searches, contract drafting (e.g., leases), and due diligence in property transactions.
- Family Law
AI for document management, financial disclosure analysis, and potentially drafting standard agreements. Empathy and client counseling remain key.
Skills to build
The skills that keep the human part of this work valuable as the routine part is automated.
- 01
Legal Reasoning & Critical Analysis. Ability to apply legal principles to complex facts, make nuanced judgments, and strategize—areas where human expertise is vital even with AI support.
- 02
AI-Powered Legal Tech Proficiency. Skill in using AI-driven e-discovery platforms, contract analysis tools, legal research databases, and case management systems.
- 03
Advanced Research & Information Synthesis. Ability to efficiently find, evaluate, and synthesize information from both traditional and AI-augmented sources.
- 04
Client Counseling & Communication. Empathy, active listening, and the ability to clearly explain complex legal matters and AI-derived insights to clients.
- 05
Negotiation & Advocacy. Core human skills for representing clients effectively in settlements, court, or other dispute resolution forums.
- 06
Ethical Judgment & Professional Responsibility. Understanding and upholding ethical duties, including client confidentiality, conflicts of interest, and the responsible use of AI tools.
- 07
Data Interpretation & Analytical Skills. Ability to understand and critically assess data-driven insights provided by AI, including statistical predictions or risk scores.
- 08
Specialization in AI Law & Ethics (Emerging). Developing expertise in the emerging legal fields surrounding AI governance, data privacy, algorithmic bias, and liability for AI systems.
Tools in use
Kinds of tool worth knowing
- 01
AI-Powered Legal Research Platforms. Platforms that use NLP and machine learning to search and analyze case law, statutes, and legal journals more efficiently.
- 02
E-Discovery Software with AI. Tools that use AI to identify relevant documents, redact privileged information, and conduct technology-assisted review (TAR) in litigation.
- 03
AI Contract Analysis & Review Tools. Software that uses AI to review contracts for specific clauses, identify risks, ensure compliance, and extract key data points.
- 04
Generative AI for Legal Drafting. Large Language Models used to assist in drafting initial versions of legal documents, correspondence, or research memos.
- 05
Case Management Software with AI Features. Systems that may incorporate AI for task automation, deadline tracking, and client communication management.
- 06
Predictive Analytics Tools for Litigation. Software (use ethically and with caution) that attempts to predict case outcomes or model litigation scenarios based on historical data.
Named tools already in use
LexisNexis (Lexis+ AI) / Thomson Reuters (Westlaw Edge with AI)
Major legal research platforms incorporating AI for enhanced search, case analysis, and drafting assistance.
Relativity (for e-discovery)
A widely used e-discovery platform that incorporates AI and machine learning for document review and analysis.
Kira Systems / Luminance / Evisort (for contract analysis)
AI-powered platforms designed to automatically review and analyze contracts, extracting key provisions and identifying risks.
ChatGPT / Claude (with careful oversight, for drafting assistance)
Generative AI models that can assist lawyers in drafting initial versions of legal documents, summarizing text, or brainstorming arguments, requiring careful review and editing.
Clio / MyCase (general case management, AI features evolving)
Popular cloud-based legal practice management software that is increasingly integrating AI for automation and client communication.
In practice
Ways people in this role are already using AI, and what they get from it.
- Accelerate Legal Research with AIExample 1
- How
Use AI-powered legal research platforms to quickly find relevant case law, statutes, and precedents across vast databases, identifying key arguments and authorities.
GainSaves significant research time, uncovers more relevant information, and strengthens legal argumentation.
- Automate Document Review for E-DiscoveryExample 2
- How
Employ AI e-discovery tools to automatically sift through thousands or millions of documents in litigation, flagging relevant items and reducing manual review time.
GainDrastically reduces the cost and time of e-discovery, allowing focus on strategic aspects of litigation.
- Analyze Contracts Efficiently using AIExample 3
- How
Utilize AI contract analysis software to review agreements for specific clauses, identify potential risks or inconsistencies, and extract key terms for due diligence.
GainImproves speed and accuracy of contract review, reduces risk of missed clauses, and standardizes analysis.
- Assist in Drafting Legal Documents with Generative AIExample 4
- How
Use LLMs to generate initial drafts of standard legal documents, client letters, or internal memos, which you then meticulously review, edit, and tailor.
GainIncreases drafting efficiency for routine documents, allowing more time for complex legal writing and strategic thought.
- Predict Potential Case Risks (with caution)Example 5
- How
Explore (ethically and with full understanding of limitations) AI tools that analyze historical data to provide statistical insights into potential litigation outcomes or settlement ranges to inform strategy.
GainCan provide additional data points for strategic decision-making in litigation or negotiation, when used as a supplementary tool.
How this role compares
Three neighbouring roles chosen to show the direction of travel, then the roles either side of yours on the exposure scale.
- Paralegals & Legal Secretaries (Routine Tasks)More exposed
- AI impact
High (Document preparation, basic research, scheduling, and filing are highly susceptible to AI automation)
Work moves toRole redefinition towards more complex support tasks, AI tool management, or specialized paralegal work requiring human oversight.
- Legal Tech Software Developers / AI EthicistsDifferent skills, growing
- AI impact
Foundational (They build the AI tools lawyers use or develop the ethical frameworks for AI in law)
Work moves toDeep technical skills in software development, AI/ML, and specialized knowledge in ethics, philosophy, and law.
- Judges / Senior ArbitratorsComplementary, less exposed
- AI impact
Moderate Augmentation (AI for research support, case management), but core human judgment and decision-making remain paramount.
Work moves toUltimate decision-making authority, interpretation of law in novel contexts, ensuring fairness and due process.
- 455–10 yrs
- 452–6 yrs
- 453–7 yrs
Lawyers · this report
455–10 yrs- 506–11 yrs
Business Development Executives
502–6 yrs- 502–6 yrs
Closing judgement
AI will not replace lawyers but will fundamentally change how legal work is done. Lawyers who embrace AI as a powerful assistant for research, analysis, and drafting will be able to deliver more efficient, effective, and potentially more accessible legal services, focusing their human expertise on strategy, advocacy, and complex judgment.
Evidence and revisions
What the exposure figure rests on, what changed when it was last revised, and the published work cited for this role.
40 → 45
Window5-10 years (unchanged)
The 4 October 2026 review moved the score up by 5 points.
Microsoft's AI applicability score for the matching occupation is 0.18, in the upper half of 785 US occupations; Anthropic's observed exposure (the share of the occupation's tasks already being done with Claude) is 0.17, which is substantial by the standards of the 756 occupations measured; the US Bureau of Labor Statistics places it in the 'very high' AI-exposure tier; BLS projects employment to grow 4.7% over 2025–35. Blending our 2025 editorial figure (60%) with the 2026 evidence composite (40%) moves the score from 40 to 45.
US Bureau of Labor Statistics · Employment Projections 2025–35 and AI Exposure Categories
Official statistics · 27 August 2026AI-exposure tier: Very high. Projected employment change 2025–35: +4.7%. Matched to Lawyers.
Microsoft Research · Working with AI: Measuring the Applicability of Generative AI to Occupations
Working paper · 10 July 2025AI applicability score 0.18 (percentile 64 of 785 occupations) for SOC 23-1011.
Anthropic · Anthropic Economic Index report: Cadences
Report · 26 June 2026Observed exposure 0.17 for SOC 23-1011 (percentile 82 of 756 occupations).
World Economic Forum · The Future of Jobs Report 2025
Report · 7 January 2025Legal secretaries and legal officials appear on the WEF declining list for the first time in the 2025 edition.
UK Department for Science, Innovation and Technology · Assessment of AI capabilities and the impact on the UK labour market
Report · 28 January 2026UK context: around 70% of UK workers are in occupations with tasks AI could perform or enhance, above the US average; a one-standard-deviation rise in exposure was associated with a 3.9% fall in UK job postings.
McKinsey Global Institute · Agents, robots, and us: Skill partnerships in the age of AI
Report · 25 November 2025Legal work is named among the "agent-centric" occupations where more than half of working hours are technically automatable with demonstrated AI agents.
Archived copies are served only where the licence permits; otherwise the link goes to the publisher. Full research library →
Readers' view
What people who do this work make of our reading: their own scores, their reasons, and the notes they left on each section.
Our report is one reading of the evidence. This section is the other dataset: what people who do or know this work make of it. Nobody has scored this role yet. Sign in to add yours.
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45
No one has explained their score yet. A line or two about what you see in your own work is the most useful thing on this page.
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Method and sources
Each report was written from a large body of published research and then, in October 2026, re-scored against occupation-level evidence: the US Bureau of Labor Statistics AI-exposure classification and 2025–35 projections, Microsoft Research’s AI applicability scores and Anthropic’s observed-exposure data, cross-checked against the reports listed in the Evidence section above. The organisations and publications below are the standing literature behind the narrative sections. Every source, with dates, licences and archived copies where we are permitted to hold them, is catalogued in the research library.
Scores are revised by blending the previous editorial figure (60%) with a composite of the three occupation-level measures (40%), capped at fifteen points per revision and rounded to the nearest five. The window shifts one notch when a score moves ten points or more. Hand adjustments are recorded with their reason in the revision log.
Research library: every source, with dates, licences and archived copies →
- World Economic Forum
- The Future of Jobs Report series — Employer survey of expected job growth and decline, skill shifts and technology adoption (2020, 2023 and 2025 editions); Artificial Intelligence and the Future of Entry-Level Work (2026).
- AI governance and transformation reports — Frameworks on ethical AI, talent strategy and industry transformation.
- McKinsey Global Institute
- AI, Automation and the Future of Work series — Research quantifying automation potential by task, sector and demographic, from "Jobs Lost, Jobs Gained" to "Agents, robots, and us" (2025).
- Industry-specific reports — Financial services, healthcare, manufacturing and others.
- PwC
- Global AI Jobs Barometer — Annual analysis of job postings and productivity by AI exposure (2024–2026 editions).
- Upskilling Hopes and Fears survey — Employee perceptions and readiness.
- Microsoft Research and Anthropic
- Working with AI (2025); Anthropic Economic Index (2025–2026) — Occupation-level usage data from Copilot and Claude conversations, the two observed-usage measures behind the 2026 revision.
- Stanford Digital Economy Lab and Stanford HAI
- Canaries in the Coal Mine? (2025–2026); AI Index Report (annual) — Payroll evidence on early-career employment in exposed occupations; annual measurement of AI capability, investment and adoption.
- Deloitte
- Human Capital Trends series — Workforce, talent and HR technology trends.
- Tech Trends series — Emerging technologies and their business implications.
- Accenture
- Technology Vision series — Forward-looking analysis of emerging technology, with emphasis on AI.
- Fjord Trends — Design, innovation and human experience in a digital world.
- Boston Consulting Group
- AI/ML insights and industry solutions — "The AI Revolution in the Workplace" and related research.
- EY
- AI and workforce reports — Adoption, talent strategy and ethics.
- IBM Institute for Business Value
- AI and automation studies — Business models, workforce evolution and leadership.
- OECD
- AI Policy Observatory — International data and policy on AI, labour markets and skills.
- Employment Outlook — Labour-market trends including technological impact (2023–2026 editions).
- International Labour Organization
- Generative AI and Jobs: A Refined Global Index of Occupational Exposure (2025) — Task-level exposure gradients for every ISCO occupation; successor to the 2023 global index.
- International Monetary Fund
- Staff Discussion Notes on AI and work (2024, 2026) — Complementarity framing: where AI augments and where it substitutes.
- UK Department for Science, Innovation and Technology
- Assessment of AI capabilities and the impact on the UK labour market (2026) — UK occupational exposure and job-posting evidence.
- Brookings Institution
- AI and automation research — Economic and social implications, displacement and skills.
- Yale Budget Lab and Goldman Sachs Research
- Tracking the Impact of AI on the Labor Market; AI and the US labour market (2026) — Aggregate labour-market monitoring; macro displacement estimates.
- Oxford University (Oxford Martin School)
- The Future of Employment — Frey & Osborne and subsequent research on susceptibility to automation.
- MIT Technology Review
- AI & Work — Reporting on AI research and its implications for industries and jobs.
- Gartner
- Hype Cycle for Artificial Intelligence — Maturity and adoption of AI technologies.
- Future of Work reports — Workplace models and talent strategy.
- U.S. Bureau of Labor Statistics
- Employment Projections 2025–35; AI Exposure Categories; Occupational Outlook Handbook — Ten-year employment projections and, from the 2025 cycle, an AI-exposure tier for every detailed occupation.
- Indeed Hiring Lab
- AI at Work Report (2025) and posting-market updates — Skill-level transformation estimates and job-posting trends by occupation.
- OpenAI
- Research papers, blog and API documentation — Large language models, generative AI, safety and societal impact.
- Google DeepMind
- Research papers and blog — Reinforcement learning, AI for science, AGI and ethics.
- Meta AI
- Research papers and blog — Large language models, computer vision, AI for social good.
- Hugging Face
- Transformers library and model hub — Open-source state-of-the-art NLP models.
- TensorFlow and PyTorch
- Documentation and community forums — Core frameworks illustrating practical capability.
- arXiv
- cs.AI, cs.LG, cs.CV, cs.CL — Pre-print research.
- NeurIPS and ICML
- Conference proceedings — Top-tier academic research.
- ACM and IEEE
- Journals and proceedings — ACM Computing Surveys; IEEE Transactions on AI.
- Kaggle
- Datasets and competition solutions — Applied machine learning on real-world problems.
- The Alan Turing Institute
- Research and reports — Responsible and applied AI.
- NIST
- AI Risk Management Framework — Voluntary framework for managing AI risk.
- European Commission
- AI Act — Risk-tiered legal framework for AI.
- Ethics Guidelines for Trustworthy AI — Principles for responsible development.
- Partnership on AI
- Research and best practice — Responsible AI development.
- AI Now Institute
- Annual reports — Social implications: power, inequality, rights.
- ACM FAccT
- Proceedings — Fairness, accountability and transparency.
- Data & Society
- Publications — Social implications of data-centric technology.
- WIPO
- Conversation on IP and AI — Intellectual-property implications of AI.
- IEEE Global Initiative on Ethics of A/IS
- Ethically Aligned Design — Recommendations for ethical AI design.
- Center for AI and Digital Policy
- Policy briefs — Accountable AI policy.