AI in Legal Services: Transforming Practice, Strategy, & Efficiency

The legal sector is experiencing a significant transformation as Artificial Intelligence (AI) becomes integrated into conventional workflows, such as research, drafting, analytics, and client insights. Legal practitioners are observing a fundamental change, shifting from labour-intensive manual tasks to strategic decision-making enhanced by AI. 

AI-driven tools facilitate quicker and more adaptable searches across statutes, cases, and firm precedents. These innovations employ natural-language queries, semantic retrieval, and automated summarisation to minimise research time and uncover intricate relationships within vast legal datasets.

In practice, however, the results of legal research AI have been mixed: while certain platforms expedite the discovery of pertinent authorities, litigation concerning data provenance and copyright has revealed limitations. A recent federal ruling found that a legal-research startup's training data raised infringement concerns, highlighting the legal risks associated with training models on proprietary databases without explicit licenses.

Beyond research and knowledge retrieval, many firms are implementing AI in contract management and transactional workflows. In these areas, the technology delivers measurable returns on investment and introduces distinct operational challenges.

Automating Document Review: Accelerating Due Diligence 

At the foundational level, AI significantly augments legal research and document creation, enabling lawyers to focus on strategy rather than rote labour. One example is Kira Systems, which uses Natural Language Processing (NLP) and machine learning to identify clauses, flag risks, and extract contract data. The company has reported up to 40% time savings in due diligence projects. Similarly, the multinational law firm Freshfields experienced a 20–40% increase in efficiency in contract analysis after implementing Kira, as stated in the study. 

Similarly, Luminance, a UK-based AI platform dedicated to legal document review, adapts based on user interactions to improve its contract analysis. Employed by 500 law firms, this platform identifies unusual clauses and uncovers hidden risks, allowing lawyers to enhance their review processes while concentrating their expertise on more valuable interpretations.

LawGeex, based in the USA, is a legal tech startup that released a study in 2018 showing that its artificial intelligence software can detect issues in Non-Disclosure Agreements (NDAs) more quickly and accurately than seasoned lawyers. Competing against twenty experienced U.S. corporate lawyers from firms associated with Goldman Sachs, Cisco, Alston & Bird, and others, the AI software achieved a 94% accuracy rate in identifying risks across five NDAs, completing its review in just 26 seconds, while the lawyers took an average of 92 minutes, with times varying from 51 to 156 minutes.

Together, these examples illustrate how AI is transforming the economics of legal due diligence, reducing review times from months or weeks to days or even seconds, without compromising quality.

Contract Lifecycle Management (CLM): Enhancing Compliance and Value 

Contracts don't end once signed; they require continuous management, renewals, compliance assessments, and enforcement. AI-driven CLM platforms are helping organisations address these challenges, as seen in the examples below:

• Icertis: Based in Bellevue, Washington, Icertis offers AI-powered contract lifecycle management (CLM) to help organisations create, manage, and analyse contracts throughout their entire lifecycle. 

• Agiloft: Renowned for its adaptable SaaS automation platform, Agiloft utilises AI to monitor contractual obligations, detect compliance risks, automate approvals, and streamline workflows. Agiloft focuses on workflow automation with contract intelligence, reducing manual effort and improving operational efficiency.

A 2025 joint study by Icertis and World Commerce & Contracting revealed that 40% of organisations employing AI-driven CLM experienced tangible cost reductions, while another 40% discovered new revenue streams. These tools revolutionise contracts from static documents into valuable strategic business assets.

Financial Sector Adoption: JPMorgan's COIN 

Financial institutions encounter distinct challenges in contract evaluation and compliance, positioning them as early adopters of AI.

In 2017, JPMorgan Chase introduced COIN (Contract Intelligence), a platform aimed at automating the assessment of commercial loan agreements. As reported by Futurism, the platform eliminated 360,000 hours of yearly legal work through AI-powered analysis, accomplishing in seconds what previously required thousands of hours from lawyers.

This example demonstrates how corporate legal teams in heavily regulated sectors can utilise AI for improved efficiency, minimising errors, and ensuring compliance.

AI in Litigation Drafting: Streamlining Early-Phase Responses 

Drafting initial responses to complaints is tedious but unavoidable in litigation, and AI is beginning to change that.

LegalMation, a California-based legal technology company, has partnered with IBM's Watsonx AI to automate one of the most tedious aspects of litigation, drafting initial responses to complaints. The firm developed a tool that generates high-quality draft responses in under two minutes by training the system on thousands of lawsuits and refining outputs with subject matter experts. This innovation streamlines early-stage litigation and delivers cost savings of up to 80% compared to traditional drafting methods.

LegalMation frees attorneys to focus on strategy and complex legal reasoning by automating routine litigation drafting.

Litigation Analytics: Data-Driven Strategy and Risk Assessment 

Beyond drafting, AI is enhancing litigation through data-driven insights.

Lex Machina, a LexisNexis company based in the USA, analyses millions of court documents to surface patterns in judicial behaviour, case outcomes, and damages. Law firms use it to predict timelines, assess risks, and refine strategies with empirical backing. This represents a shift from intuition-driven litigation toward evidence-based planning.

Executive Sentiment and Adoption Trends 

AI adoption in law is increasingly a board-level decision, not just an IT initiative.

An Icertis survey conducted in 2024 found that almost 50% of C-suite executives anticipated AI would influence their financial performance by 2024, with 56% identifying revenue or cost enhancements as key priorities. Additionally, a subsequent report indicated that 90% of CEOs recognised "value leakage" in contracts, which averages 9% in losses after signing, losses that AI tools are now assisting in addressing.

Ethical and Operational Considerations: Governance, Transparency, and Privacy 

As adoption increases, so does the level of scrutiny. A report from WorldCC in 2025 revealed that although concerns regarding ROI have diminished, security, privacy, and integration challenges continue to be the primary obstacles to adoption. Ethical and regulatory guidance sets forth fundamental obligations concerning competence, confidentiality, and client communication in the context of AI adoption.

Professional organisations have started to provide formal opinions that regard generative AI as a tool that activates existing responsibilities, competence, supervision, confidentiality, and transparency regarding material limitations. For instance, the formal guidance issued on July 29, 2024, by the ABA Standing Committee on Ethics and Professional Responsibility outlines the tasks that firms are required to undertake:

• Conducting due diligence on tools

• Establishing validation and testing protocols

• Implementing disclosure policies

• Ensuring lawyer oversight of substantive outputs

Supporting reports from regulators highlight the importance of outcome-based supervision instead of imposing prescriptive bans. In practice, firms should formalise an AI risk framework encompassing vendor vetting, red-team testing, logging and provenance, escalation procedures, and client disclosure templates, treating these controls as integral to regular compliance with professional responsibilities.

Conclusion: Toward a Hybrid Human–AI Legal Future 

AI's entry into legal services marks more than a technological upgrade; it signals a paradigm shift in how the profession defines value. For centuries, efficiency in law was measured by hours billed and documents reviewed; today, it is increasingly measured by insights delivered, risks anticipated, and outcomes improved. AI is not a competitor to legal expertise but a catalyst for its evolution. 

AI significantly impacts due diligence, contract management, litigation drafting, and analytics. These technologies do not aim to replace lawyers; instead, they allow legal professionals to concentrate on advocacy, negotiation, and judgment, while machines take care of the repetitive and data-heavy tasks. This collaboration can elevate professional standards, broaden access to justice, and enhance the client experience.