5 Practical Ways to Use AI Features in Your Legal Practice

Artificial Intelligence (AI) features can help law firms automate routine tasks and improve speed, accuracy, and consistency across workflows—but more than a quarter of firms still aren’t using AI features in their practice. See five practical use cases firms can use to start applying AI legal tools today.

Law firms are constantly under pressure to manage growing caseloads, meet client expectations, and reduce time spent on repetitive work. AI for law can help firms address these challenges by automating routine tasks and improving overall efficiency.

Yet, many still hesitate to adopt AI legal tools. Our 2026 Software Advice Legal Software Buying Trends report shows that 15% of firms have access to AI features but don’t use them, while 13% lack access entirely. 

Why this matters: That means more than a quarter of firms aren’t taking advantage of tools that could save hours every week. This often happens because teams are unsure how the technology works, how reliable it is, or how it fits into daily practice.

Why you should read on: This article fills that gap by breaking down five practical AI use cases—contract review, legal research, document drafting, e-discovery, and client intake—that any law firm can start using today.

But first, let’s take a moment to define what AI in legal really is.

What is AI in law firms? 

AI for law firms refers to the use of technologies within legal case management software and other AI legal tools. These include machine learning (ML), generative AI (GenAI), natural language processing (NLP), and large language models (LLMs) to analyze legal text, extract key information, manage large volumes of data, classify documents, find patterns, summarize case law, and draft structured content.

These AI legal tech capabilities help attorneys, paralegals, and legal staff work faster, reduce repetitive tasks, and improve accuracy.

Real-world applications: For instance, an AI legal tool can review a long contract and highlight renewal terms, risks, and missing clauses—saving attorneys time and helping them focus on client strategy instead of hours spent on manual reading.

Challenges: However, besides the benefits of AI legal tech, concerns around hallucinations, ethics, and privacy are still valid. There have been public cases of AI tools generating nonexistent legal citations, reinforcing why human oversight and double‑checking outputs must remain part of any firm’s workflow.

Blended AI + human approach: By treating AI initiatives as a combination of technology, compliance, and process change, law firms can establish the right processes and guidelines—and use AI safely, ethically, and effectively.

5 practical use cases for leveraging AI in legal tools

Use case 1: Contract review

What it is: AI‑assisted contract review and analysis can scan contract information to extract key clauses, flag risks, and compare language across documents. These tools help legal teams shorten review cycles, improve accuracy, and focus on high‑value analysis instead of long, repetitive reading.

Why this matters for law firms: Contract review consumes significant time every week, especially for firms handling commercial agreements, vendor contracts, NDAs, or compliance‑heavy documents. This is where AI in legal helps reduce the time legal professionals spend tracking down documents.

What AI can automate in contract review

• Extract key clauses. Quickly identify renewal dates, indemnification language, termination terms, confidentiality obligations, and payment terms.

• Flag risky or non‑standard language. Detect missing clauses, nuanced risks, deviations from firm‑approved templates, and unusual terms that require attorney review.

• Control versions. Track contact versions and edits, compare drafts, and identify additions or deletions without manual review.

• Summarize long documents. Turn a lengthy agreement into a structured, digestible summary for faster client communication.

• Organize and classify documents. Sort agreements by type, counterparty, matter, or risk level to support compliance and internal review.

• Maintain consistency. Ensure contracts follow firm policies and preferred language, reducing inconsistencies and the likelihood of future escalations.

Use case 2: Legal research

What it is: AI‑powered legal research, a core part of AI for law, consumes information on statutes, case law, court opinions, and regulations for identifying relevant precedents, summarizes key findings, and helps attorneys answer legal questions faster. 

Why this matters for law firms: Attorneys often spend hours finding the most accurate documents and sources, reading cases, checking citations, and validating sources. This is exactly where AI in legal tools is already proving useful: It can analyze vast legal datasets and case law to identify relevant precedents and arguments, making legal research faster and more efficient.

What AI can automate in legal research

• Find relevant cases. Search across case law using natural language queries and access trusted content by retrieving precise, citation‑backed answers grounded in authoritative legal databases

• Summarize rulings and long documents. Automatically generate structured summaries of opinions, statutes, regulatory materials, among others—saving hours of manual reading.

• Validate citations. Check whether cases remain authoritative and flag incorrect or fabricated citations.

• Highlight legal patterns. Detect recurring arguments, treatment patterns, and judicial tendencies through analytics tools.

• Build research memos. Pull together statutes, commentary, and case references automatically.

Use case 3: E-discovery and litigation support

What it is: AI in electronic discovery (e-discovery) and litigation support uses machine learning and natural language processing to sort, classify, and prioritize electronically stored information (ESI). 

Why this matters for law firms: This technology reviews documents in multiple formats—combined with AI‑driven methods—and accelerates the identification of relevant information across massive corpora, including emails, databases, chat logs, and other digital records. It helps legal teams find relevant evidence faster, generate summaries, and surface patterns that inform case strategy, plus meet deadlines without increasing headcount.

What AI can automate in e-discovery and litigation support

• Use technology to review. Quickly cluster and organize large document sets by topic, custodian, date, or concept.

• Handle multiple file formats. Process a wide range of file types—including PDFs, Word documents, emails, images, and scanned files within one review platform.

• Prioritization and relevance ranking. Use tagging, labeling, and relevance scoring to move high‑value documents to the top of the review queue and filter data efficiently.

• Collaborative approach. Review documents in a unified platform with shared access, secure permissions, real‑time collaboration, and follow‑up tools.

• De‑duplication and near‑duplicate detection. Remove redundant material and group similar files to speed up review and reduce overall document volume.

• Issue tagging and timeline building. Tag documents by issue, reviewer, or relevance, and build timelines that connect events, facts, and evidence to support deposition prep and motion practice.

Use Case 4: Document drafting

What it is: AI‑assisted document drafting uses NLP and LLMs to generate first drafts of briefs, memos, motions, correspondence, and other legal documents. It can produce structured content based on templates, past filings, or user prompts, helping attorneys move from a blank page to a usable outline in minutes.

Why this matters for law firms: Drafting legal documents is repetitive and time‑intensive, especially for firms with high matter volume or limited staff. Instead, firms can use AI legal tools to summarize documents, generate written content, and support routine drafting tasks, allowing practitioners to reallocate time toward strategy and client communication.

What AI can automate in document drafting

• Set and manage templates. Turn existing Word files into reusable templates with variables and conditions, or use built‑in template options to start drafting faster.

• Assemble documents from clause libraries. Insert curated clause options that automatically adapt to context, ensuring consistent terminology, grammar, and style across documents.

• Use smart questionnaires. Generate customized documents through guided Q&A flows, applying conditional text and logic automatically.

• Extract clauses from past work. Pull preferred language from your drafting history and add it to your clause library to standardize best practices.

• Automate review and proofreading. Detect missing definitions, broken cross‑references, formatting inconsistencies, and other red flags before sharing drafts.

• Maintain firmwide consistency. Ensure every document uses preferred styles, terms, and structures, applying firm‑approved language automatically.

Use case 5: Client intake

What it is: AI‑assisted client intake uses online forms, automated workflows, scheduling, e‑signatures, and customer relationship management (CRM) capabilities to capture lead details and qualify prospects. It then converts them into clients while syncing data into case management for a smooth handoff.

Why this matters for law firms: Manual intake (paper/PDF forms, email back‑and‑forth, missed calls) slows conversion and creates errors. Dedicated intake platforms let prospects self‑serve from any device, route submissions into a lead inbox/pipeline, and trigger reminders so fewer consultations are missed.

What AI can automate in client intake

• Capture lead data with online forms. Publish secure, customizable intake forms on your site or send by email/SMS; submitted details flow directly into your intake workspace.

• Use conditional logic to qualify prospects. Dynamic forms change questions based on prior answers to personalize intake and improve completion—supporting faster triage and better matter fit.

• Schedule consultations online. Prospects can book an appointment based on your availability and receive automated text or email reminders to reduce no‑shows. You can also optionally accept payment for the consultation at booking.

• Generate documents and collect e‑signatures. Create engagement letters and retainers from templates, auto‑populate them with intake data, and send for secure e‑signature from any device.

• Track leads in a CRM pipeline. Monitor each prospect’s status, source, and progress in a centralized pipeline to prioritize outreach and improve conversion.

• Sync intake to case management. When a lead becomes a client, push contact and matter data directly into your case management system to avoid re‑keying.

Choosing the right AI tools for your law firm

Selecting the right AI for law firms doesn’t just involve a features checklist—it’s a strategy choice that affects skills, pricing, workflows, and client experience. Below is a concise, practical framework to assist you in your buying journey.

Identify areas for automation, and set strategy before you build your shortlist

Start by mapping where your team loses the most time—intake, drafting, research, document review, calendar management, or billing. These pain points will guide which AI capabilities matter most. At the same time, set your strategy and internal guardrails: Who will use AI, what tasks are appropriate, and what level of oversight is required. Defining skills, workflows, and governance early helps you avoid buying tools your firm isn’t ready to adopt.

Create a shortlist based on the features that matter most

Once you know what you want to automate, identify 3–5 vendors that directly support those priorities. Keep your first shortlist small to avoid decision fatigue and long selection cycles. Focus on core features tied to your goals rather than generic AI promises. Evaluate each option over a short window of time so comparisons stay meaningful.

Shortlist vendors with a security‑first mindset

Your IT security staff should filter your shortlist before you evaluate anything else. Look for vendors that clearly explain how they handle your data, protect confidentiality, store information, and prevent unauthorized model training. Strong authentication, encrypted data, and clear privacy terms should be non‑negotiables. This step prevents costly pivots later and ensures you start only with options that meet your firm’s risk profile.

Schedule demos and free trials

Seeing the software in action is the fastest way to confirm whether it fits your real workflows. Use demos and trials to test how the tool handles your documents, how intuitive the interface is, and whether it integrates smoothly with your existing systems. Have attorneys, paralegals, and support staff test the tool, not just IT or firm leaders, so you can gauge day‑to‑day usability.

Evaluate implementation cost and adoption before signing anything

Implementation often costs more time and resources than expected. Before committing, understand the onboarding plan, who will migrate your data, how long training takes, and what support is included. Ask vendors to outline a realistic timeline and internal workload. A tool is only valuable if your team uses it, so adoption should be a central part of your evaluation—not an afterthought.

Define your own KPIs to measure success

Finally, set a clear definition of success before rollout. Choose measurable indicators such as time saved, fewer errors, faster intake, lower admin hours, improved turnaround times, or higher client conversion rates. These key performance indicators (KPIs) help you evaluate whether the tool is delivering real value and guide optimizations after implementation.