AI for Legal Teams: The No-Nonsense Guide

Key takeaways:

• Avoid using public AI models like ChatGPT or Claude for confidential contract work, as they may retain your inputs and lack the security controls, clause library integration, and legal language training that purpose-built legal AI tools provide.

• Begin AI adoption by identifying high-volume, time-consuming tasks that drain your team’s capacity without requiring strategic judgment (such as contract review, metadata tagging, or regulatory summarization) and start with a focused pilot before expanding to full deployment.

• Evaluate legal AI tools based on data security (encryption, zero-data-retention policies, storage location), integration with your existing technology stack, accuracy controls that prevent hallucinations, and whether non-legal teams can use the tool without extensive training.

• Mitigate organizational AI risks by maintaining legal oversight over all departmental AI procurement, staying informed on regulatory developments like the EU AI Act, educating teams on data confidentiality risks, and scrutinizing vendor security documentation with your IT and engineering colleagues.

How prevalent do you think AI is in the legal world today? Artificial intelligence (AI) for legal is the use of AI tools, including machine learning, natural language processing (NLP), and generative AI, to automate and improve how legal teams draft, review, manage, and analyze contracts and other legal work.

The conversations are happening everywhere: in board meetings, budget reviews, and Slack channels. Legal teams are under pressure to have a point of view on AI, adopt the right tools, and manage the risks that come with both. That’s a lot to sort through when you’re already stretched thin.

This guide covers what AI for legal actually means in practice, what it can do for your team, and how to start using it without cutting corners on security or judgment.

We’ll cover:

• What AI is, in plain language for legal pros
• How AI is already being used in legal work today
• What to look for when evaluating a legal AI tool
• Practical projects you can start with this week
• How to mitigate risk across your organization

An AI crash course for the rest of us

You don’t need a computer science background to use AI effectively in legal work. You just need enough context to evaluate tools, ask the right questions, and know when an output deserves a second look. At its core, AI mimics certain aspects of human thinking, and it can do several things that make it genuinely useful for legal teams:

• Recognize patterns in large datasets
• Group information based on similarities
• Classify information into predefined categories
• Summarize large sets of information
• Create new content
• Make predictions based on past data

Key AI terms for legal professionals

Understanding a handful of core concepts is enough to evaluate tools, ask the right questions, and use AI confidently in your work. Here are the terms that come up most in legal AI conversations.

• Artificial intelligence (AI): Software that simulates human thinking by recognizing patterns, processing language, and generating outputs, at speeds and volumes no human team could match.
• Machine learning: The process by which AI programs improve over time by learning from data, without being manually reprogrammed. Think of it as the AI doing its own homework.
• Generative AI: A category of AI that creates new content (text, summaries, drafted clauses) based on patterns in its training data. Tools like ChatGPT fall into this category.
• Natural language processing (NLP): The technology that allows AI to understand and respond to plain human language, which is why you can type a question into a legal AI tool instead of writing code.
• AI agent: Software that works toward a goal independently, breaking it into subtasks without needing a human to prompt each step. Increasingly common in automated legal workflows.
• Large language model (LLM): The underlying model type that powers most modern AI tools for text-based tasks. LLMs are trained on vast amounts of text and are what make contract summarization, drafting, and review possible at scale.

How AI is being used in legal today

AI adoption in legal is no longer an early-adopter phenomenon—it’s nearly universal. Our research in the 2026 State of AI in Legal Report found that 92% of legal professionals report using AI for legal work in some capacity. Among in-house teams, 84% plan to invest in enterprise AI tools in the next 12 months.

The tasks legal teams are using AI for most frequently map directly to the work that consumes the most time with the least strategic payoff: contract review, drafting, and research. The business impact is starting to show up outside legal, too: 97% of respondents who use AI for legal work reported at least one measurable business outcome, with 52% reporting faster response times to business stakeholders and 50% reporting faster contract turnaround times, according to our research. That same data shows 57% of legal professionals who use AI say it frees up time for higher-value analysis and strategy.

What’s changed in recent years isn’t the concept (legal tech has always promised efficiency); it’s the capability. Modern AI tools can read and interpret legal language with enough accuracy to generate useful redlines, flag compliance gaps, and summarize complex agreements without requiring a human to manually guide every step. That shift is what’s moving AI from “interesting experiment” to core legal ops infrastructure.

What can AI do for legal teams?

AI gives legal teams a practical way to handle more work without adding headcount: 82% of firms using AI report greater productivity. The tasks it handles best are the ones that are high-volume, time-consuming, and rules-based, exactly the kind of work that tends to pile up on understaffed legal teams.

Here are the six areas where legal teams get the most out of AI.

Contract management

AI for contract management uses machine learning to automatically extract, tag, and organize contract data, turning a pile of Portable Document Format (PDF) files into a searchable, analyzable repository.

Setting this up manually would take weeks. A contract management system with AI-based import detects and categorizes contract details like agreement dates, values, and key clauses in a fraction of the time.

Once your contracts are structured data, you can also use an AI legal assistant to analyze them through plain-language prompts—no Structured Query Language (SQL) required.

Use AI to:

• Tag and extract contract data like agreement date, contract value, and clauses
• Build charts and data visualizations from contract data
• Analyze agreements via chat prompts, such as “pull out the 10 contracts with the highest contract value”
• Bulk translate contract clauses to different languages

Legal research

AI for legal research uses large language models to find, organize, and summarize case law, regulatory developments, and legal precedents, compressing research that once took hours into a matter of minutes.

The most effective use cases pair AI with a trusted primary source. Rather than asking an AI tool to generate legal research from scratch (which risks hallucinated citations), legal teams are using AI to analyze and summarize specific documents they’ve already sourced from Westlaw, Lexis, or court databases.

Use AI to:

• Summarize case law and identify relevant precedents across large document sets
• Compare regulatory requirements across jurisdictions for a specific contract type
• Synthesize research from multiple sources into a single reference document
• Surface recent regulatory changes that may affect existing agreements

Contract review and negotiation

AI for contract review uses your clause library and playbook to automatically flag deviations, surface risks, and generate redlines, turning a task that takes hours into one that takes minutes.

Reading through contracts line-by-line is critical work, but it doesn’t scale: 40% to 60% of a lawyer’s time can go to drafting and reviewing documents. When your team is reviewing their tenth non-disclosure agreement (NDA) of the week, details get missed.

Use AI to:

• Flag inconsistencies in terms within a contract
• Summarize contracts for non-legal stakeholders
• Train a custom model with your contracts and playbooks to identify redlines
• Draft redlines and amendments
• Identify missing clauses
• Find potential regulatory issues

Contract drafting and consolidation

Imagine you need to create a new contract type or clean up your existing templates. It would take a while to review examples from your industry, decide on necessary clauses, and cross-reference duplicate documents. Or, you could bring in AI to speed up the process.

Orangetheory is a good example of what that looks like in practice. The boutique fitness brand had accumulated 1,000 distinct membership templates after years of rapid growth; each one a variation that needed to be reviewed, compared, and collapsed into something manageable. Consolidating them was expected to take six months. By pairing AI tools with their legal team’s judgment, they cut that timeline in half, completing the project in three months.

Here’s a peek at how to consolidate and templatize contracts faster using our AI Assist™:

Use AI to:

• Generate clauses and contracts based on similar agreements in your industry
• Consolidate contracts into a single templatized version

Compliance and risk checks

AI for compliance uses natural language processing to scan existing agreements for clauses that don’t meet current regulations or internal standards, flagging issues before they become liabilities.

Your legal team can’t manually review every contract every time a regulation changes. AI closes that gap by monitoring your agreement library continuously.

Use AI to:

• Review existing agreements for potential risks or clauses that don’t adhere to new regulations or internal compliance standards
• Set alerts for new laws and regulations for your industry or jurisdiction

Self-service contracting

AI-based self-service contracting lets non-legal teams generate agreements from pre-approved templates and clause libraries, without pulling legal into every routine request.

When your playbook is built into the system, sales can spin up an NDA and procurement can issue a vendor agreement without waiting in the legal queue. That kind of guardrailed self-service matters at scale: our 2026 Contracting Benchmark Report found that contract automation reduced legal involvement by 6%, from 34% to 32%, across 1,700+ organizations. Contract-level alerts and forecasting tools also help teams act on existing agreements before opportunities slip.

Use AI to:

• Generate a contract based on approved terms in an AI playbook
• Forecast sales timelines and cash flow based on past workflows and existing contract data
• Set alerts for opportunities to renew or re-contract with vendors

Legal intake

AI for legal intake uses automated routing and response tools to handle routine requests, so your team spends their time on questions that actually require legal judgment.

Inbound requests pile up fast. Without a system to triage and respond to common questions automatically, legal becomes a bottleneck for questions that don’t need their expertise.

Use AI to:

• Create an internal chatbot to answer common questions and direct requests
• Summarize requests and responses
• Draft responses or templates for legal requests

What to look for in a legal AI tool

Not all AI tools are built for legal work, and the distinction matters more than most vendor conversations will tell you. The right tool for your team depends on what you need it to do, and whether it can do it safely with confidential agreements.

General-purpose AI tools

General-purpose AI tools like ChatGPT and Claude are useful for low-stakes writing tasks: drafting emails, summarizing public documents, and generating first-pass ideas. They’re accessible, fast, and increasingly capable.

Where they fall short for legal work is predictable. They’re not trained on your clause library. They don’t connect to your contract repository. They carry real data privacy risks when you paste in confidential agreements—most public models retain inputs and use them to improve future outputs. For anything involving client data, negotiation strategy, or proprietary terms, a general-purpose tool isn’t the right fit.

Purpose-built legal AI tools

Purpose-built legal AI tools are designed around the specific requirements of legal work: confidentiality, accuracy on legal language, and integration with the workflows your team already uses.

Contract lifecycle management (CLM) platforms with native AI capabilities are the most common example. These tools apply machine learning to your own agreements, clause libraries, and playbooks, so the outputs are calibrated to your standards rather than generic training data. Legal research platforms like Westlaw and Lexis also fall into this category, with AI layers that help you navigate and synthesize large bodies of legal information more efficiently.

Key evaluation criteria

When you’re comparing legal AI tools, these are the questions that matter most. Legal leaders from Google DeepMind, Perkins Coie, and Intuit have a useful framework for this: evaluate each tool based on criticality, confidentiality, complexity, and comfort, a practical lens you can apply to any tool your team is considering, as discussed in our State of AI in Legal roundtable.

• Data security: Does the tool use strong encryption? Does it train on your data? What is its zero-data-retention policy, and where are your agreements stored and processed?
• Training transparency: Can the vendor explain what data the model was trained on and how bias is monitored and addressed?
• Integration with your existing stack: Does it connect to your customer relationship management (CRM) system, e-signature tool, or document storage without requiring a custom build?
• Accuracy and hallucination controls: What mechanisms does the tool use to flag uncertainty or prevent fabricated outputs? Can it show you why it flagged a specific clause?
• Usability for non-legal teams: If sales or procurement will use it, how easy is it for someone without a legal background to initiate or review a contract through the tool?
• Scalability: Can it handle your contract volume today and grow with you without significant additional cost or configuration?

Start using AI in your legal team today

The hardest part of AI adoption is usually just getting started—not the tools themselves, but the decision to pick something concrete and try it. One of the best approaches is to start where the pain is most obvious.

Eleanor Lacy, General Counsel at Asana, started using AI in her team for “spirit killer” tasks. “We asked each other what is the work that just makes you go ‘ugh, I’ve gotta do it,’” she shared. “We’ve thought about how we can use AI to address things that we all feel are spirit killers.”

If you’re curious about AI for legal projects, here are three quick tasks you can try today.

Summarize information

AI can turn stacks of case law, due diligence documents, and dense contracts into concise summaries in seconds, freeing you from the read-through-everything approach that doesn’t scale.

To get started, upload your documents into an enterprise-grade legal AI tool that offers document summarization, training data anonymization, and a zero data retention policy. Then prompt the tool to either summarize the full document or pull specific information, like key dates, obligations, or risk flags.

One important guardrail: never use a public AI model to summarize or review confidential agreements. Public models may retain your inputs, which creates real data exposure for your organization.

Recap laws and regulations

AI can turn dense regulatory text into a quick reference summary, but only when you pair it with a reliable primary source.

Here’s the thing: AI models can misstate or fabricate legal developments when asked to generate regulatory summaries from scratch. The workaround is straightforward. Find the actual update from a trusted source—Westlaw, Lexis, JD Supra, or your state bar association, then feed that specific text into your AI tool and ask it to summarize. The output becomes a clean reference document you can save, share with your team, or add to a shared knowledge hub.

This approach keeps the efficiency gain of AI without the accuracy risk of asking it to do legal research on its own.

Tag contract metadata

AI-based metadata tagging converts your existing contracts into structured, searchable data, without manually opening a single PDF.

To do this, you need a contract lifecycle management (CLM) platform with built-in AI extraction. Upload your contracts, set the record type if you’re working with a mix—like non-disclosure agreements (NDAs) and sales agreements—and the system will automatically detect and tag properties like agreement date, contract value, and key clauses. From there, you can verify the tagged fields or go straight to searching and analyzing your repository.

It’s one of the highest-value things you can do with AI in the first week. Contracts you’ve had sitting in shared drives for years become actionable data almost immediately.

Tips for mitigating AI risks across your organization

AI adoption comes with real risk: for your team and for the organization as a whole. The space is moving fast, and many legal teams are still figuring out where to draw the line. As a legal professional, you have a role to play on two fronts: as someone using these tools yourself, and as a voice in how your organization buys and governs them. That caution is warranted: 53% of respondents who use AI for legal work cite security and data privacy concerns as the primary barrier to using AI more extensively across contracting workflows, according to our State of AI in Legal report.

Here are five practical ways to mitigate risk as you bring AI into your workflow.

1. Stay informed on legislative changes. Your team should be involved anywhere your organization touches AI—whether that’s your own contracting tools or another department’s procurement process. Stay proactive about developments like the EU’s Artificial Intelligence Act and any state-level legislation relevant to your jurisdiction.

2. Educate yourself, your team, and your organization. AI tools can feel like a seamless conversation, which creates a false sense of security around confidentiality. Education helps everyone understand what data is actually moving, where it goes, and what the risks are.

3. Scrutinize every AI tool your organization uses. Individual departments often own their own procurement, but legal oversight should be part of any AI tool evaluation. Review security documentation with your IT and engineering teams. Ask where the model pulls data from, whether it’s public or private, and what your rights are around the outputs.

4. Lean on your community. You’re not the first legal team to work through these questions. Legal pro communities are a practical place to hear what’s worked and what hasn’t at organizations like yours.

5. Start small. Even the most capable AI tools benefit from a careful rollout. Begin with a limited test before committing to a full deployment: it’s the fastest way to find the edge cases before they become problems at scale.

The right foundation for AI in legal

Understanding what AI can do is one thing. Getting the full benefit requires a platform that connects your clause library, playbooks, and contract data in one place, so every AI output is calibrated to your actual standards, not generic training data.

Basic CLM tools give you a place to store and approve contracts. Our platform goes further, applying machine learning across the entire lifecycle (from intake to renewal), so your team spends less time on mechanical review and more time on the work that actually requires legal judgment.

Our customers have saved an estimated cumulative 29 years of effort across contract uploading, review, and redlining using tools like our AI Playbooks for company-specific guidelines and our AI Assist™ for automatic redlining. Security is built into everything—from integrations with OneTrust to Center for Internet Security (CIS) benchmarks and National Institute of Standards and Technology (NIST) Cybersecurity Framework controls.

If you’re ready to see how this works in practice, request a demo today.

Frequently asked questions about AI for legal

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Ironclad is not a law firm, and this post does not constitute or contain legal advice. To evaluate the accuracy, sufficiency, or reliability of the ideas and guidance reflected here, or the applicability of these materials to your business, you should consult with a licensed attorney. Use of and access to any of the resources contained within Ironclad’s site do not create an attorney-client relationship between the user and Ironclad.