How AI in Legal Industry Workflows is Being Used: Benefits, Risks, and Guidance

Legal teams are under constant pressure to move quickly, handle growing volumes of agreements, and maintain strict compliance, all without increasing headcount.

Luckily, AI is changing how that work gets done.

AI in legal industry settings automates tasks such as contract review, clause extraction, and compliance tracking, reducing turnaround times from hours to mere minutes. But while the upside is clear, adoption comes with real considerations around accuracy, data security, and oversight.

Read on to explore how AI is being used in legal workflows today, along with the key benefits, risks, and best practices for implementing it effectively.

Key Takeaways

• AI helps automate time-intensive tasks such as contract review, risk identification, and compliance monitoring at scale.

• The benefits of AI in the legal industry are substantial, but so are the risks, including “hallucinations”, data security concerns, and the need for human oversight.

• Successful adoption depends on using enterprise-grade, compliance-focused tools built specifically for legal workflows, and not general-purpose AI.

• The future of AI in legal operations is shifting from reactive review to proactive, data-driven agreement management.

How is AI being used in legal workflows today?

AI is increasingly embedded in the most time-intensive parts of legal work, particularly in areas such as agreement analysis, due diligence, and compliance monitoring, where teams routinely process large volumes of documents under tight deadlines. That shift is reflected in the numbers, with AI adoption for legal document review up 75% year-over-year opens in a new tab as organizations look to reduce manual workload and improve consistency.

One of the clearest examples is how teams handle commercial agreements. Attorneys often spend hours reviewing individual contracts. At scale, that quickly becomes a major operational bottleneck. AI is helping address this by significantly reducing review time, with many reporting measurable time savings from AI tools opens in a new tab in their day-to-day workflows.

At a high level, AI in legal workflows focuses on understanding and structuring unorganized contract data. Using natural language processing (NLP) and machine learning, these systems can read legal documents, extract key information, and apply predefined rules at scale.

Some of the most common applications include:

These capabilities augment legal expertise rather than replace it. By handling repetitive analysis at scale, AI allows legal professionals to focus on judgment, negotiation, and strategic decision-making.

What are the benefits of using AI in legal settings?

As AI becomes more embedded in legal workflows, its impact is most visible in day-to-day operations, delivering faster turnaround times, more consistent outcomes, and better risk control. Here are several key benefits:

• Speed: AI moves teams from hours of manual review to near-instant insights, keeping agreements from becoming a bottleneck.

• Consistency: The same criteria are applied across all agreements, reducing variability and ensuring that key clauses and compliance requirements are enforced consistently.

• Risk visibility: Missing clauses, unusual language, and compliance gaps are surfaced earlier, giving teams more time to respond before agreements are finalized.

• Cost efficiency: Less time is spent on repetitive reviews, and fewer errors requiring rework result in lower operational costs without sacrificing accuracy.

• Scale: As agreement volumes grow, teams can handle significantly more work without expanding headcount.

Together, these shifts move legal operations from manual, reactive processes to structured, scalable, and data-driven ones. Tools like Docusign Iris are built around this model, combining agreement AI with enterprise-grade security to help legal teams move faster without compromising accuracy or compliance.

The impacts of this implementation become even clearer when comparing AI-assisted workflows with traditional legal processes side-by-side:

What are the biggest challenges of implementing AI in legal workflows?

AI offers clear advantages for legal teams, but adoption comes with real risks that need to be understood before deployment. The most common challenges fall into four areas:

• Hallucinations and AI errors: AI systems can misinterpret clauses, flag issues that aren’t actually present, or even generate language that didn’t exist in the original document. Because these errors aren’t always obvious, human review remains essential. With 90% of lawyers reporting concerns about hallucinations opens in a new tab in AI outputs, this reinforces the role of AI as a first-pass reviewer, not a final decision-maker.

• Data security and confidentiality: Documents processed through third-party AI tools may be subject to GDPR, HIPAA opens in a new tab, or attorney-client privilege obligations that don't pause for technology. Organizations need clarity on data residency, whether documents are used for model training, and whether on-premise options are available.

• Bias, liability, and accountability: AI systems trained on historical contract data can reflect existing biases, and when something is missed, liability isn't always clear. Regulatory frameworks for AI in legal contexts are still catching up, making internal governance policies critical in the interim.

• Change management: Resistance to new tools is common in legal departments, where established workflows carry significant institutional weight. Successful adoption requires training, clear communication about what AI can and can't do, and a culture that treats AI as a support tool rather than a replacement.

These challenges aren’t disqualifying, but they do require deliberate planning. Organizations that go in clear-eyed about the risks are better positioned to capture the benefits without incurring unnecessary liabilities.

How can legal teams adopt AI solutions safely?

Adopting AI in a legal context demands a deliberate implementation strategy that accounts for governance, compliance, and people. Here's where to start.

• Establish governance before deployment. Define how AI will be used, by whom, and under what conditions before any tool goes live. Clear usage policies reduce the risk of inconsistent application and create accountability from day one.

• Choose tools built for legal, not adapted for it. General-purpose AI poses legal risks. Look for enterprise-grade tools with security certifications (SOC 2, FedRAMP, ISO 27001), built-in audit trails, and transparent policies on data usage and model training.

• Build a human-in-the-loop process. AI works best as a first-pass reviewer, not a decision-maker. Design workflows in which AI handles volume and pattern recognition, while attorneys retain sign-off authority on anything consequential.

• Account for regulatory requirements. Depending on your jurisdiction and industry, AI-assisted workflows may need to comply with the ESIGN Act, UETA, eIDAS, or sector-specific regulations. Map these requirements before deployment, not after.

• Train teams on what AI can and can't do. Overpromising AI capabilities leads to over-reliance. Set realistic expectations, invest in onboarding, and make it easy for teams to flag issues when AI output doesn't look right.

The goal is to make human judgment more focused. With the right tools, processes, and guardrails in place, AI becomes a force multiplier for legal teams rather than a liability. Done right, adoption is as much about organizational readiness as it is about the technology.

Where do AI tools deliver the most value in legal work?

AI's impact is most pronounced where document volume is high, and consistency is critical. These are the use cases where the technology has proven most effective.

Contract review and risk analysis

Contract review is where most legal teams first encounter AI, and where the time savings are most immediate. AI tools can scan agreements for unusual terms, missing clauses, and non-standard language in a fraction of the time it would take a human reviewer. Rather than reading every line, attorneys can focus their attention on the exceptions AI surfaces, making the overall review process faster and more targeted.

Due diligence and mergers and acquisitions (M&A)

M&A transactions involve enormous document volumes under tight timelines, making them a natural fit for AI-assisted review. AI can process large document sets simultaneously, flagging relevant clauses and potential risks across hundreds of agreements. The result is faster due diligence timelines and significantly reduced manual review costs, without sacrificing the thoroughness that transactions of that scale demand.

Compliance monitoring and obligation tracking

AI's role doesn't end after a contract is signed. Post-execution, legal teams need to track renewal dates, obligations, and regulatory milestones across potentially thousands of active agreements. AI makes this manageable by monitoring contracts continuously and surfacing upcoming deadlines or compliance gaps before they become problems.

What does the future of AI in the legal industry look like?

AI's current role in legal workflows is just the beginning. The next wave of development points toward a more proactive, integrated model of legal operations.

• Generative AI for drafting. AI is increasingly used to produce contract first drafts and automate playbook applications, reducing the time attorneys spend on routine document creation.

• Predictive analytics. Historical contract data is being used to flag risk patterns and model likely outcomes opens in a new tab, helping legal teams make more informed decisions earlier in the process.

• Regulatory evolution. AI-specific legal frameworks are emerging in a growing number of jurisdictions, and compliance requirements are likely to become more defined as adoption increases.

• Ecosystem integration. AI is integrating contract lifecycle management (CLM), electronic signatures, and ERP systems into more unified workflows, reducing manual handoffs that slow legal operations.

Together, these developments point toward a shift from reactive agreement management to a model in which risk is anticipated, and obligations are tracked proactively.

Lead the change and bring AI into your legal operations

AI is reshaping how legal teams work. To do it right, organizations must balance innovation with compliance, human oversight, and technology designed for the demands of legal work.

Ready to see what that looks like in practice? Discover how Iris can speed up your legal work, simplify your contracts, and keep you compliant.

Frequently asked questions

What are the main risks of using AI in the legal profession?

The most significant risks include hallucinations (where AI systems misinterpret or fabricate contract language), data security concerns when handling sensitive client data, and unclear liability when AI errors go undetected.

There are also broader considerations around legal ethics, professional conduct, and client confidentiality, particularly as AI adoption accelerates across law firms and legal departments.

These risks don’t outweigh the benefits, but they make human-in-the-loop review and strong governance essential in any AI-assisted legal workflow.

How can legal teams ensure data security when using AI tools?

Legal teams should prioritize AI solutions built specifically for the legal industry rather than general-purpose AI tools.

Look for platforms with compliance certifications such as SOC 2, ISO 27001, or FedRAMP, along with clear policies on data usage, model training, and data residency.

It’s also critical to ensure that AI tools align with regulatory requirements and protect attorney-client privilege, especially when processing sensitive legal documents at scale.