Why Legal Teams Need More Than Generic Generative AI — and What to Look for Instead

Generative AI (GenAI) is reshaping how legal work gets done. But as the hype fades and the real decisions begin, many legal teams are running into the same problem: most tools weren't built for this field.

Whether you're in-house or at a mid-sized firm, you're likely being asked to move faster, reduce costs, and handle more complexity — all without adding headcount. It's a familiar story across the legal industry right now, and it’s driving teams to take a closer look at how legal work is supported, structured, and scaled — especially as collaboration increasingly happens across teams, jurisdictions, and platforms.

In-house legal teams are being asked to rethink their mix of internal vs. external support, with more matters staying in-house. At the same time, many are part of broader digital transformation efforts, where other business units are already using AI to reduce manual tasks and cut spending. Legal is expected to follow suit — but often with more scrutiny, less tooling, and higher risk.

Reflecting these pressures, a recent Gartner survey found that 64% of legal and compliance leaders plan to accelerate investments in legal technology. This surge in interest underscores the urgent need for solutions that can help legal teams manage increasing workloads without sacrificing quality, compliance — or the ability to collaborate effectively across the organization.

Mid-sized firms, on the other hand, are navigating a different kind of pressure: keeping pace with larger competitors while staying lean. There's growing interest in tools that can help attorneys focus more on strategic work by reducing time spent on research, review, and drafting — but without the overhead of custom solutions or large legal ops teams. Just as importantly, they need ways to work more efficiently across distributed teams and ensure consistency across client deliverables.

Most foundation models were trained on internet-scale content and designed for broad use: writing emails, summarizing reports, generating code. They're flexible, fast, and surprisingly fluent.

But legal work isn't just about fluent writing. It's about structure, precedent, and detail. It's about interpreting laws that vary by jurisdiction, citing the right authority, and maintaining internal standards that align with professional and regulatory obligations.

GenAI can generate language that sounds right — but that’s not enough when the risks of inaccuracy are so high in legal work. The consequences of even minor mistakes can be severe, including regulatory breaches, financial penalties, or reputational harm. Because of these stakes, generic AI tools often fall short: they may produce plausible-sounding answers that cannot be relied upon in practice. That’s why every AI-generated output must both be grounded in verifiable sources — traceable to statutes, regulations, or case law — and be carefully reviewed by qualified legal professionals to ensure it meets the highest standards of accuracy, compliance, and ethical responsibility.

The term “purpose-built” gets used a lot. But in practical terms, a legal AI platform should be designed to support the way legal professionals already work — and help them do it more effectively. That includes:

The early phase of GenAI adoption was exploratory — testing outputs, comparing interfaces, and seeing how far the technology could stretch. Now, teams are taking a more critical approach.

Some of the most common questions we're hearing include:

These aren't just IT questions — they're operational ones. And they're increasingly shaping how legal teams evaluate AI tools.

If you're starting that evaluation process — or need a framework to guide internal discussions — our new resource, 7 Key Criteria for Evaluating AI Solutions for Law, outlines the most important factors to consider. It's based on real conversations with legal teams across industries, and designed to help you cut through marketing claims and focus on what matters most in practice.

Download the guide for actionable steps, real-world examples, and a practical framework to help your team make informed decisions about legal AI adoption that you can feel confident in.