Legal AI’s Next Breakout

The products are good enough. What’s missing is a way to get them into the hands of the lawyers at scale.

The legal industry is experiencing a flood of new AI tools—contract reviewers, chat assistants, drafting copilots, and more. They seem to launch by the week, each promising to transform the way legal work gets done.

But here’s the reality: building AI is no longer the hard part. Distribution is.

Thanks to foundation models, the technical barriers to creating legal tools have dropped dramatically. Most startups now build on the same platforms. The user interfaces look and feel the same. The core capabilities appear to be increasingly interchangeable.

What used to be a product challenge is now a go-to-market one. It’s not about who can build the best AI tool—it’s about who can actually get people to use it.

This article explores why existing channels—startups, legacy vendors, and law firms—are struggling to deliver AI at scale. I’ll then talk about the traits of an ideal channel, and explain why. And then at the very end, I’ll share a teaser of where I think that distribution channel can be found.

Two important points before I dive in:

• In this piece, I refer to “AI” generally, but my focus is on the kinds of systems that are reshaping legal work most aggressively in 2025: generative and agentic AI. One generates content. The other takes action. More on the difference here.

• The opinions I share are my own views colored by my personal experience. They are my own, and do not represent the views of Latitude Legal, Stanford Law School, or any other organization I’m a part of.

Startups Can Build, But Can’t Reach

Coming up with new legal AI tools has never been easier. Most startups today are built on top of the same handful of foundation models—OpenAI, Anthropic, etc—with nearly identical capabilities. User interfaces have largely converged, too: the workflows center around contract review, chatbots, and summarization tools that look and feel remarkably similar across products.

Some teams claim differentiation through proprietary data or fine-tuned models. While this can matter at the edges, the differences are often invisible to legal buyers and hard to prove. The capabilities may be slightly different but they all *feel* interchangeable.

That wasn’t always the case. Historically, B2B SaaS products in the legal space had some differentiation: in feature set, product vision, and philosophy on workflows. Startups took unique approaches, validated early usage, and used that traction to raise meaningful seed rounds. The goal was to build a product worth trying out, then use that validation to raise funding and build distribution from scratch.¹

Today, that formula is widely known—and widely copied when it comes to legal AI. Everyone raises money to fund growth.² But the larger the round, the more the go to market approach starts to look the same: AE/CSM model, outbound SDRs, sales engineers, premium sponsorships at the same 3-5 conferences, saturating the same digital advertising channels, etc.

The only things that seem differentiated are how much money was raised and who invested.

Think about why some startups have been able to draw disproportionate attention right now: is it really product quality that commands headlines? Or is it the valuation, the investor roster, and the resulting hype?

Consider why lawyers immediately think of Harvey when discussing legal AI. Is it because of anything about the product itself or is it because they’re backed by OpenAI, Sequoia, & Kleiner Perkins, and reached a $5B valuation within just a few years?³

(For a breakdown of funding/hype based advantages, check out Zach Abramowitz’s fantastic article Has Harvey Already Won?)

Legacy Giants Were Built for a Different Era

If startups struggle to scale and law firms lack go-to-market infrastructure, what about the legacy legal tech giants?

For decades, Thomson Reuters and Lexis have defined legal tech distribution. Their dominance came from a potent combination of brand trust, embedded access, and control over proprietary legal content. They reached users early—starting in law school—and remained central to core legal workflows like research, citation, and compliance.⁴

But AI doesn’t fit neatly into their model.

These companies are built to distribute static tools into familiar workflows—tools that lawyers already know how to use, often procured top-down by centralized law firm buyers or IT. The latest iterations of generative or agentic AI by contrast, requires behavioral change, uncertainty tolerance, and iterative refinement. It’s dynamic and messy, not structured.⁵

One reason may be strategic: TR’s true long-term advantage isn’t AI—it’s data. Its moat is built around proprietary legal content, annotations, analytics, and decades of structured information.⁶ That’s where it has pricing power and defensibility. If AI tools commoditize, TR is incentivized to treat them as wrappers around its core data—not as standalone innovations that could undermine it.

The legacy giants’ dynamic makes sense from a business perspective—but it also reveals the limits of relying on legal tech incumbents to usher in AI transformation. They may invest in AI, but they’re unlikely to be the ones to restructure how the work gets done.⁷

What About Biglaw?

Compared to legacy giants, BigLaw may seem like the ideal channel for legal AI. Firms are deeply trusted by corporate clients, embedded in critical workflows, and highly effective at expanding services through lateral hiring, cross-selling, and geographic or practice-area growth.

Some firms are starting to show real promise. By now, many already use AI for client matters. A few have successfully launched their own subsidiary service providers, invested in proprietary AI initiatives, or even begun commercializing internal tools.⁸

But these remain outliers.⁹

The reality is that Biglaw is built to sell legal advice—not novel products. Evangelizing innovation requires a different kind of muscle. You can’t just give clients what they already ask for; you have to reframe problems, guide workflow changes, and navigate long sales cycles.

Driving AI adoption requires coordination, sustained investment, and repeatable sales infrastructure. Most law firms aren’t structured for that. They typically lack

• Incentives that reward experimentation or go to market investment

• CRM systems or visibility into client buying patterns pre-revenue

• Professional sellers & marketers with expertise on how to pitch novel solutions to old problems

• Unilateral decision-making authority necessary to move quickly to direct firmwide investment

There’s also a key blind spot: while BigLaw can influence corporate buyers, it has limited leverage with peer firms. Distribution within the firm’s client base is possible—but across the broader legal market remains unsolved.

What Makes a Strong Distribution Channel for Legal AI?

Rather than immediately come up with a solution, what we should do instead is to ask: “Well what traits actually make a distribution channel effective for legal AI?” I don’t have all the answers, but here are a few that quickly come to mind:

1. Embedded in the Work

Startups often treat the sale as the finish line, but in legal, the buying decision is just the beginning. Without ongoing support and integration, much of the technology becomes shelfware. This is especially true for tools that require behavioral change—those that introduce entirely new workflows, rather than improved versions of existing ones.

As a litigation associate, I used to be surprised by how many of my colleagues struggled with newer, better doc review platforms or resisted abandoning Boolean search. But that experience reflects a deeper truth: in law, even incremental change can feel disruptive.

Adoption is far easier when the tool is delivered by someone already doing the work. Rather than selling AI as a standalone tool, the most promising distribution models wrap it inside services already being delivered—making adoption nearly invisible—but nevertheless impactful.

2. Trusted by Senior Decision Makers

The first step in any legal AI adoption is a senior decision maker choosing to move forward. That means the person introducing the tool must have peer-level trust with CLOs or law firm equity partners—leaders who think in terms of resourcing, risk, and strategic outcomes.¹⁰

Salesforce beat its competitors not because it was easier to use, but because it sold directly to sales leaders who understood the problem and had authority to act.¹¹ In legal, it’s a similar situation: ease of use helps with adoption. But economic buyers and senior decision makers must first determine that the tool drives outcomes that matter to the business.

Many startup GTM staff (sellers) lack the credibility to deliver that message. They’re often recent grads or generalist reps without legal experience, unable to speak to senior stakeholders in their language or context.

By the way, agentic AI heightens this trust requirement—it’s not just summarizing or drafting, it’s acting on behalf of the lawyers. That makes peer-level trust from clients even more important, especially when accountability and risk are shared.

3. Internal Operating Maturity

There’s no playbook for distributing legal AI—so success depends on learning fast through structured trial and error. That means tracking what’s working, what’s not, and why.

Startups often have the infrastructure in place: CRMs, analytics tools, and scalable databases. But they struggle with data hygiene and sample size—sales notes are inconsistent, outcomes go untracked, and insights don’t scale. Without clean, consistent inputs, you can’t generalize lessons or build repeatable motions.¹²

It also requires training. Sellers and delivery teams need to know how to talk about AI credibly—what it can do, what it can’t, and how it fits into legal work. And importantly, what that means for the end clients.

Without that baseline, even the most groundbreaking AI features get lost in translation.

4. Incentive to Scale

Distribution only works when the people involved have a reason to make it succeed. That’s where many law firms fall short—selling legal advice generates cash flow, but it doesn’t build enterprise value. There’s limited motivation to invest in scaling something that doesn’t fundamentally change the firm’s economics.¹³

Startups are structurally better positioned, but their incentives often drift. Once they gain initial traction in legal, many start chasing larger TAM in other verticals or add features outside their core competency. That may make sense for product development—but for distribution, it creates whiplash.

To succeed in legal, you need a focused, repeatable go-to-market motion. Constantly shifting messaging, features, and positioning makes it harder to build trust and momentum with buyers.

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Conclusion

If legal AI is going to scale, it needs a new type of distribution channel—one that’s embedded, trusted, and built for outcomes. Most startups focus on building their own distribution, but struggle with execution. Established categories of distribution channels—legacy giants & Biglaw firms—all come up short in some way.

This article has largely focused on the limits of those existing channels and the traits held by high potential channels. In my next article, I’ll explore where the real opportunity may lie: service businesses that already work with law firms & legal departments and have firsthand exposure to workflows.

They’re called “alternative legal services providers.” And that’s who I will be talking about next time.

Stay tuned, my friends!

1. If you take a look at the CLM category, as an example, some products focused on pre-signature and others focused on post-signature. These cloud native applications contained some overlap with one another but they were at some level quite differentiated. When I was at Evisort, our engineers/data scientists prioritized our AI-first repository & related features; later at Ironclad, I noticed our strongest features were pre-signature workflows, like routing approvals and drafting templates. Eventually more entrants emerged in the space, causing significant overlap with Evisort, Ironclad, and other incumbents. When a sector becomes hot, investors pile in and fund copycat products—which is what’s happening now in legal AI.

The same is true for e-discovery; the various providers were focused on various points of the EDRM. Logikcull, as an example, had a highly intuitive drag and drop interface that made processing super easy; Relativity contained a robust functionality and enterprise-ready features (at the expense of usability, which was a point I constantly hammered home as a young account executive).

2. One additional challenge: Right now legal AI budgets are flowing, and buyers are often making purchase decisions just to show their stakeholders that they’re doing something with AI. In the old world, gaining 100k in ARR, or a few paid pilots, might have been a strong signal of product market fit. But now, it’s not. So you now have a flood of AI startups with phantom traction armed with multi million-dollar war chests all coming to market with minimal validation.

3. Another recent example is EvenUp which raised $135M at a $1B+ valuation last fall. They have a roster of blue chip VCs which enabled them to stand out from the crowd from a brand awareness perspective. The same phenomenon played out several years ago when Ironclad rapidly raised funding from leading VCs at sky high valuations.

Trouble is, if you’re a startup that raises a “generic” $5m to $10m from relatively unknown investors, you will not benefit from these same tailwinds.

4. There’s an interesting lesson here about what type of brand plays work in legal. Both legacy giants and Biglaw embed their brands within the minds of impressionable law students—and the brand associations remain decades later. Thomson Reuters and Lexis give out their legal research product for free in law school, and hire students as their representatives. Biglaw firms create unprofitable summer associate programs and lavish students from top schools with all kinds of perks. These brand marketing plays are unavailable to startups and other smaller providers—they require decades long time horizons and significant resources to pull off. (Legal research startup Ravel Law had a similar law school program back in the day, but after they were acquired by Lexis, it basically disappeared)

5. The Casetext acquisition by Thomson Reuters highlights the tension. Casetext was one of the most visible early movers in legal AI, with strong brand recognition and early market traction. But after it was acquired for $650M by TR, things seemed to slow down. Chatter on the Internet suggests that momentum has slowed—and that elements of their legacy product have been shuttered.

6. TR understands where its moats lie, leading it to engage in litigation against new entrants on occasion to defend its turf.

7. A recently minted giant that might hold some upside potential is Clio. Unlike the legacy giants, they don’t depend on proprietary data for a competitive advantage; and unlike typical hype-driven startups, they have a real proprietary distribution channel—the ecosystem they’ve built around their core product, the annual Clio Con conference, and a wide range of programs & initiatives. That likely gave them (and their investors) the confidence to acquire a significant legal research provider in vLex for a cool billion dollars. Personally I think certain flavors of AI may actually do quite well under their umbrella—but not necessarily those that are workflow driven, like agentic AI.

8. I wrote about how legal AI startups can take a page or two out of the Biglaw GTM playbook in an article from last year. Basically, trust and credibility matter a lot in the legal vertical, and large law firms have that in droves.

9. Here’s a well written article from 2021 about the potential challenges of captive ALSPs. Also consider the recent news from Bob Ambrogi that SixFifty, a Wilson Sonsini subsidiary, was sold off to Paychex. Some of the commentary highlighted this as a success story but in my view—if it was so successful, why did they get rid of it?

10. What I’m talking about is another way of saying, having a trusted brand matters. Not just your law firm brand, but the brand of the individuals who work within the firm. See my earlier article on this subject. This extends to others throughout the ecosystem. Your personal brand matters because it demonstrates accountability to the community. This is where AI startups struggle—building that trust takes a long time, a luxury they do not have.

11. As every account executive knows, SFDC is designed for the bosses, not the users. That was the critical insight into driving adoption across all companies. See this article.

12. I also suspect that the types of GTM executives who end up joining early stage startups are surprisingly uncomfortable with uncertainty. They tend to come from BigCo with pre-prepared playbooks and formulas.

13. The billable hour poses obstacles but it’s not the biggest one—I don’t even think it’s in the top 3. Organizational incentives, compensation structures, core competency of the law firm, all play a bigger role.