ALSPs: Legal AI’s Secret Weapon

Why this segment of the legal ecosystem will be critical to AI adoption among lawyers

In my previous article, I argued that the real bottleneck for legal AI isn’t product quality—it’s distribution. Startups, legacy giants, and even law firms won’t be able to get AI into the hands of lawyers at scale. That raised an obvious next question: if those channels won’t work, what will?

In this article, I’ll make the case that alternative legal services providers (ALSPs) are uniquely positioned to fill that role. ALSPs are embedded in client workflows where AI can make the biggest difference, are funded from budgets clients already understand, and trusted by the decision makers who matter.¹

Here’s what I’ll cover:

1. Why ALSPs Are Natural Distribution Channels

2. It Happened in E-Discovery

3. The Coming Wave (AI Agents)

4. Traits of the Ideal ALSP Partner

5. Where the Market Is Headed

6. Conclusion

Before I get into it, I want to cover two important points (that I also shared last time) that I’ll share again::

• In this piece, I refer to “AI” generally, but my focus is on generative and agentic AI.

• These are my own views, and do not represent the views of Latitude Legal, Stanford Law School, or any other organization I’m a part of.

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1. Why ALSPs Are Natural Distribution Channels

The barriers that slow AI adoption in legal—unclear budgets, long procurement cycles, and client skepticism—are the exact areas where ALSPs have an edge. Their positioning, incentives, and track record with legal buyers make them uniquely suited to act as distribution channels for AI.

A. They’re Already Embedded in Workflows

ALSPs are engaged directly in the streams of legal work where outcomes depend on coordinating multiple tasks—commercial contracting, technology implementations, litigation support, document-heavy investigations, etc. They don’t just deliver tools; they put people inside these processes to ensure the work gets done.

That positioning makes them uniquely suited to introduce AI. By contrast, many AI tools today are marketed as abstract generalists, e.g. “AI legal assistant.” That label may spark imagination, especially from outsiders/investors, but they don’t map cleanly onto how legal work is actually structured.²

Legal tasks are interdependent and outcome-driven. Buyers don’t want a free-floating assistant; they want assurance that a specific workflow gets done correctly. ALSPs are already inside those processes, which allows them to deploy AI in a way that feels specific, contextual, and safe.

B. There’s Budget Alignment

ALSP engagements are funded from budget categories legal departments already rely on—outside counsel and professional services. These budgets are stable and recurring, and are relatively uncontroversial.

By contrast, legal technology is often funded from fleeting experimental AI resources or shared budget from other departments. At first, these resources appear significant. But the dollars rarely sustain over time. Once they shrink, startups whose product is attached to them face stalled renewals and declining net dollar retention³ because clients don’t know who should own the spend.

ALSPs avoid this trap by tying AI to commonly accepted categories of spend that have existed for years. To the client’s CFO, spend on AI doesn’t require “new money” that needs to be justified; it simply fits into buckets of dollars the legal department was already planning to spend.

C. They Have the Ability to Drive Innovation

ALSPs, by their very nature, had to prove themselves as innovators from day one. Unlike law firms, they couldn’t lean on legacy brand power or the safety of precedent. They had to persuade cautious, risk-averse clients to move work outside the traditional model.

That meant demonstrating why their approach was not only more cost-effective, but also safer, faster, and more reliable. In doing so, they built a unique organizational skill: the ability to make change feel safe for conservative legal buyers.

That same skill translates directly to AI. Where a startup pitching an “AI assistant” can sound abstract and untested, an ALSP can leverage their sales and marketing teams to frame AI as a practical extension of existing services. That shift in framing makes a huge difference.

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Now don’t get me wrong. Relying on ALSPs for distribution isn’t a magic bullet. The client still needs to run security and risk review, and approve the AI for internal use. You can’t sneak AI through the back door by having an ALSP use it for client matters without any vetting.

But here’s the upshot: AI deployed through ALSPs will be viewed very differently at the outset. Instead of clients reacting to an AI startup with: “Who are you and why should we trust you with our data?” the framing becomes, “Your partner’s been doing sensitive work for us for years—help us understand how your technology helps them be even more efficient.”

I’m not making this up—there’s already some precedent for this.

2. It Happened in E-Discovery

There’s some historical precedent for what I’m articulating.⁴ In e-discovery, Relativity didn’t become the dominant platform because every firm/legal department rushed to buy licenses directly. They didn’t raise a ton of money at the outset, have flashy marketing, or advertise monstrous valuations.

Their go-to-market was instead designed around how lawyers viewed technology. They didn’t want to deal with the hassle of standing up servers, training staff, or taking on the risk of a new system. The lawyers recognized that all that was outside their core competency.

Instead, adoption spread through a fragmented ecosystem of local vendors who hosted, customized, and supported the platform. To the clients, it felt like they were simply using a vendor they trusted, not “adopting Relativity.” That helped the technology scale in those early days, and quickly become a necessary tool in the lawyers’ day to day.

Now like many other providers that outsourced distribution at first, Relativity did eventually build their own direct distribution. But that was a deliberate strategic choice made years after they’d already become the market leader.

ALSPs are positioned to go further

It’s worth noting that the work many lit support vendors do, by their nature, is highly reactive. Generally, they only get pulled in by a law firm after a litigation or investigation is already underway. Other ALSPs, by contrast, may sit closer to the origin point of legal work. For example:

• At law firms: Instead of merely standing up a doc review environment when the litigator receives a production from opposing counsel, a higher end ALSP might be involved during the pleading stage.

• At legal departments: Instead of merely reviewing NDAs that the commercial team sends over, a higher end ALSP might be involved in developing playbooks and SLAs with the in-house lawyer’s internal clients.

All that early stage visibility gives the AI-enabled ALSP the opportunity to proactively reshape downstream work. You don’t want AI to incrementally improve traditional processes designed for a pre-AI world.

The takeaway is this: The winners in the legal AI arms race are the ones who can drive adoption of redesigned workflows. To do that they need to be trusted by the client, and have early visibility into processes that take place upstream. This is absolutely critical for anyone building AI agents designed for legal.

Which brings me to my next point.

3. The Coming Wave

If the first wave of generative AI felt disruptive, imagine what the next wave will feel like. Agentic AI doesn’t just produce text—it goes ahead and “acts” on your behalf.

Imagine an agent that redlines a contract against playbook terms, circulates it to the right stakeholders, and logs the approval into the CLM. Or one that monitors regulatory changes, compares them against policies, drafts suggested updates, and pushes them to compliance.

Or in the law firm context, picture an agent that pulls precedent from the DMS, drafts a first-pass motion, cites relevant authorities, and sources comments from a senior associate, before passing it on to a partner.

These aren’t discrete tasks. They’re multi-step workflows that look and feel like real legal work done by a human being.

Legal work is different from other types of work

The leaps that AI agents promise dramatically raises the stakes. And this is what many outsiders don’t understand about the fundamental nature of legal work.

When a sales-focused agentic AI gets something wrong, the cost is minimal—maybe you send out the wrong e-mail, or maybe you fail to schedule a sales appointment. Annoying but not catastrophic.

When legal-focused agentic AI takes the wrong step—like accidentally filing the wrong version with the SEC or sending privileged documents to opposing counsel—the consequences are devastating.

As a result, the primary question about agentic AI isn’t “does it work?” It’s “how do we make sure that never happens?” And potentially even more important: “who is ultimately accountable when something terrible happens?”

Ensuring that a trusted human is involved

This is where ALSPs come in. They regularly deliver outcomes to clients, and their people are already accountable for the work. Embedding agents into this system doesn’t look or feel like a scary leap; it may not even be noticeable.

Humans—contractors, employees, and staff of ALSPs, can design escalation points, validate outputs, and ultimately stand behind their results. Which makes agentic AI feel like a seamless extension of existing services.

I truly believe that’s how agents will rapidly scale in the legal vertical. Yes, some of it will happen through direct adoption by pure AI companies. But a ton of it will just happen through intermediaries who make the agents seem invisible. Which brings me to the next question:

Which intermediaries are likely to be most effective in this new world?

4. Traits of the Ideal ALSP Partner

All ALSPs have some potential to play a significant role in AI adoption. But certain traits make some especially well-suited to lead the way. These characteristics don’t just make distribution possible; they make it smoother, faster, and more credible in the eyes of clients. Here are 4 examples of key traits of the “ideal” ALSP to partner with:

A. Already Embedded in High-End Workflows

The first trait is being embedded in high-end workflows. If an ALSP is doing associate-level or interim counsel-level work (drafting, negotiating, advising) AI can be slotted into those workflows seamlessly. The tasks that make up those workflows can be shifted around to address the high level goals of the legal work itself.

By contrast, ALSPs focused on lower-end tasks are too far from upstream factors to shape workflows and influence how/where AI fits in. Example: An ALSP specializing in first level doc review or data hosting will have limited opportunities to drive efficiency. Conversely, an ALSP that has exposure to early negotiations during the discovery process of litigation has the potential to impact what files/documents get exchanged by the parties.

Which ALSP do you think AI startups would rather partner with?

B. Trusted by Senior Decision Makers

The second trait is trust with senior decision makers. Nothing moves in legal without a senior in-house counsel or law firm partner giving the green light. An ALSP that has already delivered sensitive, high-stakes work has built credibility with that exact audience, which will accelerate adoption.

As a result, clients won’t see AI enablement as an unfamiliar or risky proposal. Instead it’ll be viewed as a recommendation from a trusted third party who has already proven reliability on highly sensitive matters. The technology review process is still there, but will be guided by confidence in the relationship rather than doubt about the messenger.

C. Lawyer-In-The-Loop Accountability

The third trait is having a licensed attorney involved in overseeing the work AI helps with. Theoretically, any competent professional can do this. But licensed lawyers face consequences that go far beyond a bad outcome—they can lose their license and derail their entire career. That reality raises the stakes and creates a heightened threshold of trust.

When experienced attorneys are involved, clients know there’s someone who is betting their license on validating the AI’s outputs, monitoring escalations, and managing risk. This layer of professional responsibility makes AI adoption feel less like an experiment and more like a mere extension of existing legal services.

D. Quiet, Outcome-Focused Delivery

Finally, the best distributors lead with quality work—not marketing driven hype. Legal buyers generally don’t see themselves as loud early adopters. They view themselves as more traditional types who quietly do good work. Accordingly, the market positioning of ALSPs that will be most effective might take a more understated approach.

Personally, I think it’s better if the AI operates in the background early on. The marketing pitch should revolve around faster turnaround times, better outcomes, and consistently high quality. The AI will be there, but working in the background. Adoption becomes a foregone conclusion because it feels less like change management and more like organic improvement.

Eventually, after the AI has been de-risked, and the technology has moved from the early adopter phase to the early majority phase—startups can re-evaluate.

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The ALSPs that combine these traits—embedded in high-value work, trusted at the executive level, accountable for results, and quietly focused on outcomes—are the ones positioned to become the most effective distribution channels for AI.

5. Where the Market Is Headed

We’re already seeing signs that tech and services are starting to converge. First, multiple providers on both sides, ie. AI startups and ALSPs, have begun to partner with one another.⁵ Second, some startups are already pushing AI-enabled services directly to clients. These developments suggest that services will increasingly play a large role in early adoption of legal AI.

However, the ALSP-first distribution strategy I’ve been describing here is *not* the most popular one right now. I mentioned this briefly in my previous article, but it’s worth re-emphasizing: The current strategy in legal AI go-to-market right now is the social proof/valuation-led approach. Which I’ll admit, can be incredibly effective in the legal vertical. From Ken Priore:

While competitors chased demos, press releases, and conference buzz, Harvey quietly cracked the code on BigLaw's actual decision-making process. The insight? Law firms don't buy technology—they buy social proof. "What other firms are using this?" isn't just the first question; it's often the only question that matters in the initial evaluation. Harvey recruited former BigLaw partners who understood this psychology, then methodically secured Allen & Overy, Paul Weiss, and PwC as early adopters. No flashy marketing. No thought leadership campaigns. Just strategic relationship-building with the right insiders at the right firms.

I think this is right, but only if you combine it with Harvey’s continuous stream of fundraising announcements. No one *really* knows if their product is better, but everyone’s aware that they raised $500M from leading VCs and are worth $5 billion, just a few years after founding.

Harvey clearly knows what its doing here—their approach to the market has helped them achieve $100M in ARR in record time. But consider the more relevant question for everyone else: What happens to the second, third, fourth etc. startup that follows the same playbook? Will they be able to beat Harvey (and Sequoia, Kleiner Perkins, etc.) at their own game?⁶

Conclusion

The biggest risk for founders, employees, and investors of legal AI companies, isn’t whether they can build a good product. They probably can. It’s whether these technologies can find their way to this group of conservative, risk averse lawyers.

Most of today’s startups are betting on direct sales, hiring teams of AEs and SDRs, and flooding the same handful of conferences and marketing channels. That playbook looks familiar, but in legal it’s brutally expensive, slow, and rarely sustainable.

To me, the overlooked channel is ALSPs. They already sit in the workflows where AI matters most, already pull from budgets that legal departments know how to spend, and already have the trust of the executives who make the calls.

And if they become an intermediate layer for AI, then they will ultimately be the ones who define adoption velocity. And startups would be mistaken not to partner with them.

1. From this fantastic article from Legal Evolution:

“ALSP” is an umbrella term used to describe a wide variety of businesses in the legal industry that are not law firms, but which provide legal or related support services. ALSPs usually leverage low-cost labor, technology, and efficient processes to perform certain types of work more quickly and less expensively than many law firms can perform it.

Note that ALSPs also serve law firms, so they’re not necessarily a direct competitor—although they can be, depending on how they’re positioned.

2. When I posted Part 1 of the article to LinkedIn a couple weeks ago, one of the most popular comments identified “finding the right use cases” as a larger obstacle than distribution. I’m not sure I agree, but regardless—by partnering with ALSPs, AI companies will have a clear path to validating use cases. AI will target existing workflows (via ALSPs) that are clearly established.

3. NDR is incredibly important in this highly competitive environment. Without continuing to grow revenue from an existing base of customers, AI startups will be forced to acquire net new customers—which is extremely expensive and often unsustainable at scale.

4. In the early days, enterprise buyers were skeptical about cloud-based software. They worried about data security, integration with existing systems, and workflow disruption—resulting in slow adoption even when the tools offered clear value. Systems integrators—such as consulting partners—stepped in and filled the gap, helping companies deploy and trust new technologies like Salesforce.

That dynamic didn’t vanish once the vendors grew. Even now, major SaaS companies rely heavily on SI ecosystems to roll out solutions at scale—because trusting a familiar intermediary makes a new technology feel less risky.

5. Although I’m bullish on this approach, AI-ALSP partnerships aren’t risk-free for the ALSPs. There’s lots of uncertainty around client demand, technology capabilities, revenue impact, and delivery expectations. It’s also unclear how these factors will interact with each other. It’ll be interesting to see how these early partnerships play out.

6. Even as we all recognize that Harvey is winning in 2025; we should also consider whether their strategy will generate enough momentum to carry them through the years to come. Will adoption and usage expand once the hype dies down and their initial clients are coming to the end of their multi-year contracts?