Traditional platforms focused on administrative tasks such as document storage, billing, case management, corporate secretarial work. Research databases like Westlaw and Practical Law came closer to legal analysis by surfacing precedents and statutes, but they deliver raw materials, not drafting or reviewing legal documents from scratch.

Integrating AI to the same features

With the rise of LLMs, vendors added AI to what they already sold. Summaries instead of raw court decisions. Synthesized answers instead of search results. Automated tagging instead of manual classification. The infrastructure remained the same, the output just got faster.

As LLMs demonstrated they could draft and summarize at scale, generative AI was supposed to reduce working time drastically, make deliverables more predictable and bring costs down. But AI legal softwares weren’t delivering on that promise. As Jordan Bryan recently wrote, “many lawyers report these products aren’t reliable enough for them to trust and don’t do much more than ChatGPT.”

AI features accelerated the margins, they didn’t touch the core of legal work.

Distribution came first

Why did vendors ship incremental features instead of tackling the service itself? The answer, as Bryan highlights, lies in incentives.

Whoever captured market share before the technology matured would own the market when it did. The strategy was to reach as many clients as possible, not product quality.

Legal tech companies positioned themselves as trusted partners for the years ahead. When firms subscribed to Legora or Harvey, the bet was less about immediate competitive advantage and more about backing the right horse before the technology matures. Distribution first, product consolidation later.

From tools to services

The end users don’t want a clause-by-clause comparison table. They want their shareholder agreement drafted. The service itself is becoming the product.

Legal tech startups scaled products first to lock in clients. Now they’re scaling services, where the real value lies. They are building orchestration capabilities into their platforms, inviting lawyers and clients to create workflows, share them, and make the platform the hub for legal service delivery.

In June 2025, Harvey launched Workflow Builder, a tool that lets legal teams create custom AI-powered workflows without code. Over 18,000 workflows have been created since launch. The same month, Legora introduced Workflows, an agentic framework that orchestrates multi-step legal tasks, calling on multiple tools and data sources in a single sequence.

These platforms are moving beyond features toward full service orchestration. They’re hiring experienced lawyers to encode expertise into their systems. And they’re repositioning how services reach end users. Harvey introduced Shared Spaces in December 2025, allowing firms to collaborate with clients directly through the platform. Legora is launching white-label client portals in early 2026, connecting both sides of the legal relationship, lawyers and clients, within a single environment.

They now control both workflow creation and distribution.

Some are positioning to avoid intermediaries, keeping control over the entire value chain. Crosby, an AI-native law firm in the US, builds services while retaining distribution. But not everyone can build a firm from scratch. Scaling alone is hard. Workflow creation, distribution, maintenance, deployment require resources most practitioners don’t have. In many jurisdictions, law firms face restrictions on raising external capital, making it difficult to compete with well-funded legal tech platforms.

The agentic turn

Foundational model providers offer alternatives that may prove more robust. They sit at the source.

Anthropic’s Skills standard offers something different. A Skill teaches an agent how you work. The analogy is training a new associate. You share templates, explain methodology, provide reference resources, guide step by step. A Skill packages that for an AI agent. Expertise encoded once, applied consistently.

This allows the entire sequence to be defined. From a tailored intake through document analysis, legal research, and draft production. No step left implicit. The workflow becomes executable and you can create it yourself.

Skills and native orchestration through tools like Claude Code are not just alternatives. They are becoming the standard for agentic AI. They manage context and agent orchestration, which is the hard part. When Meta acquires Manus for two billion dollars, it signals how difficult it is to replicate these capabilities. Foundational model providers will likely remain best positioned to advance these agentic systems. They’re the safer long-term bet.

Of course, legal tech platforms offer more than orchestration. Data security certifications, compliance frameworks, enterprise integrations, dedicated support. For large firms and corporates with strict data governance requirements, these guarantees matter.

But alternatives are emerging for legal professionals who want to distribute services on their own. Skills make orchestration accessible without platform subscription. Security and compliance tooling is still maturing. If these tools reach entreprise-grade standards, lawyers could package and distribute their expertise directly.

Who captures the value?

The question is now open.

Legal tech companies are likely to capture the service layer for large corporates. They have the infrastructure, the capital, and they’re already embedding into enterprise legal departments.

AI platforms like OpenAI, Anthropic, and Google add another layer. They’re becoming the entry point for legal questions, capturing demand at a scale neither firms nor vendors built.

And the market is fragmented. Mid-sized companies with one in-house counsel managing everything. Small businesses with occasional needs. Each segment consumes legal services differently. Understanding who needs legal services, where they are, how to reach them will become critical. Innovating on distribution will matter as much as innovating on the service itself.

It’s also worth noting that AI won’t just redistribute value. It will probably expand the market. By surfacing legal risks no one identified before. By raising awareness of existing ones. By creating new risks altogether.

The transformation was probably never about buying better software, but about how value gets redistributed when AI reshapes an industry.

Who builds, who distributes, who captures?

Subscribe now

Legal expertise has a problem. It’s trapped in people’s heads. When a senior partner retires, thirty years of pattern recognition walks out the door. When an associate leaves for another firm, their methodology goes with them.

Firms try to solve this with knowledge management systems. They build document repositories, write practice guides, create checklists. But these are passive. They sit there, waiting to be read. And mostly, they aren’t.

Junior lawyers learn by osmosis. They watch, they ask questions, they make mistakes, they gradually absorb how things are done. It works, but it’s slow and inconsistent. Two associates trained by different partners will review contracts differently.

This is the state of legal knowledge: valuable, fragile, and frustratingly non-transferable.

From knowledge to methodology

AI agents are increasingly capable of legal work. They can read a contract, identify a non-compete clause, summarize its terms. That part is commoditized. Many models can do it.

What no model knows out of the box is that your firm flags non-competes over 12 months as concerning, that you always check for carve-outs protecting general skills, that your partners want findings structured as Key Terms / Risk Areas / Suggested Revisions.

That’s methodology. And methodology is what differentiates one firm from another. Agent Skills let you encode it.

What are Agent Skills

A Skill is how you close that gap between generic legal knowledge and how your firm actually practices.

When a lawyer builds a Skill, they’re packaging their methodology into something an AI agent can pick up and apply. Every time, consistently.

The format is simple: a folder with instructions, checklists, reference materials. A senior associate who’s never written a line of code can create a Skill that captures how they review NDAs, what they flag, how they structure their findings.

What changes in practice

Three things happen when methodology becomes portable.

• Knowledge preservation. When someone leaves, their Skills stay. The expertise they encoded keeps working.

• Repeatable output. Right now, if you ask an AI to review a contract, you’ll get a different result each time. Different structure, different emphasis, different things missed. A Skill pins that down.

• Compounding quality. A Skill isn’t static. When a review misses something, you update the Skill. When a partner flags a new risk pattern, you add it. Every improvement applies to every future review automatically.

An open standard

Skills aren’t locked to one AI provider. The format is an open specification, already supported by major models including Claude, Gemini, and OpenAI’s Codex.

This matters. It means the methodology you encode today isn’t trapped in one vendor’s ecosystem. As AI platforms evolve, your Skills stay portable. You invest in capturing expertise, not in a particular product.

The long-term goal is a common language for teaching AI agents how to do specialized legal work.

The economics shift

If methodology can be encoded, competitive advantage changes.

Small firms can punch above their weight. A three-partner boutique with well-built Skills can deliver consistency that rivals a BigLaw practice.

Routine work commoditizes faster. If an AI can apply a firm’s methodology reliably, the value of having a human do routine reviews diminishes. This isn’t new (it’s been happening for years) but Skills accelerate it.

Value migrates. The premium shifts from “doing the work” to “designing how work gets done.” The lawyer becomes a methodology architect, an orchestrator, and a reviewer. The AI does the rest.