In the last few months, Harvey announced Contract Intelligence. Anthropic launched Claude for Legal with practice-area plugins. DocuSign unveiled agentic contract workflows.
Last year brought more of the same: LexisNexis announced Protégé™ General AI, Thomson Reuters announced agentic capabilities in CoCounsel Legal, OpenAI published a detailed account of building an internal contract data agent.
That’s not a coincidence.
The market is converging around the contracts space using ever-improving AI capabilities — from creating “contract intelligence” to building end-to-end “workflows.”
But there’s a lot missing in the conversation around these announcements. So let’s unpack what it means and why it matters.
To start — contract intelligence
I’ve spent about a decade working on this problem.
I started in 2015, inside a small contracts business at Axiom that eventually became Knowable, a LexisNexis company. Back then, the deliverable was a spreadsheet containing structured data. The deliverable was created using a combination of people, process and technology: project managers, subject matter experts and analysts running a specially designed review process on large volumes of enterprise contracts using specially configured eDiscovery software.
There was no software to buy that did this reliably. Early contract AI vendors were able to sell and market the tools but struggled to deliver results. The concept of turning large volumes of contractual text into structured data and analytics to solve legal and business problems predated the term “contract intelligence”.
That is why the current wave is so interesting. The concept is old but the technology is new and continues to evolve.
Same space, different layers
These announcements are solving for different things in different ways, but they’re all filed under similar labels.
Harvey’s Contract Intelligence appears to be a portfolio-level playbook management system — surfacing negotiation patterns, fallback positions, and clause language from executed agreements to make future review faster. The goal is that every signed contract feeds back into and updates the playbook automatically. This looks like a workflow-compounding capability aimed at in-house teams doing high-volume, BAU contract reviews.
Claude for Legal is positioned as the intelligence that connects to other tools and enterprise systems, not a standalone contracts product. It includes practice-area plugins, first-pass review and redlining, and MCP integrations to help do so.
OpenAI’s contract data agent was announced as an internal tool for their own finance team: ingesting PDFs and scans, extracting structured data, and serving it up for human-in-the-loop review. The purpose of the tool is to meet the scale of a growing business with limited headcount.
DocuSign’s agents operate inside its Intelligent Agreement Management platform: checking agreements against company standards, flagging risks, tracking obligations, and allowing teams to build custom agents for deal and renewal workflows through Agent Studio. The use cases overlap with what others cover, but the emphasis is on a platform-ecosystem anchored in their e-signature capabilities.
While the use cases sound similar on the surface, these providers are addressing different workflows that most announcements don’t unpack.
And if you’re a contracts professional, in-house counsel, legal ops leader, or someone running a deal process, it’s helpful to break down the announcements:
Which part of my actual problem does each one address?
How well does it address each part of the problem?
What parts of the problem are left unsolved?
What trade-offs come with choosing one approach over another?
The Root Causes of Complexity
Those questions are hard to answer from the announcements alone because the announcements are built as much to impress as to inform.
The announcements rarely explain the assumptions, i.e. what must already be true for the product to work well, not just demo well. These assumptions include: the scope has been defined properly and the relevant contracts have been collected, organized, and analyzed in a way that the output can be trusted and used in the company’s legal and business processes.
Why I’m writing this
The goal of The Contract Signal is to give people who work with contracts an independent read on what the capabilities can and can’t do and how to use them to solve real problems. I come at this from a decade of working across ML and LLM product development, contract analysis, solution sales enablement, and end-user research, with cross-functional visibility into how enterprise contracts work actually gets sold and delivered.
I am not promoting any particular vendor or employer.
If you work with contracts, buy or evaluate AI tools for legal workflows, or build in this space, subscribe to follow along.