The contracts AI announcements keep coming. They’re not solving the same problem.
The market is converging on contracts AI. But 'contract intelligence,' 'agentic workflows,' and 'practice-area plugins' aren't the same thing — or where most of the time goes.
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. The engine was people: project managers and analysts running a quality-controlled review process on enterprise contracts, turning dense legal language into structured data someone could actually use.
There was no software to buy. The concept we now call “contract intelligence” predated the term — and predated most of the tools now claiming it.
That is why the current wave is so interesting. The concept is old. The technology is genuinely new and continues to evolve. And the gap between those two things is where the market is hardest to understand and is where the useful analysis lives.
Same space, different layers
Here’s what’s worth noticing: 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 promise is that every signed contract feeds back into and updates the playbook automatically. That’s a workflow-compounding play aimed at in-house teams doing high-volume, BAU contract reviews.
Claude for Legal is a horizontal capability layer: practice-area plugins, first-pass review and redlining through Cowork, and MCP integrations that connect Claude to other systems in your stack. It’s positioned as the intelligence underneath other tools, not a standalone contracts product.
OpenAI’s contract data agent is an internal build for their own finance team — ingesting PDFs and scans, extracting structured data with retrieval-augmented prompting, and serving it up for human-in-the-loop review. The goal 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 serve, but the emphasis is on a platform-ecosystem anchored in their e-signature capabilities.
These are not competitors in the simple way a feature comparison might suggest.
They’re addressing different layers of a workflow that most announcements don’t bother to decompose.
And if you’re an end user — a contracts professional, in-house counsel, legal ops leader, or someone running a deal process — the useful question is not simply “which one is best?”
It is:
Which layer of my actual problem does each one touch, what’s still missing, and what trade-offs come with choosing one approach over another?
The part the announcements skip
That question is harder to answer than it sounds because the announcements are built as much to impress as to inform.
They rarely locate themselves precisely in the workflow. They rarely explain what must already be true for the product to work well. And they rarely dwell on the gap between a staged demo and a system that survives real documents, real users, and real organizational complexity.
Every one of these announcements assumes a set of prerequisites that practitioners know are the actual hard part:
That the contracts have been collected.
That you know which contracts are in scope.
That the entity names in your CRM match the party names in the agreements.
That users have access to the right documents.
That someone has decided what data points matter.
That the output can be reviewed, trusted and used in the company’s legal and business processes.
Very little of that is solved by the announcement itself.
All of it is where most of the time goes.
The decade I spent building in this space taught me one pattern above everything else: each wave of technology solves the layer everyone is staring at, and the value quietly relocates to the layer nobody was watching.
Services gave way to software. Traditional machine learning improved extraction. Generative models changed what could be attempted. Each time, the extraction layer got better — genuinely, materially better.
And each time, the constraint moved somewhere upstream or downstream:
Which contracts matter?
Where are they?
Who has access?
What should we extract?
How do we evaluate quality?
How do we surface the answer clearly?
How does the output change the way the business actually makes decisions?
That pattern is playing out again now, across every one of these announcements.
It is the thing most worth tracking.
Why I’m writing this
I come at this from a decade of working across ML and LLM product development, contract analysis, solution sales, and end-user research, with cross-functional visibility into how enterprise contracts work actually gets delivered.
I have no vendor to sell and no employer to promote.
The goal of The Contract Signal is to give end users — the people who actually work with contracts — an independent, practitioner-level read on what the capabilities are, what they are not, what is coming, and how to use them to solve real problems.
The next pieces will get hands-on: testing tools against real contract workflows, looking at what they can actually do, where they break, and what they quietly assume you have already solved.
If you work with contracts, buy or evaluate AI tools for legal workflows, or build in this space and see something I’m missing, I’d like to hear from you.
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