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 a new product called Contract Intelligence. Anthropic launched Claude for Legal with twelve practice-area plugins. DocuSign unveiled agentic contract workflows.
These were preceded by even more announcements last year — 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 on this exact problem. I started working on it 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 every tool currently claiming it.
So when I see announcement after announcement related to this, I’m curious. Because the concept is old but the technology is genuinely new and continues to evolve, and the gap between announcement and reality is exactly where the useful analysis lives.
Same space, different layers
Here’s what’s worth noticing: each of these announcements is solving for different things in different ways, but they’re all filed under similar labels.
Harvey’s Contract Intelligence is portfolio-level playbook management — surfacing negotiation patterns, fallback positions, and clause language from executed agreements to make the next review faster. Every signed contract 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, Cowork handling first-pass review and redlining, and MCP integrations that connect it to whatever else lives 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 — hundreds to thousands of customer contracts per month — with limited headcount.
DocuSign’s agents operate inside their Intelligent Agreement Management platform, checking agreements against company standards, flagging risks, tracking obligations, and — through Agent Studio — letting teams build custom agents for their specific deal and renewal workflows. 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 way a feature comparison would 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, an in-house counsel, someone running a deal team — the question you should be asking isn’t “which one is best.” It’s “which layer of my actual problem does each one touch, what’s still missing, and what are the trade-offs of choosing one over another?”
The part the announcements skip
That question — which layer — is harder to answer than it sounds, because the announcements are built as much to impress as they are to inform. They rarely locate themselves precisely in your workflow or get specific about their approach.
And as anyone who’s been in the space long enough can attest, there’s always a gap between a staged demo and a tool that survives your real use cases, documents, and 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 ones are in scope. That the entity names in your CRM match the party names in the agreements. That you have access to the documents at all. That someone has decided what data points matter.
Very little of that is solved by any of these launches. 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. Machine learning gave way to deep learning. Deep learning gave way to generative models. Each time, extraction got better — genuinely, materially better — and each time, the constraint turned out to be somewhere upstream or downstream: knowing which contracts matter, getting access to them, defining what to look for, applying the tools effectively, surfacing the insights in an easily digestible way, and integrating the output into how the business actually makes decisions.
That pattern is playing out again right now, in real time, across every one of these announcements. And it’s the thing I think is most worth tracking closely.
I come at this from a decade of working across ML/LLM product development, contract analysis, solution sales, and end-user research — with cross-functional visibility into how services delivery actually operates.
I have no vendor to sell and no employer to promote. The goal is to give end users — the people who actually do contract work — an independent, practitioner-level read on what the capabilities are, what’s coming, and how best to use these tools to solve real problems.
The next pieces will get hands-on with these tools against real contract workflows — what they can actually do, where they break, and what they're quietly assuming you've already solved.
If you work with contracts and want to follow along — or if you’re building in this space and see something I’m missing — I’d like to hear from you.