Our Take
The legal tech stack exists; the problem is that most in-house teams are still operating like it's 1995, which means no amount of AI will help until they fix their data foundation.
Why it matters
Legal teams are sitting on the foundational work required to actually benefit from AI. Until contract repositories, playbooks, and systems talk to each other, AI agents and automation tools remain theoretical.
Do this week
General Counsel: audit your contract storage and playbook digitization within 30 days so you can identify whether you're in the 67% with a repository or the 33% still managing disconnected documents.
The baseline adoption numbers are worse than expected
A survey by World CC and Sirion of in-house legal teams found significant gaps in foundational contract management infrastructure (company-reported). Only 67% of respondents have tools that create a repository of signed contracts, meaning one-third operate without even that basic capability. Just 16% reported using AI or machine learning tools at all. Only 13% have digitized contract playbooks or tools to support them. Template assembly, which should be one of the most elementary efficiency tasks in legal operations, reached only 34% adoption.
The report frames these gaps as a data quality problem: most organizations still manage contracts as disconnected documents spread across repositories, shared drives, and siloed systems. Without a trusted system of record, structured data, and clear ownership, AI cannot reliably operate on contract data at scale.
AI reliability depends on data discipline you probably don't have yet
The survey exposes a hard constraint that many legal tech vendors won't say out loud: AI tools are only as reliable as the underlying data foundation. Conversations about agents, automation, and AI-driven contract intelligence assume clean, connected, structured data. Most in-house teams don't have that.
This creates a two-tier market. A small pack of innovative general counsels are moving forward; the majority are still addressing foundational tasks that should have been solved years ago. For legal tech companies, this is simultaneously an opportunity and a sales problem. The products exist. Deployment requires not just software but sustained engagement, data cleanup, and often new workflows that bump against existing organizational inertia.
Close the foundational gaps before betting on AI
If you cannot answer with confidence where your signed contracts live, who owns them, or what version is in force, no AI product will fix that for you. Start by auditing your current state: Do you have a single repository? Are your playbooks digitized or still embedded in email and shared drives? Can your systems talk to each other?
The survey suggests that forward-deployed engineers or consultants embedded with your chosen vendor may be necessary to make progress. That is an added cost, but it signals that the work is execution-heavy, not just a software install. If your tech partner is not willing to invest in that kind of support, you are not ready for an AI product yet.