Legal AI has largely been sold on productivity: faster research, drafting, and review. But as legal departments move from experimentation to deployment, speed is no longer the only consideration.
The next question is whether AI can take action within legal workflows while preserving the approvals, controls, and records the department needs.
Recent industry research backs this up. Deloitte’s 2026 report, The AI Imperative: Reshaping of the Legal Industry, found that 71% of surveyed legal departments had moved beyond experimentation into initial, scaling, or fully embedded AI deployment. That is a sharp shift up from a landscape where 76% reported no adoption just two years earlier. Among the 121 senior legal leaders surveyed, 79% said their legal department’s AI investment had increased year over year. For departments increasing investment, budgets rose by an average of 67%.
But the same research points to a gap. Departments are buying AI tools faster than they are investing in the training, process redesign, and data foundations needed to use those tools well. Most of the money is going to technology, not to the people and systems around it.
That gap defines the next stage of legal AI. Generating an answer is one thing. Taking action with the right permissions, approvals, escalation paths, and audit history is another.
When AI Takes Action, Accountability Matters
Agentic AI, meaning AI that does not just draft but takes multi-step action, is quickly becoming a live buyer conversation. Deloitte found that 61% of surveyed legal departments are already experimenting with or piloting agentic AI. For legal teams, potential applications include intake triage, contract routing, and other repeatable workflows with defined decision points. (For a plain-English primer on what agentic AI actually is, see Agentic AI in Legal Operations: What It Is and Why It Matters.)
But autonomy without accountability is a liability, not a feature.
Every legal team evaluating agentic AI should be asking the same handful of questions. Who approved this action? What happens when the AI is uncertain? Is there a human in the loop before something is finalized? Is there a record afterward that would hold up under audit?
These are not hypothetical concerns. They are the actual criteria shaping how legal departments evaluate AI vendors right now.
Where work happens matters, but it is not the whole story
Legal AI vendors have converged on making their tools available inside familiar surfaces, Microsoft Word chief among them. That is a legitimate and overdue improvement. Lawyers should not have to leave the tools they already use to get AI assistance.
But where AI drafts a clause is a much smaller question than what happens to that clause afterward.
- Does it connect to a matter?
- Does it trigger a review workflow?
- Does it roll into spend, vendor, or obligation tracking anywhere else in the organization?
A clause that lives only inside a word processor, however capable the assistant that wrote it, still leaves the rest of the legal operating model disconnected.
The governed execution layer
This is the frame legal teams should use to evaluate AI: not only which assistant is smartest, but which platform can be trusted to execute, govern, and record legal work end to end. In practice, that means:
- Permissions and approval gates before an action is finalized
- A durable, auditable record of what the AI did and why
- Connection across the legal function — matters, contracts, spend, vendors, and outside counsel — rather than an isolated point tool
- Measurable outcomes tied to the work itself, not just usage metrics
None of this lives in the interface. It lives in the system of record underneath the work, the operational backbone a legal department already runs on. That is the layer where AI becomes durable rather than disposable, and it is the layer Onit has spent over a decade building.
Legal departments do not need one more assistant. They need an execution layer they can govern.
Preparing for Agentic AI in Legal Operations
If your team is moving from experimenting with AI to trusting it with real work, the next question is what “agentic” actually means in a legal context, and where autonomy makes sense versus where a human still belongs in the loop.
Start here: Agentic AI in Legal Operations: What It Is and Why It Matters breaks down how agentic AI works, where it delivers, and the tradeoffs worth weighing before you deploy it.
