What Real AI Adoption in Legal Teams Actually Looks Like in 2025

The Gap Between AI Hype and Legal AI Reality

Ask most legal technology vendors what AI adoption looks like and you will get a slide deck full of efficiency percentages and time-saved metrics. Ask the lawyers doing the actual work and you get a more complicated picture. Real AI adoption in legal teams, whether in private practice or in-house, tends to unfold gradually, unevenly, and with considerably more human judgment involved than the marketing suggests.

Brian Kennedy, Director of Innovation for Real Estate at Bryan Cave Leighton Paisner, is one of a growing number of practitioners speaking openly about what this transition genuinely involves. His perspective, and those of peers across the industry, points to a clear pattern: the firms and departments making the most meaningful progress are not the ones that deployed AI everywhere at once. They are the ones that identified specific, high-volume, low-ambiguity tasks and replaced manual effort there first.

Where AI Fits Into the Contract Lifecycle

Contract lifecycle management (CLM) is one of the clearest use cases for legal AI, precisely because contracts are structured, repetitive, and voluminous. The lifecycle, from initial drafting through negotiation, signature, obligation tracking, and renewal, contains dozens of sub-tasks that vary in complexity. AI tools are not equally useful across all of them.

For legal teams evaluating AI contract review or AI-assisted drafting, the most defensible starting point is extraction and classification: pulling defined terms, payment obligations, notice periods, and termination rights from executed agreements. This is where AI adds speed without adding risk. Drafting assistance follows, particularly for standard positions and playbook clauses. Negotiation support, where an AI reads a counterparty's redline and flags deviations from preferred positions, is now a mature capability in platforms like Adira that are trained to read contracts from your side of the table.

What remains genuinely human, and should stay that way for now, is commercial judgment: deciding when to accept a non-standard clause, when to escalate, and when a deal structure is simply wrong regardless of what the contract says.

The Adoption Pattern That Actually Works

The legal teams seeing real returns from AI tools tend to follow a recognisable path. They begin with a use-case audit, cataloguing the tasks that consume the most time and carry the least analytical complexity. They then pilot AI on a narrow slice of that work, measure output quality rigorously, and build internal confidence before expanding scope.

Critically, they invest in change management alongside technology. Resistance to AI in legal departments is rarely irrational. It reflects legitimate concerns about accuracy, professional responsibility, and the nature of legal expertise itself. Firms that dismiss this resistance tend to see low adoption rates regardless of how capable their chosen tool is. Firms that address it directly, by involving lawyers in tool selection, being transparent about limitations, and celebrating early wins, build the internal trust that makes adoption stick.

The lesson for legal technology procurement is direct: the platform matters less than the process around it.

Why Legal AI Adoption Is Slower Than Other Industries

Legal is not slow to adopt AI because lawyers are technophobic. It is slow because the professional and reputational consequences of errors are asymmetric. A wrong answer in a contract review is not a minor inconvenience; it can result in missed obligations, unenforceable clauses, or regulatory exposure. This reality shapes how legal teams must approach AI tools, with validation workflows, human review layers, and clear escalation paths that other industries simply do not need.

There is also a jurisdictional complexity that distinguishes legal AI from, say, AI in financial services or healthcare operations. A contract drafted under English law carries different interpretive rules than one governed by New York law or Singapore law. AI platforms that treat all contracts as jurisdictionally interchangeable are a liability. The tools gaining genuine traction in 2025 are those that understand the law of the jurisdiction they are working in, and can surface jurisdiction-specific risk without the lawyer needing to prompt for it.

What Good Looks Like for In-House Legal Teams

For in-house counsel, the most meaningful benchmark for AI adoption is not speed. It is coverage. Legal teams are chronically under-resourced relative to the volume of commercial activity they are expected to support. AI that enables a four-person legal team to review and negotiate contracts at the pace previously requiring eight people is transformative, not because it cuts headcount, but because it allows the existing team to focus on higher-value work.

The in-house departments seeing the strongest results are those using AI tools integrated into their CLM platform rather than bolted on as separate point solutions. When contract drafting, review, obligation tracking, and renewal alerts all operate within a single system that learns from a company's own precedents and preferences, the accumulated value compounds over time. The AI gets better at writing in the company's voice, flagging the risks that matter to that business, and surfacing the clause positions that its lawyers consistently adopt.

What Legal Teams Should Do Next

For legal teams not yet past the pilot stage, the priority is focus. Identify one workflow, preferably high-volume and well-defined, and implement AI there with proper measurement in place. For teams already using AI in isolated tasks, the next step is integration: connecting those capabilities into a coherent CLM workflow so that insight from one stage of the contract lifecycle informs the next.

For both groups, the honest question is not whether AI is ready for legal work. The more useful question is whether your team's processes, data hygiene, and governance frameworks are ready to get the most from it. The technology, in most cases, is ahead of the organisational readiness. Closing that gap is where the real work of legal AI adoption takes place.