The knowledge gap problem with Legal AI
AI is dominating the conversation in in-house legal. Whether it is contract review, due diligence, or regulatory monitoring, the promise of AI-powered efficiency is hard to ignore. Yet for many in-house legal teams, meaningful adoption remains frustratingly out of reach, not because the technology is not there, but because the conversation around it is not.
The problem is not resistance to AI. In most teams, there is genuine curiosity and a real appetite to work smarter. The problem is that understanding AI deeply enough to use it well has become a specialism in its own right and that is a job no one has asked lawyers to do.
There is too much to know and not enough time to know it
In-house legal teams are already operating at capacity. The expectation that lawyers should also become fluent in AI: understanding its architecture, its limitations, its appropriate use cases, and its risks is, frankly, not realistic in the time lawyers have and to the quality they wish to reflect in the output.
AI is a fast-moving, technically complex space. New models, new tools, and new debates about what AI can and cannot reliably do emerge almost weekly. Keeping pace with that requires dedicated attention, something lawyers, whose primary job is providing sound legal advice, simply cannot afford and are not incentivized to do so at the levels required for true fluency.
This is not a criticism. It is a structural reality.
The real blocker is expectation misalignment
When AI tools underdeliver in legal settings, the instinct is often to blame the technology. But in many cases, the issue lies elsewhere: in the gap between what lawyers expect AI to do and what it can actually do.
Legal work relies heavily on context, nuance, precedent, relationships, risk appetite. Lawyers who have spent years developing the contextual expertise naturally expect the tools they use to share it. However, AI does not read between the lines the way an experienced lawyer does. It does not intuit priorities or remember the particular way a client prefers their contracts to be drafted. It needs to be directed, prompted, and guided, and that requires a level of understanding about how the tool actually works.
When that understanding is missing, AI tools get used poorly, or not at all. Attorneys become understandably frustrated. Outputs seem unreliable. Trust erodes. Adoption stalls.
Who owns this problem?
This is where Legal Ops professionals and AI Innovation leads become genuinely critical as enablers of change.
These are the people best positioned to bridge the gap. They sit at the intersection of legal practice and operational delivery. They understand what lawyers need and can develop a working knowledge of what AI tools can realistically provide. In short, they can act as translators, converting AI capability into practical, trustworthy workflow improvements. They can get lawyers up to speed quickly so lawyers can learn, do, and implement in their work even faster.
In larger in-house teams, dedicated AI Innovation Director roles are beginning to emerge to do exactly this. Whether it is one person or a broader Legal Ops function, the principle is the same: someone needs to own the space between the technology and the people using it.
What that enablement role actually looks like
Bridging the knowledge gap is not a one-off task. It requires sustained effort across a few key areas:
Setting realistic expectations. The most important work happens before a tool is even deployed. Helping lawyers understand what AI is good at, and where it needs human oversight, prevents the disillusionment that often kills adoption early. These expectations can be best set by understanding what they attorney or practice wants to achieve and how much mindshare or time they are willing to dedicate to achieve the desired results.
Running internal education. This does not need to be formal training. It can be short demos, worked examples, or even structured conversations about what the team has tried and learned. The goal is building familiarity, not technical expertise.
Translating capability into workflow. The most effective enablers do not just explain what AI can do in the abstract, they show how it fits into the specific workflows their team already uses. That contextualisation is what turns theoretical interest into practical adoption.
AI adoption in legal is an organisational challenge as much as a technological one
The technology is not the hard part. The hard part is ensuring the right people have the right understanding to use it well, and then building the internal structures that make that possible.
For in-house legal teams, that means investing not just in tools, but in the people and processes that make tools useful. Legal Ops and AI Innovation leads are increasingly the ones making that happen.
