AI governance gaps open when adoption outruns oversight. Recent research found that 94% of legal professionals now use AI, yet only 29% say it is embedded in their organization’s strategy and operations. The largest group, 43%, describe their firms as still experimenting and making slow progress. That gap between use and governance is not theoretical. It lives in the space between the tool a lawyer chooses and the advice a client receives.
Why do AI governance gaps matter to professional services firms?
Two of your best associates could review the same client document and reach slightly different recommendations, each reflecting their experience and judgment. That has always been true. AI introduces another source of variation: the tools people choose to use.
A research tool can influence which authorities appear first. A drafting tool can shape the initial version of a client clause. A summarization tool can affect which facts, risks, or obligations come to light. Lawyers remain responsible for their final advice, even as AI increasingly influences the route they take to reach it.
The most common AI use cases are no longer limited to reviewing meeting notes or polishing client emails. Research is the most common use, at 69%, followed by summarization at 62%. More than half use AI for drafting and document review. AI is already part of substantive work, even where firms have yet to agree what effective governance should look like.
Who owns AI governance when every department has a stake?
AI governance fails when everyone agrees it matters but nobody owns the decision rights to make it effective. AI affects several teams at once. IT assesses the technology. Risk and compliance consider acceptable use. Information security focuses on data. Procurement and knowledge teams work with suppliers. Practice leaders identify where the tools can help.
When responsibility is spread across teams without a clear owner, it becomes difficult to see who is accountable for the whole. Picture a firm where the IT director approves a tool for security, compliance drafts a policy nobody reads, and associates pick whichever AI assistant answers fastest. Each team did its job. The firm still has no way to know whether the advice leaving the building reflects its standards.
Research from Karen Waldron highlights this problem. In organizations where AI use is high but governance is low, 18% of respondents say there is no clear owner for AI decisions. The rest of that sentence in the original article is cut off, but the implication is clear: distributed responsibility without centralized accountability creates exposure.
What does it mean to close AI governance gaps?
Closing the gap starts with answering three questions. Who decides which tools are approved? Who monitors how they are used on client work? Who is accountable when something goes wrong?
An effective governance model assigns one owner with decision rights across the AI lifecycle. That owner coordinates input from IT, compliance, procurement, and practice groups, but holds final accountability. The owner also ensures that policy is not written once and ignored. It must be updated as use cases evolve and new tools enter the environment.
Governance also requires visibility. If you cannot see which tools are in use, where they touch client data, and how outputs are reviewed before they leave the firm, you cannot manage the risk. Inventory the tools. Map the workflows. Document the review steps. Then assign someone to keep that picture current.
How do you build AI governance that scales with adoption?
Governance that scales starts with clear ownership, but it also requires practical rules that people can follow. A policy that says “use AI responsibly” will be interpreted differently by every person who reads it. A policy that says “all AI-generated research must be cited and verified against primary sources before it is included in client advice” is actionable.
Training is part of the answer, but it is not the whole answer. Training builds critical thinking skills. Governance defines the guardrails. Both are necessary. Without training, people will not recognize when an AI tool has made a mistake. Without governance, there is no standard for what constitutes a mistake in the first place.
For small and mid-sized professional services firms, the governance model does not need to be complex. It needs to be clear. Designate an owner. Write down which tools are approved and for which tasks. Require that AI outputs be reviewed by someone with the expertise to catch errors. Document who made the final call on advice that goes to a client. That is enough to close the gap between adoption and accountability.
What happens when AI governance lags behind AI use?
The risk is not that AI will replace judgment. The risk is that AI will influence judgment in ways the firm cannot see or control. A lawyer who relies on an AI summary without reading the underlying document may miss a critical fact. A drafter who accepts an AI-generated clause without checking it against the client’s jurisdiction may introduce unenforceable terms. A researcher who stops at the first answer an AI provides may overlook a binding authority.
Each of those failures is a professional responsibility issue. Each is also a client service issue. Clients expect advice that reflects your firm’s standards, not the quirks of whichever assistant a particular lawyer happened to use that day. Governance ensures consistency.
The other risk is competitive. Firms that embed AI thoughtfully will deliver faster, more consistent work. Firms that experiment without governance will deliver uneven results and spend time fixing errors. The gap between those two groups will widen as AI capabilities improve.
How do you know if your AI governance is working?
Working governance produces three outcomes. First, everyone knows which tools are approved and which are not. Second, there is a process for vetting new tools before they touch client work. Third, there is a person or team accountable for updating policy as AI use evolves.
If you cannot answer those three questions, the governance is not working. If the answer to “who owns AI governance” is “IT and compliance and the managing partner,” the governance is not working. Shared responsibility without clear ownership is another way of saying no one is in charge.
Audit your current state. List every AI tool in use across the organization. Document which teams are using them and for what. Identify where those tools touch client data or influence client advice. Then assign one person to own the next decision: keep, replace, or ban each tool. That person becomes the governance owner.
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Source: AI Governance Must Keep Pace with Legal Practice
Researched and drafted with AI, then reviewed and approved by Ron Trotto before publishing.