Enterprise AI governance works best when IT and legal make decisions together, on a regular schedule—not only after a tool has been adopted or a problem has surfaced. CIOs can make systems, vendors, data flows, access controls, and deployment practices visible; legal leaders can assess applicable law, privacy, intellectual property, contracts, and litigation exposure. Neither function has enough information to govern the whole picture alone.
Why the CIO–legal partnership matters now
AI can enter a company through a centrally approved platform, a feature embedded in existing software, or a tool adopted by a business team. That makes the governance challenge broader than choosing an AI model: organizations need to know what is being used, what information it touches, and how its output affects work and decisions.
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IBM’s 2026 survey found that 77% of surveyed organizations said AI adoption was outpacing governance capabilities, while 70% of surveyed technology executives said business teams were deploying technology faster than IT could track. Only 11% of respondents believed their organizations were fully ready for the scale of AI-agent deployment expected in the next year. These are survey results, not a census of all organizations: IBM’s Institute for Business Value surveyed 2,000 senior technology executives across 33 geographies and 19 industries from January through April 2026. IBM’s survey findings and methodology
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Legal teams are also increasingly involved in work beyond conventional legal advice. KPMG’s 2026 Global General Counsel Outlook, based on 468 senior legal leaders across 28 jurisdictions, reports that 75% were regularly or constantly asked to weigh in on nonlegal issues, and 70% said their organizations had implemented AI in legal research and analysis. The figures describe the surveyed leaders and organizations; they do not establish what every legal department does. KPMG’s 2026 Global General Counsel Outlook
The practical implication is not that legal should approve every technical choice or that IT should make legal judgments. It is that the two functions need a shared operating process for surfacing AI use, assessing risk, assigning decisions, and updating controls as systems and workflows change.
What each function contributes
CIO and IT: make the technology and its use visible
IT can identify approved and embedded AI capabilities, vendors, infrastructure, users, data sources, and workflows. It can explain how a system is deployed, who can access it, what security controls apply, and where technical constraints affect proposed safeguards. This visibility helps the organization discover not only formal deployments but also AI features that may arrive through software it already uses.
Legal: assess obligations and exposure
Legal can evaluate which requirements apply to a use case and what risks arise under existing law, contracts, privacy commitments, intellectual-property rights, consent obligations, or potential litigation. AI use does not by itself remove obligations that already apply to an organization. The relevant duties depend on the jurisdiction, the data, the system, and the way it is used; a general governance model cannot determine the answer for every deployment.
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Business owners and other control functions: connect policy to practice
The business team knows the purpose of a use case and how employees or customers will rely on its output. Security, privacy, compliance, and data specialists can contribute expertise within their remits. Their involvement should be built into the review process rather than treated as a substitute for CIO–legal coordination.
Gartner research director Stuart Strome describes general counsel as ultimately accountable for legal risk events related to AI, while noting that AI governance responsibilities are generally shared across functions. That is Gartner’s expert view, not a jurisdiction-neutral statement of legal liability. Gartner’s discussion of legal leaders and AI governance
Build a practical shared governance process
1. Maintain one working inventory
Keep a common record of AI systems and relevant vendor features, including their business owner, purpose, users, data sources, workflow, and the decisions or outputs they support. IT can help discover and describe the systems; business owners can explain how they are used; legal and other control functions can identify which uses need closer assessment. Treat the inventory as something to update when a tool, feature, data source, or workflow changes.
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2. Triage use cases by their actual use and exposure
For each entry, establish what the system does, whose information it processes, who may rely on its output, and whether it influences a consequential decision. Legal, IT, security, privacy, and the relevant business owner can then determine the appropriate level of review. A single approval rule for every use case may miss meaningful differences in data, impact, and deployment context.
3. Assign decision rights and escalation routes
Document who may approve a use case, who must be consulted, who owns implementation safeguards, and who can pause or escalate a deployment if facts change. A cross-functional committee can provide a forum for these decisions, but it is one possible operating model—not a universal legal requirement. Organizations can use existing governance forums if they provide clear ownership and timely escalation.
4. Record safeguards and required human oversight
Capture the controls selected for a use case, the person or team responsible for them, and any human review required by policy or the organization’s risk decision. Specify how people should handle uncertain, incomplete, or unsuitable output and where a decision must be referred to a qualified employee. A record of the rationale and approvals makes it easier to understand what was authorized and what must be revisited.
5. Revisit decisions when the system or context changes
Review the governance record when a vendor adds an AI feature, a system gains new users, the data changes, the workflow expands, or an agent begins taking actions rather than only generating suggestions. IBM CIO Matt Lyteson describes the challenge as scaling systems that operate “continuously and autonomously” within governance models designed for a slower, more predictable environment. The operating process therefore needs a route to detect changes and refresh reviews, not just a one-time sign-off. IBM’s findings on AI control and visibility
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose an operating model that fits the organization
There is no evidence here for one reporting line or committee structure that suits every company. The useful test is whether the chosen model makes AI use visible and decisions actionable without adding a process so cumbersome that teams bypass it.
- Visibility: Can the organization identify AI tools, vendor features, users, data, and workflows?
- Ownership: Are cross-functional responsibilities, approval authority, and escalation paths named?
- Review quality: Can legal, privacy, security, and data expertise reach the use cases that need it?
- Controls: Are risk-based safeguards and policy-required human oversight documented and assigned?
- Currency: Can the inventory and decisions be updated as agents, features, and use cases change?
- Operational fit: Does the process work with existing decision forums and the organization’s capacity?
Use these criteria to assess a central committee, a distributed review process, or a hybrid. The structure matters less than whether it gives CIO, legal, and business leaders a reliable way to see emerging use, make decisions, and keep them current.
Keep the evidence and the legal conclusions in proportion
Survey findings are useful signals, not predictions about a particular company’s risk. A September 2026 CIO opinion article attributes an 88% regular-use figure to McKinsey’s 2025 State of AI survey and a 56% legal-risk challenge figure to Norton Rose Fulbright’s 2025 litigation survey. Those are secondary attributions in that article, so they should not be treated as independently verified here. More importantly, a rising use rate or reported challenge does not establish that a particular deployment will cause a lawsuit.
Legal requirements vary across jurisdictions and use cases. A cross-functional process can help an organization identify questions and route them to the right people, but it is not a substitute for legal analysis of a specific deployment. The governance structure should support informed decisions rather than imply that one function, committee, or checklist can eliminate risk.
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