Why Legal’s Cross-functional Role Is the Key to Scalable Enterprise AI

Banner artwork by Fit Ztudio / AI

Note: This article is Part 2 in a two-part series on why Legal sits at the center of successful enterprise AI adoption. Read Part 1 here.  

AI doesn’t live inside a single department. It cuts horizontally across workflows, systems, obligations, and data flows. That means the function best positioned to lead AI strategy is one that already works horizontally. That function is often Legal, along with partners in Finance (to see what tools are being purchased) and IT (to see what SaaS platforms are being accessed from company laptops and systems). Legal tends to have a “bird’s eye view” across most — if not all — departments, and routinely observes hotspots when legal review is requested for a situation or potential dispute. 

Most teams collaborate episodically — Sales loops in Security when needed for a deal, Product checks with Compliance before a release, HR consults Finance on a new expense policy. Legal, by contrast, collaborates continuously, often daily, with all of these departments. For example, in the experience of the authors, the following functional groupings are common: 

In addition to the practice specific relationships outlined above, Legal routinely quarterbacks cross-functional tasks such as RFPs, due-diligence reviews, privacy assessments, and security questionnaires. These processes require tightly coordinated input from Engineering, Security, Product, IT, HR, Finance, and Procurement. These activities expose Legal to the operational realities of the business: where information resides, which tools are used by each team, where workflows slow, and where obligations intersect. This visibility provides Legal with a view on the categories of data each team holds and uses: trade secrets, confidential information shared under NDA, code and product technical data repositories, personally identifiable information (PII), and special categories of data (health, financial, and sensitive personal information). 

Through this constant engagement, Legal develops a uniquely comprehensive view of the business: a deep understanding of how work moves through the enterprise, and where data flows across departments and tool stacks. It sees where bottlenecks form, where obligations conflict, where data becomes trapped in silos, and where privacy and security exposures emerge. It also sees where AI can create leverage without introducing new forms of risk.  

This is where strongly permissioned AI systems become essential. AI is only safe when it operates on top of the same permissions, entitlements, and access controls that govern the rest of the enterprise. When AI inherits those controls, thereby surfacing only the data an employee already has the right to see, then Legal can enable AI deployment with confidence, knowing it won’t expand access, circumvent restrictions, or inadvertently expose sensitive information. Permissioned AI turns Legal’s deep cross-functional insight into actionable guardrails: enabling innovation while ensuring every model, query, and workflow stays inside the organization’s data and contractual boundaries. 

Legal’s enterprise-wide vantage point becomes especially valuable when understanding and determining how AI is entering and spreading across the organization. Because Legal reviews inbound AI vendor contracts, it often becomes the primary function with a comprehensive line of sight into the organization’s full AI footprint. There is an opportunity to partner with Finance teams to identify any shadow-AI spend occurring on company credit cards, and ensure appropriate contractual protection and governance are in place. There is also an opportunity to partner with IT to identify which SaaS tools data is flowing to or from, regardless of whether those tools are paid (which Finance/Legal should see) or free (which often remain undetected by Finance/Legal). With this partnership and input, Legal can identify issues that often remain invisible to individual business units: 

Many AI initiatives fail not because the underlying models are insufficient, but because organizations unintentionally create parallel, incompatible versions of the same capability, each governed by different contracts, data flows, and assumptions about risk. Left unchecked, this leads to tool sprawl, fragmented governance, and architectural inconsistency that make scaled AI deployment challenging or impractical. It is also important for Legal to educate new employees and train or refresh existing employees, providing a baseline knowledge on privacy, confidentiality, AI governance, and employee policies around use of AI tools both within and outside of company systems. 

Legal is one of the few functions with both the oversight and the authority to recognize these patterns early and steer the organization toward consolidation and coherence before the costs and risks of unmanaged AI sprawl become irreversible. 

In an environment where AI reshapes workflows across the entire enterprise, success depends on a function capable of coordinating horizontally, interpreting conflicting obligations, and ensuring systems operate under a unified framework. Legal is uniquely equipped to play that role, and AI strategy only succeeds when someone does. It is essential to build governance, trust, accountability, and explainability into any workplace AI transformation effort, both for compliance with laws (EU GDPR, EU AI Act, US State laws, and more) and to maintain customer trust regarding how their data may be processed. 

Legal knows how to balance risk and innovation — across the entire enterprise and innovation 

Every organization faces the same tension with AI: move fast enough to capture opportunity, but not so fast that you create unacceptable risk. Most teams experience that tension only within their own lane — Product sees product risk, Security sees security risk, HR sees people risk. Legal is one of the few functions that routinely sees the intersections. 

Lawyers make judgment calls every day that depend on understanding multiple functions at once: how a product feature interacts with regulations, how contract terms shape Sales strategy, how a data workflow touches Security, or how an automated decision impacts HR and Compliance. Legal’s work is inherently cross-functional, so its risk calculations tend to reflect that broader view. 

The speed at which AI coding assistants enable developers to build software quickly, marketers to generate media content in minutes rather than days, and product teams to launch new features in days instead of weeks or months is a double-edged sword. AI enables all these users to arrive at solutions faster, but often doing so bypasses traditional checkpoints such as engineering design reviews, product feature gate reviews, marketing content verification, and more. This can lead to something shipping quickly without compliance checks, validation of claims, or pausing to question whether a solution is the right or most appropriate one for the market, jurisdiction, or audience.  

The complexity of AI amplifies the need for an integrated perspective and global governance across the company. New exposures rarely sit neatly within one department: 

These are not theoretical issues. They are happening today: chatbots offering refunds that violate the master agreement; unapproved AI note-takers capturing privileged or confidential information; AI-generated logs complicating incident response because no one can fully explain how the output was produced, or even hallucinations present in AI-generated logs or summaries. 

Legal is often the first function to see how these issues collide across teams, not just within them. When Legal is involved early, it can translate fragmented concerns into a cohesive strategy. How do we build this feature so it aligns with our contractual obligations and global privacy regulations? How do we automate this step while preserving required review, particularly with a human in the loop? What evidence will we need to have ready to present to regulators or customers if this output is ever challenged? Early Legal involvement turns these questions into a holistic strategy while there is time to plan a well-reasoned response and process, not an emergency or patchwork fix cobbled together under regulatory scrutiny. 

The organizations that will move fastest and most safely in the AI era are the ones that treat Legal not as a brake but as the function responsible for keeping both risk and progress in balance, while moving at AI-speed. In a world where AI reshapes decisions, speed, and accountability all at once, that balancing role becomes foundational to scaling AI responsibly. Just as a champion race-car driver employs the right balance of speed and control to stay ahead of the pack, companies embracing AI must blend speed, oversight, and user empowerment to excel in their fields. 

Legal understands the enterprise obligation graph — and AI can’t scale without it 

Every enterprise runs on an invisible system that determines what it can and cannot do: its obligation graph. This graph is the network of contractual promises, regulatory requirements, data-use restrictions, privacy rules, retention mandates, audit obligations, and operational commitments the company has accumulated over time. Sometimes it also includes tribal knowledge within the business that may not be in any formal policies or procedure documents, but is simply “the way it’s done” at a given company.  

Most functions only see their slice of that graph. Legal typically sees most (and, in some cases, all) of it — and interprets how each obligation affects every other. 

This matters because AI doesn’t run inside functional boundaries such as departmental organizational charts. It runs inside the obligation graph. 

Legal, often in partnership with its IT and business system owner counterparts, is well suited for translating the obligation graph into implications for AI:  

When these obligations are invisible, AI initiatives drift into misalignment, stall, or get shut down. When Legal, in partnership with IT and business counterparts, maps the obligation graph and embeds it directly into workflows, AI becomes scalable, defensible, and aligned with the company’s commitments. 

This is the structural reason Legal is indispensable to AI strategy: it is often the primary function that knows how the enterprise’s obligations interact, and how to keep AI inside the boundaries of what the organization is allowed to build. 

Legal’s emerging leadership mandate in the AI era 

AI is forcing organizations to confront a simple truth: no single function can scale AI alone. Every meaningful AI use case crosses boundaries — between Product and Security, HR and Compliance, Engineering and Finance, IT and Operations. The work is cross-functional by design, and Legal already lives at those intersections. 

Legal’s value in this context isn’t about claiming ownership of AI strategy. It comes from the day-to-day work of helping teams interpret obligations, understand constraints, and navigate situations where requirements overlap or pull in different directions. That perspective can make it easier for cross-functional groups to stay smoothly aligned as they explore new tools and approaches. And as AI reshapes more decisions and workflows, that steadiness becomes one of the most reliable ways to keep innovation and governance connected. Trust, explainability, governance, and oversight are all skills Legal is adept at, and in the new age of AI, they’re what unlock safe, productive, and efficient enterprise AI adoption at scale. When those foundations are in place, employees trust and rely on the tools, customers see consistency, rather than surprises, and the CFO sees investment rather than exposure. Happy users, happy customers, and happy CFO. It’s difficult to argue with that level of alignment.