The Case for Distributed A.I. Governance in an Era of Enterprise A.I.

It’s no longer news that  A.I. is everywhere. Yet  while nearly all companies have adopted some form of  A.I., few have been able to translate that adoption into meaningful business value. The successful few have bridged the gap through distributed  A.I. governance, an approach that ensures that A.I. is integrated safely,  ethically and responsibly. Until companies strike the right balance between innovation and control, they will be stuck in a “no man’s land” between adoption and value, where implementers and users alike are unsure how to proceed.   

What has changed, and changed quickly, is the external environment in which A.I. is being deployed. In the past year alone, companies have faced a surge of regulatory scrutiny, shareholder questions and customer expectations around how A.I. systems are governed. The E.U.’s A.I. Act has moved from theory to enforcement roadmap, U.S. regulators have begun signaling that “algorithmic accountability” will be treated as a compliance issue rather than a best practice and enterprise buyers are increasingly asking vendors to explain how their models are monitored, audited and controlled.

In this environment, governance has become a gating factor for scaling A.I. at all. Companies that cannot demonstrate clear ownership, escalation paths and guardrails are finding that pilots stall, procurement cycles drag and promising initiatives quietly die on the vine.

The state of play: two common approaches to applying A.I. at scale  

While I’m currently a professor and the associate director of the Institute for Applied Artificial Intelligence (IAAI) at the Kogod School of Business, my “prior life” was in building pre-IPO SaaS companies, and I remain deeply embedded in that ecosystem. As a result, I’ve seen firsthand how companies attempt this balancing act and fall short. The most common pitfalls involve optimizing for one extreme: either A.I. innovation at all costs, or total, centralized control. Although both approaches are typically well-intentioned, neither achieves a sustainable equilibrium.   

Companies that prioritize A.I. innovation tend to foster a culture of rapid experimentation. Without adequate governance, however, these efforts often become fragmented and risky. The absence of clear checks and balances can lead to data leaks, model drift—where models become less accurate as new patterns emerge—and ethical blind spots that expose organizations to litigation while eroding brand trust.  Take, for example,  Air Canada’s decision to launch an A.I. chatbot on its website to answer customer questions. While the idea itself was forward-thinking, the lack of appropriate oversight and strategic guardrails ultimately made the initiative far more costly than anticipated. What might have been a contained operational error instead became a governance failure that highlighted how even narrow A.I. deployments can have outsized downstream consequences when ownership and accountability are unclear.

On the other end of the spectrum are companies that  prioritize centralized control over innovation in an effort to minimize or eliminate A.I.-related risk. To do so, they often create a singular A.I.-focused team or department through which all A.I. initiatives are routed.  Not only does this centralized approach concentrate governance responsibility among a select few—leaving the broader organization disengaged at best, or  wholly unaware at worst—but also  creates bottlenecks, slows approvals and stifles innovation. Entrepreneurial teams frustrated by bureaucratic red tape will seek alternatives, giving rise to shadow A.I.: employees bringing their own A.I. tools to the workplace without oversight. This is just one byproduct that  ironically introduces more risk. 

A high-profile example occurred at Samsung in 2023, when multiple employees in the semiconductor division unintentionally leaked sensitive information while using ChatGPT to troubleshoot source code. What makes shadow A.I. particularly difficult to manage today is the speed at which these tools evolve. Employees are no longer just pasting text or code into chatbots. They are now building automations, connecting A.I. agents to internal data sources and sharing prompts across teams. Without distributed governance, these informal systems can become deeply embedded in work before leadership even knows they exist. The main takeaway: when companies pursue total control over tech-enabled functions, they run the risk of  causing the very security risks their approach is designed to avoid.  

Moving from A.I. adoption to A.I. value  

Too often, governance is treated as an organizational chart problem. But A.I. systems behave differently from traditional enterprise software. They evolve over time, interact unpredictably with new data and are shaped as much by human use as technical design. Because neither extreme—unchecked innovation nor rigid control—works, companies have to reconsider A.I. governance as a cultural challenge, not just a technical one. The solution lies in building a distributed A.I. governance system grounded in three essentials: culture, process and data. Together, these pillars enable both shared responsibility and support systems for change, bridging the gap between using A.I. for its own sake and generating real return on investment by applying A.I. to novel problems.   

Culture and wayfinding: crafting an A.I. charter  

A successful distributed A.I. governance system depends on cultivating a strong organizational culture around A.I. One relevant example can be found in  Spotify’s model of decentralized autonomy. While this approach may not translate directly to every organization, the larger lesson is universal: companies need to build a culture of expectations around A.I. that is authentic to their teams and aligned with their strategic objectives.  

An effective way to establish this culture is through a clearly defined and operationalized A.I. Charter: a living document that evolves alongside an organization’s  A.I. advancements and strategic vision. The Charter serves as both a North Star and a set of cultural boundaries, articulating the organization’s goals for A.I. while specifying how  A.I. will, and will not, be used.  

Importantly, the Charter should not live on an internal wiki, disconnected from day-to-day work. Leading organizations treat it as input to product reviews, vendor selection and even performance dialogue. When teams can point to the Charter to justify not pursuing a use case, or to escalate concerns early, it becomes a tool for speed, not friction. 

A well-designed A.I. Charter will address two core elements: the company’s objectives for adopting A.I. and its non-negotiable values for ethical and responsible use. Clearly outlining the purpose of A.I. initiatives and the limits of acceptable practices creates alignment across the workforce and sets expectations for behavior. Embedding the A.I. Charter into key objectives and other goal-oriented measures allows employees to translate A.I. theory  into  everyday practice—fostering shared ownership of governance norms and building resilience as the A.I. landscape evolves.    

Business process analysis to mark and measure  

Distributed  A.I.  governance system must also be anchored in rigorous business process analysis. Every A.I. initiative, whether enhancing an existing workflow or creating an entirely new one, should begin by mapping the current process. This foundational step makes risks visible, uncovers upstream and downstream dependencies that may amplify those risks, and builds a shared understanding of how  A.I. interventions cascade across the organization.   

By visualizing these interdependencies, teams gain both clarity and accountability. When employees understand the full impact chain and existing risk profile, they are better equipped to make informed decisions about where A.I. should or should not be deployed. This approach also enables teams define the value proposition of their A.I. initiatives, ensuring that benefits meaningfully outweigh potential risks.   

Embedding these governance protocols directly into process design, rather than layering them on retroactively, allows teams to innovate responsibly without creating bottlenecks. In this way, business process analysis transforms governance from an external constraint into an integrated, scalable decision-making framework that drives both control and creativity.   

Strong data governance equals effective A.I. governance  

Effective A.I. governance ultimately depends on strong data governance. The familiar adage ”garbage in, garbage out” is only amplified with A.I.  systems, where low-quality or biased data can amplify risks and undermine business value at scale. While centralized data teams may manage the  technical infrastructure, every function that touches A.I. must be accountable for ensuring data quality, validating model outputs and regularly auditing drift or bias in their A.I. solutions.   

This distributed approach is also what positions companies to respond to regulatory inquiries and audits with confidence. When data lineage, model assumptions and validation practices are documented at the point of use, organizations can demonstrate responsible stewardship without scrambling to retrofit controls. When data governance is embedded throughout the company, A.I. delivers consistent, explainable value rather than exposing and magnifying hidden weaknesses.   

Why the effort is worth it   

Distributed A.I.  governance  represents the sweet spot for scaling and sustaining  A.I.-driven value. As A.I. continues to be embedded in core business functions, the question evolves from whether companies will use A.I. to whether they can govern it at the pace their strategies demand. In this way, distributed A.I. governance becomes an operating model designed for systems that learn, adapt and scale. These systems help yield the benefits of speed—traditionally seen in innovation-first institutions—while maintaining the integrity and risk management of centralized control oversight. And while building a workable system might seem daunting, it is ultimately the most effective way to achieve value at scale in a business environment that will only grow more deeply integrated with A.I. Organizations that embrace it will move faster precisely because they are in control, not in spite of it.