To move beyond pilot projects and shadow A.I., organizations must rethink governance as a cultural challenge. Unsplash

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.   

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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........

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