Most Companies Are Scaling AI Faster Than They Can Control It. Here’s Why That’s A Problem

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Key Takeaways Most organizations deploy AI without aligning governance, leaving critical risks misunderstood and unaddressed

Without clear ownership, AI decisions lack accountability, increasing exposure across legal, operational, and reputational fronts

AI doesn’t create new problems, it exposes existing governance gaps at unprecedented speed and scale

Back in 2013, Target made headlines globally when a cyberattack exposed the payment card information of 40 million of its customers, along with the personal data of 70 million others.

At the time, the breach was widely described as a cybersecurity failure, but it was more than that. It was also, by and large, a governance problem, one that mirrors what we’re seeing today as organizations look to scale through AI.

With no federal framework in place to guide how AI is governed in practice, organizations are defining their own guardrails to support responsible implementation and build trust. But the absence of regulation doesn’t mean the absence of risk. Organizations deploying AI today are still operating within existing legal structures that govern areas like data privacy, consumer protection, and employment practices, to name a few. If an AI-assisted decision exposes personal data or introduces a material error, the organization remains accountable.

AI governance can’t afford to wait for regulation to catch up. The Target breach and the years that followed marked a watershed period that elevated cybersecurity to a board-level risk. During that time, I was brought in to lead information security for an operator of critical internet infrastructure. Like many in that moment, I was forced to examine where governance hadn’t kept pace with operations.

As someone who’s spent her entire career in technology, I’ve come to know one constant. Technology moves, and governance rarely keeps up until it has to. Enterprise resource planning, or ERP, implementations, for example, have been widely adopted for decades and rarely fail because of the technology itself. The challenge is getting an organization to align on a single version of the truth across data, processes, and systems.

AI is that same forcing function, one generation later. Organizations that haven’t resolved those underlying issues are about to encounter them again with AI adoption, but at a much higher speed.

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