AI governance's next act: More reality check than retreat

Fast Company

3 days ago

At this phase of AI's emergence in business, leaders are facing the risk of trying to regulate before they've gained enough experience deploying AI at scale. It's like drafting traffic laws before the first car hits the road—when theory meets reality, everything changes.
AI is very much in its 'dress for the job you want' moment. That brings both promise and precaution. A recent Brookings Institution report on balancing innovation and regulation warns that 'regulation that intends to prevent risky AI by limiting the size of the model may […] inadvertently prevent the development of the very technology that would solve that problem.'
That's why now is the time to shift from reactive, fear-based frameworks to proactive, calculated experimentation. The winners will be those who manage risk without smothering innovation. Let's unpack what that looks like.
AI must be treated as a business strategy, not a compliance checkbox. The earliest AI success stories didn't emerge from the biggest models—they came from businesses that turned innovation into impact. Generative AI rollouts follow a familiar pattern: test, learn, iterate. That approach is a hallmark of resilient organizations.
A McKinsey report described AI as moving from a productivity enhancer to a 'transformative superpower.' But that evolution only happens when companies move past basic automation to unlock new business value.
Their conclusion? The biggest barrier to scaling AI isn't talent or tech—it's leadership. Specifically, the courage to act.
THE POWER OF THE PRIVATE SECTOR
According to S&P Global, the U.S. is leading the world in private AI investment. From 2013 to 2023, U.S. firms invested three times more than any other country. More than 5,500 AI companies were founded in that decade, and projections suggest private AI investment could hit $900 billion by 2027—close to 0.7% of global GDP.
That's momentum you don't pause. Regulation must protect people—but not at the cost of progress. Innovation depends on the freedom to experiment. And right now, we're just scratching the surface of what AI can unlock.
HOW ENTERPRISES CAN GAIN AND MAINTAIN AN EDGE
AI leadership isn't about drafting better rules. It's about executing at scale. That takes three things: sound governance, bold experimentation, and relentless execution.
Sound governance within an organization defines how AI is applied, what's allowed and what's off limits, and which data is fair game. The key is finding balance—policies shouldn't control development, but rather provide safeguards so innovation can steer. One way to do this is ranking AI projects by risk level.
At the recent AI Action Summit in France, the tone was unmistakable: innovation first, safety second. That's a controversial stance—but it could be an inevitable one. As the UK navigates its proposed AI Act, serious questions remain about whether regulation will stifle AI innovators or enable them.
Safety still matters. But if you lead with caution and forget ambition, you'll be sidelined. The real risk isn't recklessness—it's irrelevance. In AI, you're either building the future or waiting for someone else to.
'If you build it, they will come.' But if you regulate it before it gets off the ground, you'll never govern what never existed.
The future belongs to those who move. Those who use AI, scale it, learn from it. That's where the breakthroughs will come from. Because at the end of the day, ethics without execution is just theater—and no one wants to star in that show.