Opinion: AI Governance Needs to Get Out of the Ivory Tower

By Courtney King | July 18, 2025

The AI governance conversation is full of brilliant people. There is no shortage of PhDs, policy wonks, and task force working groups. We have frameworks. We have guidance. We have principles—OECD’s, UNESCO’s, NIST’s, take your pick.

But here’s the problem: all of this thought leadership has become an echo chamber. And while the academic and policy communities are busy refining their frameworks, AI itself is sprinting laps around the rest of us.

Take the EU AI Act. It is arguably the most comprehensive regulatory effort to date, and it deserves credit for that. But let’s be honest. Most of the companies it affects are still struggling to understand how to operationalize it. It’s dense. It’s bureaucratic. It’s hard to read without legal counsel sitting next to you translating every page. It is regulation, yes, but it is not a roadmap. It is not something you can build around without serious interpretation and translation.

Then there are the NIST AI Risk Management Framework and the OECD Principles. These are not regulatory instruments, but they set the tone for what "responsible AI" is supposed to mean. They are smart, thoughtful, even inspiring. But they live at 30,000 feet. Their language is theoretical. Their implementation steps are vague. They’re useful starting points sure, but they are not something a mid-sized organization company can hand to a CTO and say, "Build this."

Meanwhile, AI is being integrated into hiring, lending, healthcare, education, and law enforcement. This is happening often quietly, often imperfectly, and often with real downstream consequences for real people. And it is happening today, not five years from now.

So where is the conversation about that? Where is the step-by-step guidance for a company trying to roll out a new AI product without violating privacy law or inadvertently building a biased model? Where are the concrete discussions about navigating organizational resistance, cross-functional misalignment, or tooling gaps? Where are the practical methods for translating policy into product decisions, or governance principles into engineering guardrails?

Right now, there is a major gap between the theory and the reality. Even in the communities that understand the stakes the most, we are seeing a kind of inertia. More academic papers. More summits. More circular debates about AI ethics and alignment. More frameworks that look good in slides but evaporate in the day-to-day work of building and shipping software.

Don’t get me wrong; the thinking is necessary. The frameworks are useful. But talk is not governance.

Governance is decisions. It is tradeoffs. It is implementation. It is what happens when the data team disagrees with Legal. It is what do we do when our developers want to use a new foundation model we have not vetted yet. It is rollout plans. It is documentation. It is oversight that actually fits inside the messy realities of a real-world business.

And that messiness is exactly where the governance conversation needs to go next. Because if we stay stuck in a loop of conceptual debates, we are going to lose the plot. AI is not waiting for consensus. It is not going to pause until we figure out the perfect definition of fairness or safety. It is already shaping real lives in real ways. That means governance has to be real too.

At Stillwater Insights, we are not writing another white paper about the moral implications of AGI. We are helping organizations get their hands dirty building governance systems that work in the here and now. We help translate lofty ideals into actual processes. We help privacy teams work with product teams. We help executives understand what is at stake and what it means to lead responsibly in a world being reshaped by AI.

We have helped organizations write policies that map to actual workflows. We have worked with companies that are navigating tough choices about third-party AI tools, user transparency, and model oversight. We have built governance practices that are agile enough to evolve but strong enough to hold people accountable. And we have done it with teams who are already stretched thin and under pressure to ship fast.

This is not theoretical work. It is not academic. It is not a nice-to-have. It is the core infrastructure of a future-proof business. And if we want AI to truly serve people rather than just optimize profits or replicate existing power structures, then governance cannot be a back-office function. It has to be woven into the way companies design, build, and deliver their technology.

It is time to stop treating responsible AI like a philosophy seminar. This is not just about ideas. It is about action. It is about getting honest about what it takes to build AI systems that are not only powerful but also trustworthy.

We are doing that work. And we would love to see more people in this space join us in the trenches.

Interested in learning more? Click here to get in touch.