As governments around the world seek to regulate artificial intelligence (AI), they are creating a patchwork of laws and policy frameworks that organizations operating across multiple jurisdictions must be aligned to – and understand.
In the European Union, for example, the AI Act – described as the “first-ever legal framework on AI” – takes a risk-based approach as part of a bid to “foster trustworthy AI in Europe”.
Chief Strategy Officer at Kore.ai.
While in the US, the federal government has taken an altogether different stance. In March 2026, the White House published its National Policy Artificial Intelligence (AI) Framework, which, among other things, prioritizes deregulation in favor of innovation.
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But the picture is becoming increasingly complex at state level. In July 2026, Illinois signed one of the country’s most comprehensive AI safety laws, requiring large AI developers to introduce transparency frameworks, independent third-party audits and formal risk mitigation measures. The legislation follows similar moves in California and New York, adding further momentum to a growing patchwork of state-level governance.
The divergence is not limited to these powerhouses on either side of the Atlantic. According to a recent briefing note published by the House of Commons Library, the UK “does not have any AI-specific regulation or legislation covering AI as a technology”.
Instead, it reports that AI is “regulated in the context in which it is used, through existing legal frameworks, such as financial services legislation”.
A global response to AI
Recognizing the current direction of travel, in July 2026, the United Nations convened its first Global Dialogue on AI Governance, arguing that international cooperation is essential in a world where AI systems, data and economic impacts routinely cross national borders.
“Artificial intelligence is reshaping economies, societies, and daily life,” it said. “Its opportunities are real. So are its risks.
“No country can address either alone. The AI Dialogue exists to ensure that governance reflects the priorities of all nations, not just the most technologically advanced and that the benefits of AI are shared by all,” it said.
So, while policymakers continue to debate the shape of future governance, it begs the question: how can enterprises continue to innovate and evolve when there is so much regulatory uncertainty?
Stop thinking about governance as compliance
For me, the answer is to take a more pragmatic approach. That means first understanding that governance is not the same as compliance. And second, that organizations need to stop treating it simply as a response to regulation.
Instead, business leaders need to understand that governance establishes a framework for who is accountable for AI, how decisions are made, how risks are managed and how systems are monitored over time.
While it’s true that specific regulations may differ from one jurisdiction to another, the fundamental principles that will deliver ‘good AI’ remain remarkably consistent.
Think about it for a moment. How often do AI projects begin with the same set of questions? Is this risky? Who owns it? Who signs it off? What data can we use? How will it be monitored? What happens if something goes wrong?
When you’re faced with a barrage of uncertainty, is it any wonder that projects stall even before they start?
But with the right governance in place, many of these questions have already been answered. Instead of repeatedly reinventing the wheel and debating the same issues, organizations can focus on the task at hand.
This is what good governance looks like. And it’s why it has the ability to remove friction, speed up decision-making and allow organizations to move faster.
The trust advantage
At an operational level, it gives teams an agreed starting point and set of rules. But the benefits extend beyond the organization itself.
Sharing these frameworks with customers, regulators, investors and other stakeholders is a sure sign that governance has been built in from the ground up rather than bolted on as an afterthought.
In doing so, organizations can show that they are taking accountability, oversight and risk seriously. And in an era where confidence in AI remains fragile, that trust can become a competitive advantage.
For anyone looking to implement governance, there are five key areas you need to establish.
Establish clear ownership: Every AI initiative should have a clearly defined owner – someone who is responsible not only for deployment but also for its ongoing governance.
Put oversight mechanisms in place: Organizations need processes to monitor, challenge and review AI systems, particularly as they become more autonomous.
Define your risk appetite: Not every AI application carries the same level of risk. That’s why organizations need to establish clear criteria for assessing use cases before deployment.
Create accountability from the outset: Build accountability from day one. Teams should know who makes decisions, who approves deployments and who owns the outcome.
Review and adapt continuously: AI governance is not a one-off exercise. Review your framework regularly as technologies, regulations and risks evolve.
While each of these has its place on the AI governance leaderboard, if I were pushed to say which is the most important, I would have to opt for clear ownership. Why? Because without clear accountability, governance risks becoming everyone’s concern but nobody’s responsibility. And when that happens, it can lead to paralysis.
As I said at the beginning, while policymakers may still be figuring out legislation, there is nothing to stop organizations from adopting good governance. In fact, regulatory uncertainty makes good governance more important, not less.
The organizations that establish the right structures, processes and accountability now will be best placed to adapt as the regulatory landscape continues to evolve.
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