Adoption and development of AI tools are accelerating faster than many risk, legal, and compliance frameworks can keep up. Only 8% of business leaders feel prepared for AI and AI-governance risks. And only 35% of companies have an AI governance framework despite the pervasiveness of the technology.
Organizations often look to regulation as a north star for governance, but AI legislation is a moving target. Regulations can arrive out of nowhere, prompting compliance scrambles – or move so slowly it’s unclear what to prepare for and if they’re coming at all.
The gap between AI use and regulation may widen before it narrows. If your organization already deploys AI, you can’t put the genie back in the bottle. You can, however, institute AI governance practices to ensure AI is used responsibly, transparently, and fairly – without sacrificing technological innovation or risking noncompliance with existing and future regulations.
AI Rules Are Here, But Uncertainty Remains
Last year marked a turning point for AI regulation. The European Union passed the AI Act, the world’s most comprehensive legal framework for artificial intelligence. As the law takes shape, governments are still debating enforcement timelines and implementation remains uncertain.
In the U.S., state governments stopped waiting on Washington. Colorado passed the country’s first law relating to the development and deployment of certain AI systems. It requires organizations to conduct risk assessments, notify consumers of AI use, and adopt internal controls. Other states – California, Utah, New York, and more – are following with legislation of their own.
AI regulation is no longer theoretical – and the fines of noncompliance are steep. But regulation is not settled either. U.S. federal regulation is still in flux. This can create a confusing environment for companies to navigate. But waiting for complete clarity on external standards and regulations to govern AI use is a risk in itself.
Beyond Regulations: Governing AI
Regulatory compliance is just one piece of the puzzle. AI governance lays the foundation for responsible and scalable AI use that supports broader business goals. Without governance and oversight, an employee who uploads trade secrets to ChatGPT to create a presentation faster could hurt the company’s competitive position. An executive leadership team that makes a major strategic decision based on hallucinated data could lead to disaster. The threats of AI are already unfolding, from hallucinations and deepfakes to bias, model drift, and data privacy issues.
Now, layer in the rise of agentic AI – autonomous systems that can independently plan, make decisions, and execute tasks to achieve specific goals, often without direct human intervention – and stakes are even higher. These tools promise speed and efficiency, but without oversight, they can behave unpredictably, act unethically, or even violate laws.
Yet, 59% of organizations say leadership teams don’t actively guide and support enterprise generative AI initiatives and governance with actionable plans and strategies. And only 19% have trained or briefed employees on generative AI threats at all.
If this were cybersecurity, those numbers would trigger red alerts. The same urgency should apply to AI governance, which is quickly emerging as a board-level issue and defining business challenge.
Five Moves to Make Now
Start with these five steps to manage AI responsibly in your organization.
- Map your AI footprint. Identify where AI is in use across the business. Include everything from internal tools and customer-facing chatbots to third-party vendors using AI behind the scenes. Identify which use-cases pose the highest risk, especially where personal data, decision-making, or automated outputs are involved.
- Prioritize smart compliance. No two jurisdictions handle AI the same way, and the regulatory environment continues to evolve. Instead of reacting to each new law as a one-off requirement, take a proactive and centralized approach. Use technology to map where your existing controls align across multiple regulations – and where gaps exist. This unified, tech-enabled view helps simplify compliance, highlight shared requirements, and keep pace as rules change.
- Create clear policies. Sixty-five percent of companies don’t have a policy in place to govern the use of AI by partners and suppliers. That’s a major blind spot. Establish internal and external guidelines covering data use, transparency, accountability, and ethical boundaries – and ensure they evolve with the risk landscape.
- Involve risk teams early. Risk teams should guide AI adoption from day one – not show up after an issue surfaces. Give them a seat at the table so they can help shape safe and scalable systems. And train all employees on how to use AI responsibly and recognize red flags. Cross-functional collaboration among legal, IT, risk, compliance, HR, and business teams is critical to spotting issues before they escalate.
- Upgrade your systems. Outdated tools can’t keep up with modern threats. Replace manual tracking and spreadsheets with integrated platforms that provide real-time visibility, continuous monitoring, and dynamic risk assessment – especially as models drift or adapt over time.
Move Fast, Build Trust
AI adoption won’t slow down – and regulation won’t get simpler. As the global patchwork of AI laws continues to evolve, organizations must stop waiting for perfect clarity. What’s needed now is continuous internal oversight. Move fast to implement adaptive frameworks that help you monitor, adjust, and govern AI tools as they evolve. A “set-it-and-forget-it” or “check-the-box” approach simply won’t work.
Companies that take governance seriously now will be more strongly positioned. They’ll embed AI oversight into their GRC functions – not to stifle innovation, but to guide it. Lagging AI governance isn’t just a liability; it’s a strategic risk you can’t afford to ignore.
