IT leaders’ concerns over AI regulation compliance
Generative AI is advancing fast. But regulation? Not so much. Or rather, too much, in too many different ways. Today, more than 70% of IT leaders consider regulatory compliance one of their top three concerns when deploying generative AI. That’s not a minor worry. It’s the kind of challenge that can stall innovation, increase risk exposure, and burn valuable time just trying to figure out what’s allowed and what might incur a fine.
Only about a quarter of these leaders feel very confident in their ability to handle AI governance and security. The rest are dealing with constant ambiguity. They’re expected to build compliant systems while regulations are still being written, debated, and redrafted. That makes it hard to plan, harder to scale, and nearly impossible to standardize AI practices across a company.
The core issue is clarity, or the lack of it. Executives need to invest in solid internal governance structures and documentation. Not to add unnecessary processes, but to defend what they’re building. Because when questions come, whether in a boardroom or courtroom, you need to explain not just what the AI did, but how it was designed, tested, and deployed. That defense starts now, not once problems surface.
The organizations that get ahead of regulation will be well-positioned for the future. The ones that let uncertainty slow them down may find themselves reacting instead of leading.
Complexity of a fragmented global AI regulatory environment
Here’s what’s creating real friction for AI deployment across industries: fragmented regulation. It’s not just different from country to country, it’s also different within the same country. Think about how the EU, California, Texas, and Colorado all have their own AI laws, each with specific disclosure rules, risk-management protocols, and audit mandates.
Lydia Clougherty Jones, a lead analyst at Gartner, called the legal nuances across global frameworks “overwhelming.” She’s right. One market might define “high-risk AI” one way, another might interpret it differently. “Developer,” “deployer,” “transparency”, all get redefined jurisdiction by jurisdiction.
These differences shape real obligations and liabilities. For executives handling global operations, a regulation that’s fully compliant in one region might trigger penalties in another. James Thomas, Chief AI Officer at ContractPodAi, said the fragmentation alone creates major operational pressure, not from unwillingness to comply, but because terms like “explainability” and “accountability” aren’t consistent.
For business leaders, this isn’t a wait-and-see situation. Legal complexity scales with the size of your operations. If your AI works across borders, your compliance system has to work cross-border too. That’s longer-term thinking. Build compliance not for this quarter, but for where regulatory pressure is heading over the next five years.
Gartner projects a 30% increase in legal disputes tied to AI by 2028. If you think navigating regulation is a side task in your AI strategy, think again. The future is more regulated. Designing globally compliant systems from the ground up is foundational.
Escalating legal and financial risks from AI regulatory violations
The cost of AI noncompliance is rising, and fast. Gartner predicts that by mid-2026, AI-related violations will total over $10 billion in remediation costs for vendors and users. And by 2028, legal disputes driven by regulatory issues will rise by 30%. Those numbers reflect the pace at which governments are moving to formalize AI governance, and the real-world impact of failing to adapt in time.
Most organizations aren’t ready. The technology is moving faster than legal teams can react. While developers push to integrate generative AI into workflows, few systems are in place to thoroughly validate results, verify model integrity, or trace how outputs were produced. This isn’t just technical debt, it’s legal and financial risk.
Executives need to understand that the responsibility doesn’t end at deployment. The lifecycle of an AI model includes how it was trained, what data it used, who verified the risk factors, and how outputs are monitored. Without full traceability, defending your system in front of a regulator, or worse, in court, becomes guesswork.
Ignoring these steps won’t delay the consequences. If your system makes a decision that’s later flagged as noncompliant or harmful, the burden falls on your team to explain why it wasn’t caught earlier. Waiting for a lawsuit or government audit to address these gaps is short-term thinking. Long-term success comes from building AI systems that assume accountability from day one.
U.S. State-level legislation setting precedents in AI governance
The U.S. federal government is still figuring out how to regulate AI. In the meantime, state-level legislation is setting the tone, and it’s not soft. California, Colorado, and Texas have already passed laws that are shaping how AI is defined, disclosed, and audited. These aren’t symbolic moves; they come with real rules and real consequences.
The 2024 Colorado AI Act requires users to carry out impact assessments and establish risk management programs. The Texas Responsible Artificial Intelligence Governance Act (TRAIGA), taking effect in 2026, prohibits using AI for behavior manipulation and issues civil penalties of up to $200,000 per violation. Then there’s California’s Transparency in Frontier Artificial Intelligence Act, signed by Governor Gavin Newsom in September 2024. It mandates that AI developers disclose how their systems align with standards and report serious safety incidents within 15 days. Fail to comply, and you’re facing fines up to $1 million per violation.
These laws matter beyond their borders. California’s influence is global. With a population of 39 million and 32 of the world’s top 50 AI companies headquartered there, including OpenAI, Anthropic, Databricks, and Perplexity AI, its regulation is effectively international policy for anyone doing business in the space.
C-suite executives should stop thinking of AI regulation as theoretical or distant. These laws are in place, enforcement is coming, and regulators are looking beyond just big tech. State rules, especially in influential markets like California, are setting precedents that will shape compliance requirements worldwide. Prepare your systems for that reality now, not when your legal team brings in the first violation notice.
Heightened accountability of CIOs in AI deployment and compliance
CIOs are under pressure. Not just to deploy AI, but to do it right, with transparency, security, and legal integrity. As organizations race to integrate generative AI, the responsibility to ensure that deployments align with operational goals and meet regulatory requirements falls squarely on technology leadership.
Dion Hinchcliffe, VP and Practice Lead for Digital Leadership and CIOs at Futurum Equities, put it plainly: CIOs are “on the hook” to make this work. That includes validating accuracy, managing data trustworthiness, and ensuring that outputs from probabilistic systems are explainable and defensible. It’s not easy, especially in a space where models don’t always behave the same way twice. Unlike deterministic systems, AI won’t always give predictable results from the same input, this makes auditability and governance especially challenging.
Current compliance and governance tools, while helpful, often lag behind both the evolution of AI capabilities and the pace of regulation. Most were designed for structured systems, not technologies that build models from unstructured data and shift over time. That gap puts CIOs in the position of managing emerging risks without mature controls.
Tech leaders need to be direct and proactive. Build governance into the architecture. Use tools that monitor model behavior throughout its lifecycle. Document choices. Test rigorously. Maintain internal and external audit trails. For high-impact, high-risk AI applications, be ready to show regulators not just what your system does, but that you’ve built in safeguards and tested for unintended consequences. Any shortcuts here lead to bigger problems later.
Risk of exacerbating the digital divide in AI adoption
One of the least discussed impacts of AI regulation is how it might amplify inequality across the tech ecosystem. Large enterprises with resources, legal teams, and enterprise-grade compliance tools are already taking this seriously. But smaller companies, especially rural hospitals, independent clinics, and underfunded organizations, are struggling to keep up.
Tina Joros, Chairwoman of the Electronic Health Record Association AI Task Force, warned that the complicated and inconsistent mix of state AI laws is creating a “regulatory maze.” That maze can deter innovation before companies even start. Smaller players may hesitate to adopt AI tools if they can’t be sure those tools will remain compliant next year. The risk of building something that might later be labeled noncompliant or “high-risk” is enough to cause delays, especially in regulated industries like healthcare, where the stakes are higher.
The terms written into regulations—“developer,” “deployer,” “high risk,” “impact assessment”, are not consistent across jurisdictions. And even bills that haven’t been passed into law still require in-depth analysis by legal teams, something many small or mid-size teams simply don’t have.
For executive leadership, this isn’t a marginal issue. It’s systemic. If regulatory frameworks don’t consider the operating realities of smaller organizations, they’ll widen the adoption gap. The risk is that AI ends up concentrated in the hands of a few dominant players, while others are deterred, not by choice, but by overhead.
The path forward is twofold: first, design flexible systems that scale compliance across organization sizes. Second, push for clearer regulatory guidance, consistency over complexity. Executives who recognize this and act early will build a stronger foundation for widespread, ethical AI adoption.
Challenges of decentralized AI adoption without centralized governance
A lot of AI adoption inside companies isn’t being driven by corporate strategy, it’s happening at the individual level. Employees are experimenting with generative AI through personal productivity tools, custom scripts, and SaaS applications that weren’t built for enterprise-level oversight. It’s fast, and in some cases, effective. But it’s not governed. And that’s the risk.
James Thomas, Chief AI Officer at ContractPodAi, pointed out that these tools often lack centralized controls. They operate in silos, with no unified policy enforcement, no visibility into data provenance, and no scalable framework for compliance or accountability. That undermines enterprise efforts to build consistent, secure, and compliant AI systems.
Without a centralized deployment model, CIOs and compliance leaders can’t track how AI is being used across departments. That opens gaps, in audit trails, in data validation, and in risk classification. Ultimately, it creates blind spots that could lead to regulatory issues, especially as laws become more prescriptive about how organizations must oversee model development and deployment.
The solution is straightforward: AI governance needs to scale with usage. You can’t manage something you can’t see. Enterprises need to treat AI tool management like any other digital infrastructure, standardize it, monitor it, and build policies that apply across the board. That’s how you prevent fragmented use from turning into fragmented liability.
Necessity for robust AI governance strategies, including auditing and testing
Gartner’s guidance on AI governance is clear: if you can’t defend your model’s behavior, it’s not ready for enterprise use. This includes how the model was developed, what data it was trained on, how it makes decisions, and what mechanisms are in place to flag and correct errors. AI works on probability, not certainty, which means auditing and oversight must be part of the architecture from the start.
Lydia Clougherty Jones, Senior Director Analyst at Gartner, emphasized the need for companies to be able to defend “the data, the model development, the model behavior, and…the output.” That can’t rely solely on internal systems. For high-risk implementations, third-party audits bring objectivity and regulatory protection. Especially when facing litigation or external scrutiny, internal validation may not hold enough weight on its own.
Organizations also need to expand their operational practices. This includes more frequent model testing, use-case vetting, and sandboxing before deployment. Content moderation tools, abuse-reporting capabilities, and clear explainability workflows should be integral to any AI product accepted into an enterprise environment.
From the executive perspective, these aren’t just technical requirements. They’re foundation-level elements of AI governance that drive trust, reduce liability, and prepare the organization for compliance under multiple legal frameworks. Taking shortcuts on governance now will create friction, possibly failure, when regulators, or markets, demand answers later.
The bottom line
AI is moving faster than most regulatory frameworks can keep up with, but that gap won’t last. The days of treating AI deployment like an internal experiment are over. Regulatory pressure is building in real time, across states and countries, and it’s already driving up risk exposure and potential costs.
For decision-makers, this is the moment to shift from reactive to strategic. Don’t wait for compliance issues to land on your desk. Build internal systems that assume accountability. Prioritize transparency, traceability, and governance from the start. That includes auditing your models, tracking how data is used, and making sure your teams can explain what your AI systems are doing, and why.
None of this means slowing innovation. It means structuring it with foresight. Companies that can scale AI responsibly and compliantly will lead the next phase of technological adoption. Those that treat compliance as a technical afterthought will fall behind or pay the price.
Leadership isn’t just about using AI. It’s about shaping how it’s used, with purpose, discipline, and trust baked in from day one.
