Trump’s AI Czar and the Wild West of AI regulation: Strategies for enterprises to navigate the chaos

AI is advancing at breakneck speed, but the regulatory landscape is in chaos. With the coming Trump administration vowing to take a hands-off approach to regulation, a lack of AI regulation at the federal level means that the U.S. is facing a fragmented patchwork of state-led rules – or in some cases no rules at all. 

Recent reports suggest that President-elect Trump is considering appointing an “AI czar” in the White House to coordinate federal policy and governmental use of artificial intelligence. While this move may indicate an evolving approach to AI oversight, it remains unclear how much regulation will actually be implemented. Though apparently not taking on the AI czar role, Tesla chief Elon Musk is expected to play a significant role in shaping future use cases and debates surrounding AI. But Musk is hard to read. While he espouses minimal regulation, he also has expressed fear around unrestrained AI – so if anything, his role injects even more uncertainly. 

Trump’s “efficiency” appointees Musk and Vivek Ramaswamy have vowed to take a chainsaw approach to the federal bureaucracy that could reduce it “25%” or more. So there doesn’t seem to be any reason to expect forceful regulation anytime soon. For executives like Wells Fargo Mehta Chintan, who at our AI Impact event in January was calling out for regulation to create more certainty, this lack of regulation doesn’t make things easier.

In fact, regulation around AI was already way behind, and delaying it further meant more headaches. The bank, which is already heavily regulated, faces an ongoing guessing game of what might be regulated in the future. This uncertainty forces it to spend significant engineering resources “building scaffolding around things,” Chintan said at the time, because it doesn’t know what to expect once applications go to market.

That caution is well deserved. Steve Jones, executive VP for gen AI at Capgemini, says that no federal AI regulation means that frontier model companies like OpenAI, Microsoft, Google and Anthropic face no accountability for any harmful or dubious content generated by their models. As a result, enterprise users are left to shoulder the risks: “You’re on your own,” Jones emphasized. Companies cannot easily hold model providers accountable if something goes wrong, increasing their exposure to potential liabilities.

Moreover, Jones pointed out that if these model providers use data scraped without proper indemnification or leak sensitive information, enterprise users could become vulnerable to lawsuits. For example, he mentioned a large financial services company that has resorted to “poisoning” its data—injecting fictional data into its systems to identify any unauthorized use if it leaks.

This uncertain environment poses significant risks and hidden opportunities for executive decision-makers.

Join us at an exclusive event about AI regulation in Washington D.C. on Dec. 5, with speakers from Capgemini, Verizon, Fidelity and more, as we cut through the noise, providing clear strategies to help enterprise leaders stay ahead of compliance challenges, navigate the evolving patchwork of regulations and leverage the flexibility of the current landscape to innovate without fear. Hear from top experts in AI and industry as they share actionable insights to guide your enterprise through this regulatory Wild West. (Links to RSVP and full agenda here. Space is limited, so move quickly.

Navigating the Wild West of AI Regulation: The Challenge Ahead

In the rapidly evolving landscape of AI, enterprise leaders face a dual challenge: harnessing AI’s transformative potential while encountering regulatory hurdles that are often just unclear. is increasingly on companies to be proactive, otherwise, they could end up in hot water, like SafeRent, DoNotPay and Clearview. 

Capgemini’s Steve Jones notes that relying on model providers without clear indemnification agreements is risky—it’s not just the models’ outputs that can pose problems, but the data practices and potential liabilities as well.

The lack of a cohesive federal framework, coupled with varying state regulations, creates a complex compliance landscape. For instance, the FTC’s actions against companies like DoNotPay signal a more aggressive stance on AI-related misrepresentations, while state-level initiatives, such as New York’s Bias Audit Law, impose additional compliance requirements. The potential appointment of an AI czar could centralize AI policy, but the impact on practical regulation remains uncertain, leaving companies with more questions than answers.

Join the conversation: The future of AI regulation

Enterprise leaders must adopt proactive strategies to navigate this environment:

• Implement robust compliance programs: Develop comprehensive AI governance frameworks that address potential biases, ensure transparency, and comply with existing and emerging regulations.
• Stay informed on regulatory developments: Regularly monitor both federal and state regulatory changes to anticipate and adapt to new compliance obligations, including potential federal efforts like the AI czar initiative.
• Engage with policymakers: Participate in industry groups and engage with regulators to influence the development of balanced AI policies that consider both innovation and ethical considerations.
• Invest in ethical AI practices: Prioritize the development and deployment of AI systems that adhere to ethical standards, thereby mitigating risks associated with bias and discrimination.

Enterprise decision-makers must remain vigilant, adaptable and proactive to navigate the complexities of AI regulation successfully. By learning from the experiences of others and staying informed through studies and reports, companies can position themselves to leverage AI’s benefits while minimizing regulatory risks. We invite you to join us at the upcoming salon event in Washington D.C. on Dec. 5 to be part of this crucial conversation and gain the knowledge needed to stay ahead of the regulatory curve, and understand the implications of potential federal actions like the AI czar.