The case for slowing AI down got turbocharged last week when Anthropic researcher Jacob Coxon publicly resigned citing AI’s potential to end humanity. Anthropic CEO Dario Amodei then posted a nearly 4,000 word essay arguing for an AI slowdown. In a rare bout of unity, Sam Altman, Elon Musk and others quickly endorsed the idea of slowing down. Despite these calls, government intervention to slow down AI developments looks unlikely for now.

While an active debate on both sides of this topic gains steam, there is another kind of risk that is not getting discussed: companies that move too slowly in grasping the implications of AI will likely see their own form of a slow down. That is why outside of frontier AI labs, the rest of corporate America needs to speed up.

Some of corporate America’s slowness in adopting AI is because the talent pool who know what they are doing is still small. This argues for upskilling and reskilling to meet demand and fill emerging AI job categories. However, some of the slowness can be attributed to a cautious approach or even self-protection. But those who are covering themselves need to know they have competitors that won’t wait.

I help the executives and boards of companies from numerous industries grapple with the opportunities and risks of AI. Everyday I see their urgency to understand and get ahead with AI in industries as varied as defense, food distribution, reinsurance, utilities, manufacturing, consumer products, retail, engineering, and international banking.

American companies are under tremendous pressure to accelerate their AI adoption. AI now ranks as the top issue on public company board agendas for 65% of public company directors in a recent survey. And that makes sense. AI is evolving fast and beginning to show the outlines of cross-industry disruption. Corporate America understands the stakes, and they are not waiting for federal regulators to help (or hinder) them.

For now, the powers that be are leaving the big questions about AI to the companies themselves. Corporate America knows they are the ones who need to grapple with AI. The worry is that given how fast AI is moving, very few corporate leaders know exactly how to approach the defining issue of our time.

Only 22% of S&P 500 companies and 6% of the Russell 3000 disclosed board oversight of AI while only 29% of leaders say they have the right expertise on their boards to advise on AI implementation. Without major federal regulations setting the guardrails for how companies adopt AI, the big decisions about how AI is being deployed are being made in the boardroom, not the halls of Congress.

The good news for the private sector is they are used to moving faster than Congress. The bad news is if they move too fast without getting their heads fully around the nuances of AI, it can cost them dearly.

Take the example of Ford trying to run before they could crawl. Ford leaned hard into AI for vehicle quality, installing 900 AI-assisted inspection cameras and automated quality systems meant to replace veteran engineers. Their AI systems, however, failed to live up to the hype. Ford’s VP of vehicle hardware engineering was quoted as saying “mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high-quality product.” The false start cost them time and money.

Despite the set-backs, Ford learned from their mistakes. They re-hired veteran safety engineers who set about training the automated systems and mentoring young workers. The technology improved with human input and Ford just returned to the top of the JD Power rankings that measure vehicle quality and safety.

So how do leaders balance the need to act quickly with the risks of getting it wrong?

The first step is strategy, not technology: set a vision, educate leadership and work to set up structures, policies, and quick-win pilots. For most companies, the quickest gains are going to be realized through making humans more productive and powerful, not by getting rid of them. This crawl phase is all the more important because of some of the limitations inherent in today’s AI capabilities.

After you crawl, you can start to walk. That involves developing complex use cases, tracking and revising what you do, and monitoring risk and ROI closely. Think how to recruit and upskill your workforce, not decimate it. Next you can start to apply these new organizational skills across the entire business, scale AI capabilities, drive new experimentation, and build out the right partnerships and infrastructure.

Finally, you can run. This is where the real rewards are unlocked: developing next-generation technology, discovering new solutions and conceptualizing never-before-seen products. This stage is where companies can get really bold and shoot past efficiency gains and towards raw, new value creation.

The risk for most companies is that they are stuck in the crawl phase while their competitors are already planning how they will run.

The argument consuming all the attention this week is about who builds AI. Almost nobody is discussing who deploys it. This is where the rubber hits the road for the vast majority of Americans. The AI industry will continue to create incredible new tools while improving safety. But someone has to govern how the rest of the economy deploys these capabilities. The opportunities and risks are too important to be left to chance. As Washington D.C. decides how to engage, the job belongs to the boardroom, whether directors are prepared for it or not.

Ryan McManus is the President of the National Association of Corporate Directors New York chapter. He is also the founder and CEO of techtonic.io, where he works with boards, CEOs, and investors on AI.

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