Don't Wait for Congress: What Business Leaders Must Do Before AI Regulation Lands

In March 2026, the White House released its National Policy Framework for Artificial Intelligence — a sweeping set of recommendations that, if enacted by Congress, would create a single federal framework for AI governance and preempt a patchwork of conflicting state laws. The Trump administration has already signed executive orders directing the Attorney General to challenge state AI regulations deemed inconsistent with federal priorities, and conditioning $42 billion in BEAD broadband funding on states rolling back their own AI rules.

This is a politically turbulent regulatory environment. And the instinct for most business leaders is to wait — to see how the federal-versus-state battle resolves before making governance investments that might need to change.

That instinct will cost you.

Why Waiting Is the Wrong Strategy

Here's the reality: the organizations that will have the strongest position when AI regulation crystallizes are not the ones that waited to see which rules would apply. They're the ones that built robust governance infrastructure before they needed to.

This is how it always works in regulatory transitions. When GDPR arrived in 2018, the companies that had already invested in data governance frameworks — not because they were required to, but because it was good practice — absorbed the compliance burden at a fraction of the cost that scrambling organizations paid. The same dynamic played out with SOX, with HIPAA, with every major regulatory shift of the last 30 years.

AI regulation in 2026 is in the same pre-crystallization window. The federal framework is being written. States are fighting for jurisdiction. The exact rules are unclear. But the underlying principles — transparency, accountability, human oversight, bias mitigation, data governance — are already visible in every proposed framework at every level of government. Building to those principles now is not wasted work. It's early advantage.

What the White House Framework Actually Tells You

The National Policy Framework released in March isn't vague. It lays out six clear objectives that should inform your enterprise AI governance strategy right now: protecting against AI-related harms, respecting intellectual property rights, preventing AI-driven censorship, promoting innovation, developing an AI-ready workforce, and protecting children online.

For enterprise leaders, the most operationally significant of these are the first two. "Protecting against AI-related harms" translates directly into accountability frameworks — knowing who is responsible when an AI system makes a consequential decision, and what the escalation path looks like. "Respecting intellectual property rights" means your AI policies need to address training data provenance and output ownership, whether you're using third-party models or building your own.

The framework also explicitly favors relying on existing sector-specific regulators rather than creating a new centralized AI authority. That means your AI governance requirements will likely be layered on top of the regulatory frameworks you already operate under — financial services, healthcare, insurance, manufacturing safety. If you haven't started mapping your AI deployments to your existing regulatory obligations, that work is already overdue.

The State-Level Wildcard

Even if federal preemption moves forward, don't assume state laws disappear quickly. California has already issued new executive orders expanding AI oversight through state procurement. Several states have enacted laws effective January 2026 that remain on the books pending legal challenges. If you operate across multiple states — and most enterprises do — your compliance posture needs to account for this patchwork, not assume it away.

The practical implication: build your AI governance framework to the highest standard you're exposed to, not the lowest. It's far easier to operate a rigorous framework loosely in permissive jurisdictions than to operate a permissive framework in rigorous ones.

What to Build Right Now

AI regulation 2026 is not a future problem. It's a present opportunity to build infrastructure that will become a competitive differentiator. Here's what I recommend prioritizing:

An AI use case registry. Know every place AI is making or influencing a consequential decision in your organization. You cannot govern what you haven't inventoried. This single step puts most organizations ahead of 80% of their peers.

Decision accountability mapping. For each use case, define who is responsible for the output — a human, a team, an executive. AI decisions without human accountability are the highest regulatory risk category across every proposed framework.

A model governance policy. Address the key questions: What data can be used to train or fine-tune models? What models are approved for which use cases? How are outputs reviewed before they affect customers, employees, or operational decisions?

An AI ethics review process. Not a committee that meets once a year, but a lightweight, fast-moving process for flagging high-risk deployments before they go live. The goal is speed plus accountability, not bureaucracy.

None of this requires knowing exactly what the regulations will say. All of it will be valuable regardless of how the federal-state battle resolves. And all of it positions you as a responsible AI operator — which is increasingly a factor in enterprise customer decisions, talent attraction, and board confidence.

The window to build ahead of the mandate is open. It won't be open forever.

Alex Goryachev speaks to executive audiences on AI strategy, governance, and organizational readiness. If your leadership team is navigating the AI regulatory landscape, explore his corporate keynotes and strategic AI advisory — or book a conversation.