Fat Meat Is Still Greasy: What AI Regulation Delays Really Mean for Your Business.

Why businesses that wait for AI regulation to arrive are making a costly bet

Growing up, I remember my mom and my grandmother saying “Y’all don’t believe fat meat is greasy”. It sounds like a strange thing to say, but what she meant was something more like you won’t believe the obvious until it hurts or it costs you something, which brings to mind the way some businesses are approaching their AI strategy. The warning signs have been there with the Hugging Face incident and now the latest reveal of the Australian government hack, but in the case of some companies, it might take something that hits closer to home before a more proactive approach to AI governance, including cybersecurity is taken more seriously.

AI moved from pilot project to everyday infrastructure faster than almost any technology before it. Employees draft contracts with it. Recruiters screen résumés with it. Customer service teams answer questions with it. Finance teams forecast with it.

Much of that adoption happened informally, one team and one subscription at a time. The laws meant to govern it are still catching up, and their path has been anything but straight. For many leaders, that uncertainty could feel like a reason to wait for more stringent governance, but it’s actually the strongest reason not to.

A regulatory landscape still under construction

In Europe, the AI Act is the most complete framework in the world, yet even it has shifted: a provisional agreement reached in May 2026 delayed application of its high-risk obligations by a year.^[1]

In the United States, the picture is more fragmented. Colorado passed the first comprehensive state AI law in 2024, delayed its start date once, then repealed and replaced it entirely in May 2026 with a narrower statute, now set to take effect January 1, 2027.^[2][3] Along the way, a lawsuit from xAI drew in the Department of Justice, the first time the federal government has intervened to try to strike down a state AI law.^[4]

None of this has produced a single national standard. Congress has repeatedly declined to pass federal AI legislation, and state laws remain in effect while courts and legislatures keep negotiating.^[5] The safest planning assumption is a patchwork that keeps shifting for years, not a rulebook that settles down.

You can “Wait and See” if you want to…

“Wait and see” … another thing my grandmother used to say. It's tempting to read the delays and reversals as breathing room. But if you do that, you’re glazing over a few things:

Liability doesn't wait for AI-specific law. Existing rules on discrimination, consumer protection, privacy, and contract law already apply to decisions made with AI. A hiring tool that screens out protected groups creates exposure under employment law today, and breach caused by an ungoverned AI tool creates exposure under data protection and negligence law, with or without an AI statute on the books.

Governance debt compounds. Every month a company uses AI without an inventory, documentation, or oversight, the eventual cleanup grows. When a regulation lands, or a customer asks hard questions, or a security incident forces a difficult postmortem discussion, someone has to reconstruct what tools were used, what data went into them, and who approved what. That's far harder to do after the fact than to record as you go.

The market is already regulating. Enterprise buyers, insurers, and investors are asking vendors how they use AI and how they manage the risks. For many businesses, the bigger threat isn't a fine. It's the deal that quietly falls through because procurement couldn't get a straight answer.^[6]

The hidden cost of shadow AI

The riskiest AI in most organizations is the AI leadership doesn't know about. Someone pastes sensitive customer data into a public tool. A department signs up for an AI feature buried inside software it already licenses. A team builds an automation that starts quietly influencing pricing or hiring decisions.

Without governance, a company can't answer the basic questions that matter most: What confidential information left the building? Which decisions were shaped by a model, and could that be explained to a regulator or an affected customer? Who's accountable when something goes wrong? These aren't abstract compliance questions. They decide whether an incident stays a manageable correction or becomes a reputational crisis.

What proactive governance looks like

Good governance doesn't require predicting exactly which law passes next. The core practices overlap across nearly every framework in circulation, from the EU AI Act to the lighter disclosure-based rules emerging in US states.^[7]

•      Build an inventory. Know every AI tool in use, including the ones buried inside other software, with an owner for each.

•      Classify by risk. Pay close attention to anything touching jobs, credit, housing, healthcare, insurance, or other high-stakes decisions.

•      Set clear rules for employees. People need to know what data they can and can't put into an AI tool.

•      Keep humans in the loop on high-stakes calls. And give people a way to challenge an AI-influenced decision.

•      Hold vendors to the same bar. Due diligence and contract terms, not just trust.

•      Document as you go. Decisions, testing, incidents. This record is what protects you later.

None of this is exotic. It's the same discipline businesses already apply to financial controls and data security, applied to a new category of tools.

Governance as an advantage, not just a defense

Framing this purely as risk avoidance undersells it. Companies with real visibility into their AI use can scale it with confidence, move faster through procurement reviews, and adapt quickly when new rules land, because the groundwork is already done.

Companies without that visibility face the opposite: a scramble with every new regulation, a gap exposed by every customer inquiry, a surprise buried in every incident.

The bottom line

Regulation is lagging AI adoption, and it will likely stay unsettled for years. But that gap between what the law explicitly demands and what responsible practice requires is exactly where risk builds up.

The better bet is to govern now, on your own terms, while you still get to set the pace. When the rules finally settle, the companies that prepared won't be starting from scratch. They'll just be proving what they already do.

Sources

1.     Carpe Datum Law — Colorado's AI Reset: Two Weeks, a White House Callout, and a Pivot Away from the EU Model — EU AI Act high-risk obligations delayed from 2026 to 2027 under a May 7, 2026 provisional agreement.

2.     Norton Rose Fulbright — Colorado enacts revised AI law — Revised Colorado AI Act rules and January 1, 2027 effective date.

3.     VerifyWise — US AI regulations 2026: the state laws you must comply with — Timeline of Colorado's original SB 24-205, its delayed start date, and its May 2026 repeal and replacement by SB 26-189.

4.     STACK Cybersecurity — Colorado AI Act Compliance Guide — xAI's April 9, 2026 lawsuit and the April 24, 2026 DOJ intervention against the original Colorado law.

5.     Carpe Datum Law — Colorado's AI Reset — Congress declining to enact preemptive federal AI legislation; state laws remaining in effect pending legislative or judicial action.

6.     AI Regulation News, September 2026 (Mean CEO / STARTUP EDITION) — Framing of lost deals, weak documentation, and procurement risk as a bigger business exposure than fines.

7.     STACK Cybersecurity — Colorado AI Act Compliance Guide — Practical compliance priorities under Colorado's revised law: notice, meaningful human review, appeal paths, vendor management, and recordkeeping.

As of September 23, 2026. Regulatory timelines referenced above (EU AI Act, Colorado SB 24-205/SB 26-189) are subject to further change;