There are, at least by my count, hundreds of companies, consortia, and individual projects racing to solve AI governance. Each with a slightly different angle. Each positioning theirs as the definitive framework. Dozens of “patent pending” stamps on what are, functionally, variations of the same idea: constrain the system, log the outputs, enforce the rules.

The ones that work will eventually prevail. Markets sort themselves out. But the sheer volume of nearly identical solutions chasing the same problem, with almost no collaboration between them, tells us something important. Actually, it tells us three things.

The problem is real. The landscape is shifting. And yet, there is a fundamental challenge at the center of all the discourse. Do we need AI at all?

The Problem Nobody Disagrees On

Let’s start with the easy part. AI governance matters. When organizations deploy systems that make decisions affecting customers, employees, revenue, and risk, those systems need guardrails. They need transparency. They need accountability structures. This isn’t controversial. Every CIO, every board member, every regulator on the planet agrees that some form of AI governance is necessary.

The disagreement isn’t about whether. It’s about how. And more critically, it’s about what we think we’re governing.

This is where the conversation fractures. Because buried inside the governance debate is a much deeper confusion about the nature of the technology itself.

The Descent Has Started

For the past several years, enterprise AI has been largely deterministic. Rule-based automation. If X, then Y. A customer who enters this segment gets this email. If a transaction exceeds this threshold, it gets flagged. Predictable inputs, predictable outputs. Governance for deterministic systems is straightforward. You audit the rules, you monitor the outcomes, you adjust the parameters. It’s process management with a technology layer on top.

But the market spent the last two years sprinting toward “agentic” AI. Systems that don’t just execute predefined rules but dynamically interpret context, make multi-step decisions, and take action with varying degrees of independence. The promise of an Agentic Web. AI that reasons, adapts, and operates across workflows without a human approving every step.

And now, organizations are starting to come down from the peak of that hype cycle. Not because the desire has faded. Everyone still wants agentic systems. The problem is that the closer organizations get to actual implementation, the more they discover these systems aren’t reliable enough, aren’t governed enough, aren’t stable or accurate enough to risk deploying them in production.

So the natural response is to place controls on top. More rules. More approval chains. More human-in-the-loop checkpoints. And this is where the paradox begins, because the more governance you layer onto an agentic system, the more you strip away the very thing that made it agentic in the first place.

Organizations are starting to ask a question that nobody discussed during the climb: why invest in building AI systems with dynamic reasoning capabilities if we’re just going to control every decision they make?

That question, and the uncomfortable answers that follow, is what’s pulling the market into the trough of disillusionment. We haven’t hit the bottom yet. But it’s coming this year, and it’s coming fast.

The Contradiction at the Foundation

Here’s the question almost no one in the AI governance conversation is asking: if we constrain every decision an AI system makes, is it actually autonomous?

The industry loves the word “autonomous.” Autonomous agents. Autonomous workflows. Autonomous decision-making. It sounds powerful. It sounds like the future. It sells.

But the moment you wrap a system in deterministic governance, rigid rules, mandatory human approvals, and predefined decision boundaries, you’ve negated the very autonomy you’re claiming. The system isn’t autonomous. It’s heteronomous. It operates under externally imposed laws rather than self-directed reasoning.

This isn’t a semantic distinction. It’s the distinction. And it matters enormously for how organizations should think about governance, risk, and the actual capabilities of the systems they’re deploying.

Autonomous systems govern themselves. They make decisions based on internal reasoning, learned patterns, and contextual interpretation. True autonomy means the system can encounter novel situations and determine its own course of action.

Heteronomous systems operate under external governance. Their behavior is dictated, bounded, and constrained by rules they didn’t create and can’t override. They function within a framework of imposed limitations.

This isn’t a spectrum. It’s a threshold. A system is either making its own decisions or it isn’t. And the vast majority of AI systems deployed in the enterprise today, including the ones marketed as “autonomous agents,” fall squarely on the heteronomous side of that line.

The Simulation of Autonomy

So if these systems aren’t truly autonomous, what are they?

What we’re actually witnessing is something more nuanced and, once you see it, more honest. Call it simonomy, the simulation of autonomy. These systems create the appearance of self-directed behavior while operating entirely under external governance. They look autonomous. They feel autonomous to the end user. But underneath, they are heteronomous through and through.

A simonomous system doesn’t reason freely and then get checked by governance. The governance is baked in from the start. The system generates a simulated action based on what it’s been told to do or what it knows about the workflow. It produces outputs that resemble independent decision-making, but every output is shaped, bounded, and filtered by the rules imposed on it before it ever reaches the surface.

This is not a flaw. In fact, for most enterprise use cases, simonomy is exactly what organizations should want. The illusion of intelligent, adaptive behavior with the safety and predictability of governed execution. The problem isn’t that these systems work this way. The problem is that we refuse to describe them honestly.

When you call a simonomous system “autonomous,” you create a cascade of misaligned expectations. Executives expect capabilities that the system can’t deliver. Customers assume a level of intelligence and adaptability that doesn’t exist. And governance teams build frameworks for a kind of system they don’t actually have.

What This Means for the Governance Landscape

The hundreds of governance solutions flooding the market aren’t misguided about the problem. But the lack of collaboration and the absence of shared language are creating fragmentation where convergence should occur.

If you’re building governance for deterministic automation, you’re solving yesterday’s problem. The organizations buying your solution are already moving toward agentic architectures, and your rule-based framework will become a bottleneck rather than a safeguard.

If you’re building governance for “autonomous AI” without acknowledging that your constraints make the system simonomous by definition, you’re selling a product that contradicts itself. Your customers will be confused about what their systems can actually do, and your governance model will be misaligned with reality.

The governance solutions that will prevail aren’t the ones with the most patents or the slickest dashboards. They’re the ones that help organizations answer a more fundamental question: for this specific use case, what does governance look like when the system is simonomous rather than truly autonomous?

That’s a harder product to build. It requires understanding that the goal isn’t to control autonomy. It’s about designing and managing the simulation well. To ensure that what the system generates as a simulated action is accurate, appropriate, and aligned with organizational intent.

The Human Question at the Center

Underneath all of this, the governance race, the autonomy contradiction, the hype cycle correction, there’s a question that matters more than any framework or patent filing.

What role do we want humans to play?

Governance isn’t just about controlling AI. It’s about defining the relationship between human judgment and machine capability. Every governance decision is, at its core, a decision about how much we trust the system and how much we trust ourselves to manage it.

As the market descends toward the trough of disillusionment, the organizations that will emerge strongest on the other side aren’t the ones with the most sophisticated governance platforms. They’ll be the ones that were clearest about what decisions should remain human, what decisions can be augmented, and what decisions can be delegated to simonomous systems operating within well-designed constraints.

That clarity doesn’t come from technology. It comes from leadership, from organizational self-awareness, and from intellectual honesty in calling these systems what they are rather than what we wish they were.

The governance problem is real. But the solution isn’t more governance tools built in isolation. It’s better thinking, and better collaboration, about what we’re actually building and how it truly works.

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Chris Hood is an AI strategist and author of the #1 Amazon Best Seller Infailible and Customer Transformation, and has been recognized as one of the Top 30 Global Gurus for Customer Experience. His latest book, Unmapping Customer Journeys, will be published in 2026.