Five AI Governance Mistakes That Undermine Your AI Return

This voice experience is generated by AI. Learn more.This voice experience is generated by AI. Learn more.Alessio Alionço is the founder and CEO of Pipefy, a global leader in AI-driven business process automation solutions.

getty​Only 21% of organizations reported having a mature governance model in place for agentic AI, according to the Deloitte 2026 State of AI in the Enterprise project. At the same time, nearly three-quarters of respondents expect to be using AI agents at least moderately within the next year. These findings were based on a survey of more than 3,200 IT and business leaders across 24 countries, all of whom were directly involved with their organization’s AI programs.

That gap—between how fast AI is being deployed and how few companies can effectively govern what it’s doing—is where I see five key mistakes play out again and again.

Before we look at these mistakes, it is important to redefine AI governance in a way that is relevant today. Governance used to be about reviewing what AI produced. Now it is about defining what AI is permitted to do before it does it. In regulated and high-stakes operations, that boundary is not a constraint on adoption. It is the precondition for it.​

Governance outlines which actions AI agents can take, under what conditions, with what level of human oversight and with what record left behind. Rather than managing risk after the fact, AI governance proactively provides the operating boundaries within which the technology runs.

​Avoid these mistakes to keep your AI in check.

Companies write approval thresholds, process steps and escalation rules, codify them into manuals and training decks, and then trust people and the AI to follow them.​

But governance that lives in a document is governance that can be overlooked or deliberately ignored. When a rule sits outside the process, someone will eventually find a way to work around it, and the AI will too. As such, governance constraints and off-ramps to human intervention must be built into the architecture of AI tech implementations—not just added on as a document that sits beside the technology.​

I’ve watched teams spend months evaluating which AI system is “safest,” while the real exposure was never in what the AI says. It’s in what it’s permitted to do. A meticulously vetted model with unbounded permissions is still a liability. Make sure your organization is spending at least as much time on governance structure as on AI model selection.​

Being more independent isn’t automatically better. Without a defined scope, an agent that can act freely is a risk multiplier, not a productivity gain. I’d rather see an agent operate reliably inside a narrow, well-defined boundary than one that’s technically more “autonomous” but unpredictable.

This mistake is quieter, but just as damaging. If you can’t answer who authorized an action and what it touched, you don’t have governance—you have hope. When a decision is questioned, “The AI did it,” is not an answer that an auditor or regulator will accept.

When encoding governance rules into processes, make sure you can reconstruct the life cycle of every action: who authorized it, what it touched and when. If you cannot retrace the steps of the decision after the fact, you cannot govern it.​

When AI executes a process, such as approving spend, onboarding an employee or releasing a customer record, it is acting on decisions that belong to the entire business. IT can build the guardrail, but it cannot define where the guardrail goes. That responsibility sits with the people who own the risk, which reaches across departments.​

For example, a wrong action in a hiring workflow becomes both an HR and a legal problem. In a payment flow, finance and compliance are ultimately responsible. Any action touching customer data risks security and regulatory exposure. If IT sets these boundaries alone, it is making judgment calls on risks that it does not own and cannot fully see.​

This is why governance must start at the executive level, where leaders can define shared ownership of rules and risks. Governance should not be a technical configuration handed to IT. It must be a set of business decisions that IT then makes enforceable.

There’s an illusion of tension between governance and innovation. It’s often assumed that every unit of governance costs a unit of speed. In practice, I think the opposite is closer to the truth.

Clear boundaries let people and AI agents move quickly, since they can act without waiting for permission on every step. Ambiguity is what actually slows things down. When teams and agents don’t know what they’re allowed to do, they either freeze or improvise, and both are expensive.​

So don’t think of AI governance as a brake on innovation. A better analogy is the wall of a racetrack. It’s not there to slow the cars down; it lets them drive at full speed without leaving the road.

What agentic AI will be capable of a year from now is as hard to predict as the outcome of any race. But the businesses that come out ahead won’t be the ones with the fastest tech. The winners will be the organizations who built guardrails first, deliberately deciding what AI can and can’t do, before it’s ever let loose on the track.

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