Five AI Governance Mistakes That Undermine Your AI Return

Alessio Alionço is the founder and CEO of Pipefy, a global leader in AI-driven business process automation solutions.

​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.

The Top Five AI Governance Mistakes​

​Avoid these mistakes to keep your AI in check.

1. Treating Governance As A Policy Instead Of An Architectural Component

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.​