Most enterprise AI discussions are stuck in the wrong debate.
We keep asking: Is the model accurate enough? Is it explainable enough? Is it safe enough?
A recent Sage Journal study (DOI: 10.1177/29498732261443099) suggests a more uncomfortable truth:
Those questions are incomplete—because they assume AI behaves like traditional enterprise systems. It doesn’t.
AI introduces a fundamentally different reality where systems are shaped by four behavioral facets:
-Autonomy (it acts)
-Learning (it evolves)
-Inscrutability (we cannot fully trace reasoning)
-Anthropomorphism (humans instinctively over-trust it)
This combination breaks the classical enterprise assumption of controllable, fully observable systems.
And it exposes a governance gap most boards are not prepared for:
We are still trying to govern AI as if transparency, explainability, and reliability are features.
In reality, they function as control mechanisms over systems we do not fully understand or stabilize.
The uncomfortable implication for leadership:
AI risk is no longer model risk.
It is interaction risk—emerging from how humans and AI co-adapt over time in operational environments.
Which leads to a sharper question for boards and CxOs:
If your AI system cannot fully explain itself, and continuously evolves its behavior—
what exactly are you governing?
-The model?
- The data?
- Or the illusion of control?
Enterprises that treat AI as a software upgrade will optimize efficiency.
Those that recognize it as a new class of decision actor will need to redesign governance itself.
Source: Sage Journals, DOI: 10.1177/29498732261443099
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