The Category Error in Enterprise AI Adoption

You're Not Using the Model Wrong,

You're Using the Wrong Mental Model.

Enterprises have never had more capable AI—and yet results remain erratic, fragile, and hard to scale. Teams respond by swapping vendors, tweaking prompts, or waiting for the next release.

This paper argues that those responses miss the point.

Large language models are probabilistic reasoning systems, but most organizations engage them as if they were deterministic tools. That mismatch creates instability, misdiagnosis, and churn—no matter how advanced the model becomes.

Until the mental model changes, performance will not.

LLMs Aren’t Tools

They’re Instruments That Require Practice

Search engines retrieve. Databases execute. Compilers obey.

LLMs do none of those things.

They construct responses within probability distributions shaped by context, constraints, and prior interaction. Output quality is not binary—it is conditional. And like any instrument, results depend less on the hardware than on how it’s played.

This research reframes AI performance as a practice problem, not a tooling problem—and shows why constraint discipline, role framing, and interaction norms matter more than prompt cleverness or marginal model gains

Why Model Switching Feels Like Progress

(And Why It Never Lasts)

Many teams experience a brief uplift when adopting a new model. Outputs seem sharper. Engagement improves. Optimism returns.

Then results regress.

This paper explains why that cycle is predictable—and misleading. The temporary gains come from heightened attention and behavioral change, not from the model itself. Without durable shifts in how AI is engaged, performance converges back to baseline.

The real variable was never the vendor.
It was practice.

From Prompting to Organizational Practice

Prompting is a tactic.
Practice is an organizational capability.

Sustained AI performance emerges when teams share interaction norms, apply constraints consistently, and treat engagement as a system—not a one-off exchange. Over time, this discipline compounds, transferring across models and tasks.

The implication is structural: successful AI adoption is not about creating better prompt engineers. It is about redesigning how responsibility, judgment, and interaction operate in the presence of probabilistic intelligence.