AI Is Not an Employee: Workplace Mental Models and the Context Discovery Gap in Human-AI Interaction

Abstract

AI adoption can falter not because systems lack capability, but because organizations apply employee mental models to systems that do not participate in work as socially embedded employees. This article develops a theoretical model of that mismatch, drawing on theory of mind, psychological contracts, shared mental models, transactive memory, and automation reliance research. It introduces the context discovery gap: a mismatch between the organizational context people assume AI already has or will discover and the context it can actually obtain and use in a workplace. Employee-associated expectations may contribute to this mismatch by leading users to leave relevant background unstated. The article argues that the missing psychology of AI adoption lies in the human assumptions through which AI systems are understood, delegated to, and evaluated. As a conceptual contribution, it connects these assumptions to context provision and reliance, develops testable propositions, and identifies responsibilities for designing and maintaining context discovery.

A question of context

Workplace metaphors make unfamiliar technologies easier to understand. They can also carry expectations that a computational system cannot meet simply by being called a colleague.

This conceptual article examines the context discovery gap: human participation can surface missing organizational context as work proceeds, while AI systems depend on arrangements that make that context available or its absence visible.

It is a conceptual paper: it offers a theoretical model and testable propositions, not new empirical data. It ties workplace mental models to context provisioning, calibrated reliance, and responsibility mapping in human–AI collaboration.