What does this research mean for the field?

Organizational AI integration can be modeled as a three-layer controlled decision process—comprising generation, selection, and governance—to predict and measure operational stability, productivity gains, and performance inequality. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to develop a framework for understanding the impact of LLMs on organizational productivity and decision-making.

May 30, 2026Open Access

The Operator Theoretic Foundations of Cognitive Amplification (CAIT) in LLM Systems

Key Points

The aim is to develop a framework for understanding the impact of LLMs on organizational productivity and decision-making.
Developed a model comprising a generative layer, selection layer, and governance layer.
Identified five measurable aspects of organizational functioning, using operational data for estimation.
Evaluated conditions for system stability based on organizational data.
Model indicates AI suggestions can become more predictable, impacting performance levels.
Findings predict when AI may destabilize workflows and widen gaps between worker performances.
Framework provides diagnostic tools to assess AI governance and its effectiveness in reducing risk.

Abstract

This paper presents a practical and testable framework for understanding how Large Language Models (LLMs) influence productivity, risk, and decision-making inside modern organizations. The model decomposes AI behaviour into three interacting layers: a generative layerthat produces options, a selection layer that determines which option is used, and a governancelayer that enforces policies, safety rules, and organizational constraints.The framework tracks five measurable aspects of organizational functioning: the amount ofjudgment employees must exercise, the psychological strain created by AI-assisted work, thequality of AI utilization, the depth of reliance on AI, and the realized performance gains. Eachof these quantities can be estimated from operational data such as decision logs, verificationoutcomes, error corrections, and AI usage patterns.The paper models the organization as a controlled decision process in which AI-generatedsuggestions, human verification, and governance rules jointly shape how work evolves over time.The framework identifies conditions under which the system settles into a stable operating stateand conditions under which it may become unstable, overloaded, or oscillatory. These stabilityboundaries can be evaluated directly from organizational data.The model provides leaders with concrete diagnostic tools. It enables the measurement ofwhether AI suggestions are becoming more predictable or more variable, whether governancerules are effectively reducing risk, whether AI dependence is rising faster than employee capacity,and whether performance gains are sustainable. The framework also predicts when AI willincrease productivity, when it will widen performance gaps between workers, and when it maydestabilize workflows.Overall, the paper offers a rigorous but accessible foundation for managing AI-enabled organizations. It clarifies how AI generation, decision filtering, and governance interact to shapeperformance, inequality, and operational stability, and it provides a set of measurable indicatorsthat organizations can use to validate or falsify the model using their own data.

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