Position: Adopting AI in Practice Does Not Guarantee the Productivity Boost

2026-05-23

AI Summary

This study challenges the assumption that AI deployment inherently enhances organizational productivity, demonstrating how its benefits are often attenuated by human and contextual factors. Moving beyond technological determinism, the research integrates organizational behavior theory with AI implementation practices through a partial equilibrium framework to systematically examine five moderating variables: workforce composition, individual baseline capabilities, learning curves, incentives for equitable usage, and goal flexibility. The findings reveal critical limitations in prevailing economic models that overlook non-technical dimensions, thereby reconceptualizing theoretical frameworks of organizational effectiveness. By bridging disciplinary insights, this work offers actionable implications for industry practice and educational policy, advocating for coordinated, multidimensional strategies to optimize AI deployment.

Abstract

This position paper argues that adopting AI in organizational practice does not guarantee productivity gains, because human and environmental factors critically moderate the relationship between AI deployment and realized productivity improvements. Following the advent of high-performance generative models, AI use has been rapidly encouraged in some sectors while being restricted in others. Most practitioners assume that AI brings productivity boosts owing to enhanced technical capabilities, but regardless of apparent performance advances in AI technology, human and environmental factors of the organization may substantially attenuate -- or even negate -- the effective productivity benefits. We identify five key moderating factors: human resource composition, baseline capability of individuals, learning curve of practitioners, incentives for fair use, and flexibility of objectives. Drawing on the partial equilibrium model of Gries and Naudé (2022), we argue that existing economic frameworks may inadvertently overlook these factors. We revise the existing framework to redefine effective organizational determinants and shed light on practical implications including industry and education, responding to alternative views and calling for action of stakeholders.

Problem

• AI adoption
• productivity gains
• human factors
• environmental factors
• organizational practice

Innovation

• AI productivity
• moderating factors
• organizational adoption
• human-AI interaction
• economic framework