The Workplace GenAI Usage Divide: A Genetics-Informed Cognitive and Metacognitive Account

Generative AI (GenAI) is increasingly deployed in organizations, yet employees differ substantially and persistently in how frequently and intensively they use it. This workplace “usage divide” raises a fundamental but underexplored question: Why do employees differ systematically in their use of GenAI, even under common formal access? We argue that this divide reflects a broader challenge for IS research: explaining the use of systems that are probabilistic rather than deterministic, and general-purpose yet uneven and evolving in their capabilities. In workplaces where employees remain accountable for task performance, these properties make the use of GenAI not a routine adoption decision but a judgment process in which employees assess the system’s credibility and determine where task-relevant expertise lies. Drawing on molecular genetics and metacognition research, we theorize that genetic propensity, indexed by the educational attainment polygenic score (PGS)—an aggregate genetic index associated with educational attainment—helps account for systematic variation in these judgments and, ultimately, persistent differences in workplace GenAI use. Specifically, we develop a parsimonious two-pathway model linking educational attainment PGS to GenAI use: a cognitive pathway involving formal educational attainment and perceptions of GenAI credibility, and a metacognitive pathway involving strategic mindset and metaknowledge of where task-relevant expertise lies. We test our model using a multisource dataset combining primary genetic data, survey measures, and 21 months of objective usage logs. The results show that educational attainment PGS, a broad distal genetic index, is associated with both GenAI use frequency and interaction intensity and provide theory-consistent evidence for the proposed pathways. Overall, our study offers a micro-foundational account of the persistent GenAI usage divide and advances IS theory on the use of probabilistic, general-purpose systems in the workplace.