The Promise and Peril of Generative AI for Organizational Selection and Socialization

Details

Co-Authors

James Chu and Sameer B. Srivastava

Category of Paper

Peer-Reviewed Research Papers

Tags

AI, Computational Linguistics, Generative AI, Machine Learning, Natural Language Processing, Organizational Culture

Abstract

Organizational survival and success depend on having members who have a shared understanding about the enterprise’s purpose and strategy. Organizations therefore invest heavily in the selection and socialization of new members. Since the public release of generative artificial intelligence based on large language models (GAI) in 2022, organizational leaders have been grappling with foundational questions about how this new technology will reshape these core activities. Although it is difficult to make precise predictions amid ongoing technological ferment, here we offer informed guesses about the trajectory of GAI-driven change in organizational selection and socialization. To organize our predictions, we draw on three key conceptual distinctions. First, we distinguish between the ability of GAI to select and socialize individuals who are internally committed to organizationally desirable values, versus individuals who only perform these values. Our second distinction pertains to the cross-pressures of fitting in versus standing out within organizations. Third, we distinguish between how GAI is adopted initially, and responses to these configurations by strategic actors, which we refer to as “second order effects.” Based on these distinctions, we array our predictions across three phases, with each new phase a response to the tensions and dissatisfactions of a preceding one.

“The Promise and Peril of Generative AI for Organizational Selection and Socialization.” Forthcoming: Journal of Organization Design.

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