One Technology, Divergent Outcomes: Extending Adaptive Structuration Theory to Explain Heterogeneous Organizational Responses to Generative AI

Abstract

This paper explains why organizations that adopt the same generative AI/LLM technology end up with different results. It argues the real driver isn't the technology itself but "appropriation" — how employees actually use it day to day, which can differ from how it was intended. Using Adaptive Structuration Theory (DeSanctis & Poole, 1994) — originally built for older, simpler group technologies — the paper adapts it to fit modern AI's more flexible, unpredictable, conversational nature, and connects it to how organizations choose to automate, augment, or redesign work around AI. It offers a model, seven research propositions, a plan for future testing, and practical guidance for managing AI use responsibly.