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This paper investigates the limitations of current generative AI tools in workplace settings by examining the disconnect between how developers, users, and social scientists conceptualize "context." Through expert interviews, the study reveals that generative AI systems often fail to adequately account for users' contextual needs, leading to "context collapse" and degradation over time. The authors argue for a shift from indiscriminate data collection toward interactional practices to better embed GenAI systems within specific user contexts.
Generative AI's much-hyped contextual awareness falls apart in the workplace, leaving users to pick up the pieces.
As generative AI technologies are pressed into service in workplace settings, current approaches to account for the contexts in which such technologies are used fall short of users'expectations and needs. This paper empirically demonstrates, through expert interviews, both how these tools fail to account for users'context and how users deploy concrete strategies address such failures. The paper analyzes how context is variously conceptualized by tool developers, users, and social scientists to identify specific pitfalls inherent in computational approaches to context. Multiple distinct contexts tend to collapse into one another or rot, degrading over time, reducing the utility of any efforts to account for context. The paper concludes with a provocation to shift from an indiscriminate collection of context-relevant data toward a more interactional set of practices to embed GenAI systems more appropriately into users'contexts of use.
