Middle-Manager Sensemaking During Artificial Intelligence Transformation

Linda Rouleau

Middle-Manager Sensemaking During Artificial Intelligence Transformation

Author(s)	Prof. Linda Rouleau
Country	United States
Abstract	Artificial intelligence transformation alters more than organizational technology. It modifies decision authority, occupational boundaries, information flows, performance expectations, supervisory practices, and employees’ understanding of how work should be performed. Middle managers occupy a particularly consequential position during this transition because they must simultaneously interpret strategic AI initiatives communicated by senior leadership and translate those initiatives into operational meaning for employees. Yet much of the discussion surrounding organizational artificial intelligence adoption emphasizes technical capability, executive strategy, employee acceptance, or algorithmic performance while giving comparatively limited attention to the interpretive work conducted by middle managers. This study develops a sensemaking-centered framework for examining how middle managers respond to ambiguity during organizational AI transformation. A simulation-based explanatory design is used because no primary organizational dataset was supplied. The analytical framework represents AI transformation through five stages—awareness, interpretation, experimentation, integration, and stabilization—and evaluates changes in ambiguity, role clarity, trust calibration, communication translation, and overall sensemaking effectiveness. The simulated results indicate that the sensemaking effectiveness index increases from 48 during the awareness phase to 84 during stabilization, while organizational ambiguity declines as managerial interpretation, contextual translation, and experience with AI-supported work accumulate. The findings suggest that middle managers should not be treated merely as implementation conduits. They operate as interpretive intermediaries who connect technological capability with organizational context, mediate employee concerns, identify contradictions between algorithmic recommendations and operational realities, and shape whether AI transformation is understood as displacement, surveillance, augmentation, or organizational learning. The study contributes a structured framework for integrating sensemaking theory with AI-enabled organizational change and proposes that successful transformation depends not only on technological readiness but also on the organization's capacity to develop interpretive capability at the middle-management level.
Keywords	artificial intelligence transformation, middle managers, organizational sensemaking, digital transformation, human–AI collaboration, managerial interpretation, organizational change, algorithmic management
Field	Engineering
Published In	Volume 4, Issue 6, November-December 2023
Published On	2023-11-12

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American Journal of Advanced Multidisciplinary Innovation and Research

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Middle-Manager Sensemaking During Artificial Intelligence Transformation

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