From Promise to Pressure: An Employee‐Focused, Context‐Aware Framework of <scp>AI</scp> Adoption Balancing Performance and Cognitive Load

Abstract

ABSTRACT As artificial intelligence (AI) becomes increasingly embedded in organizational processes, firms face the challenge of balancing performance gains with employee well‐being. While existing research emphasizes strategic and structural drivers of AI adoption, limited attention has been paid to employees' lived experiences, particularly, the cognitive issues associated with sustained AI use. To address this gap, this study examines how organizations can manage the dual impacts of AI on performance and employee overload. Drawing on the stressor‐strain‐outcome (SSO) model, complemented by the Technology Acceptance Model and Cognitive Load Theory, a qualitative study was conducted with ten expert employees across knowledge‐intensive Moroccan firms. The findings reveal that while AI tools enhance coordination, speed, and strategic decision‐making, they simultaneously introduce technical complexities, cognitive strain, and psychobehavioral burdens. A conceptual framework relying on the SSO model is proposed to show that poorly integrated AI systems act as stressors triggering cognitive overload and undermining performance. These negative outcomes can be mitigated through three best organizational practices as shown in the proposed model. This study contributes to the existing literature by reframing the SSO model as a proactively manageable system offering theoretical and practical guidance for governing AI adoption and fostering a healthy work environment. For practitioners, it provides an actionable roadmap to implement AI responsibly, reduce overload, and align AI tools with employees' cognitive capacities and workflow rhythms to sustain performance.

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