This study examines the complex phenomenon of technological resistance, especially the impact of perceived workload (PW) and responsibility denial (RD) in the adoption of artificial intelligence (AI) technologies in organizational settings. The introduction highlights the growing implementation of AI across various sectors and the diverse reactions it provokes among stakeholders. Business leaders and investors often celebrate AI’s potential to boost financial performance. In contrast, employees might believe it threatens their job stability, leading to avoidance behaviors. These behaviors impede progress and innovation, leading to economic inefficiencies due to the underutilization of AI investments. A review of existing literature supports technology avoidance behaviors by identifying various precursors to technological resistance, such as perceived threats, goal misalignment, and the impact of belief systems on the decisions to adopt technology. This study argues that the anticipation of an increased workload and apprehension of being accountable for the outcomes of AI’s actions are significant contributors to resistance behaviors. The paper seeks to dissect the interplay between the use of AI, PW, RD, and aversion to technology. The researchers propose that the obligation to oversee AI systems and accountability for their results can deter employees from assigning tasks to AI, thus increasing PW, and encouraging avoidance. The discussion outlines the theoretical and practical ramifications of the findings of the literature review, including the critical role of PW and perceptions of responsibilities in decreasing technology resistance and enabling the seamless integration of AI into organizational processes. Additionally, the paper underscores the importance of addressing cognitive and emotional barriers to technology adoption to minimize avoidance and maximize the acceptance of AI in the workplace. This research enriches the discourse in Information systems by illuminating the psychological foundations of technology resistance and providing actionable guidance for entities aiming to tackle the hurdles of AI implementation. Empirical research is needed to confirm the suggested Nam’s (2014) model and identify effective measures for alleviating PW and responsibility concerns about AI technology.