| Sumario: | Uneven distribution of workload, unequal task sharing, and inefficient resource utilization in information technology (IT) departments can negatively impact operational performance. Traditional and manual methods are unable to respond quickly and effectively to dynamic workload changes.This study aims to examine the leverage effect of artificial intelligence in workload balancing in IT departments and to evaluate its contributions to operational efficiency and resource utilization. It also aims to reveal the impacts of AI applications not only on technical optimization but also on human and organizational dimensions.The research was conducted using a qualitative approach, and data were collected through a systematic literature review and document analysis. Academic articles, technical reports, and corporate documents were examined; the roles of artificial intelligence in workload balancing were determined using thematic analysis. The analysis revealed the following themes: workload estimation, task prioritization, dynamic resource allocation, automation of routine tasks, performance and cost optimization, human-machine interaction, and organizational adaptation.The results show that artificial intelligence algorithms reduce workload imbalance, shorten processing times, and minimize error rates by prioritizing tasks and dynamically allocating resources. Furthermore, it has been determined that operational efficiency is increased through performance monitoring and cost optimization. However, human-machine interaction, employee engagement, and organizational adaptation levels directly affect system effectiveness. While technical efficiency gains are widely discussed in the literature, the effects on employee satisfaction and motivation have been examined to a limited extent.Artificial intelligence is a powerful tool for balancing workload, increasing operational efficiency, and optimizing resource utilization in IT departments. However, effective and sustainable implementation requires a holistic approach that considers not only technological performance but also human factors, organizational adaptation, and strategic management processes. This study systematically frames AI-based workload management from a socio-technical perspective, contributing to academic literature and offering a guiding framework for practitioners.
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