Unveiling the Impact of AI Utilization, Tools, and Training on Work Ef-ficiency: A Mediation Analysis of Task Typologies

Abstract

Artificial Intelligence (AI) continues to be a transformative force in workplaces, not only reshaping traditional workflows but also enhancing operational efficiency. The frequency of AI usage, the complexity of AI tools, and the amount of employee training on these tools play a significant role in work effectiveness outcomes. However, its ramifications on work efficiency are not straightforward; rather, it is mediated by the nature of tasks performed, such as routine, analytical, or creative. The aim of this study is to probe how routine, analytical and creative task types mediates the application of AI usage, utilization of AI tools, and training in improving work efficiency. This is also intended to provide operational insights for both practitioners and researchers for policy formulation. For the purpose of this study, employs a quantitative cross sectional research design where a total of 233 responses were gathered. Data was collected from the Malaysian workforce residing in Klang Valley aged between 21 to 55 years, who are likely to use AI technology in their daily work routines. Data collection is carried out leveraging on a (5) five-point Likert scale, through a structured online questionnaire, developed using instrument adapted from prior studies. The GLM Mediation Model suggest a significant positive relationship between AI Usage significantly influences Routine Task (β = 0.31674, p < 0.001**), Analytical Task (β = 0.18861, p = 0.023*), and Creative Task (β = 0.23839, p = 0.005**), while AI Training demonstrates even stronger positive effects on Routine Task (β = 0.43833, p < 0.001**), Analytical Task (β = 0.45248, p < 0.001**), and Creative Task (β = 0.35741, p < 0.001).  Additionally, the technology usage scores show a significant difference across technology adoption levels (all p < 0.001**), with the Advanced group consistently reporting higher mean compared to the Low and Moderate groups. In conclusion, a stronger organizational emphasis on AI utilization and training is expected to enhance task performance, leading to improved work efficiency. Thereby allowing businesses to make more effective data-driven decisions, and in building a more capable and versatile workforce.