Staff Competency Assessment and Task Allocation Methods Considering AI Augmentation: A Study Based on the E-CARGO Model

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With the widespread application of AI in workplace scenarios, integrating AI into workflows has become a significant trend. However, most existing studies treat AI as independent agents operating in parallel with humans, assigning tasks in isolation, and failing to fully exploit AI’s impact on human capabilities. This article goes beyond the simplistic division of labor and proposes an AI-augmented collaborative task allocation method, emphasizing AI’s role in supporting human performance. By systematically modeling factors, including individual differences, interpersonal conflicts, technical constraints, and AI’s dynamic impact on human capabilities, we establish a multidimensional AI-augmented capability model to quantify capability impacts. Fuzzy interval numbers and cloud models are employed to address measurement instability and the heterogeneity of individual capabilities. Real-world case studies and numerical experiments validate the method’s effectiveness in scenarios that reflect realistic office characteristics and scales. Furthermore, experimental analyses identify transition patterns in AI-augmented environments, and verify the method’s adaptability to different AI development stages and diverse business contexts. The results provide a new theoretical perspective for understanding organizational resource reallocation driven by emerging technologies.