RESEARCH ON THE MULTI-CRITERIA ASSESSMENT MODEL OF PERSONNEL DIGITAL COMPETENCIES USING AI ELEMENTS

Abstract

This study explores the theoretical, methodological, and practical approaches to assessing the
digital competencies of personnel as a critical factor in ensuring the successful digital transformation of modern
enterprises. It is argued that within the evolving digital economy, business transformation follows a hierarchical
structure, incorporating the stages of digitization (basic technical level), digitalization (process-oriented level), and
digital transformation (strategic level of organizational change). Digital transformation is interpreted as a profound
reconfiguration of business operations, in which employees serve as primary drivers of change. The analysis of
existing assessment approaches indicates that reliance solely on self-assessment methods, particularly within the
DigComp framework, is characterized by limited objectivity, as it fails to capture actual patterns of digital behavior
in the workplace. In response to this limitation, an integrated multi-criteria model for assessing digital
competencies (C1) is proposed. This model synthesizes four key components: self-assessment results (S), analysis
of real digital activity (O), indicators of learning engagement (L), and expert evaluation provided by managers (E).
The application of this model enables the identification of “digital gaps” and supports the classification of
personnel according to behavioral types, ranging from users with limited digital skills to digital leaders. Special
emphasis is placed on the role of artificial intelligence (AI) as a driving force in the evolution of economic systems,
contributing to the shift toward data-driven management practices. The study also presents a comparative analysis
of the conceptual models DigComp, ICDL, and IC3, concluding that DigComp operates at a strategic level by
defining “what should be assessed,” whereas ICDL and IC3 function at an operational level, focusing on the
practical verification of digital skills. Furthermore, projections regarding AI development over the next 5–10 years
suggest a trend toward increasing cognitive autonomy of intelligent systems, alongside the transformation of
business platforms into self-regulating management ecosystems. It is demonstrated that the effectiveness of AI
adoption is closely linked to employees’ attitudes toward digital technologies and their overall level of digital
maturity.