Industry 5.0 emphasizes the synergy between humans and technology, where employees’ competencies are critical for the effective implementation of collaborative workplaces. In this context, collaborative robots play a key role in modernizing organizational processes by increasing flexibility, usability, and reprogrammability (Segura et al., 2021; Ojsteršek et al., 2024). Research on human–robot collaboration (HRC), grounded in task analysis and ISO 10218-2 safety guidelines (International Organization for Standardization, 2011), highlights the need to align task requirements with human capabilities and competency profiles. Effective HRC requires not only technical knowledge but also cognitive and psychosocial competencies (Wilson & Salas, 2010; Villani et al., 2018; Salas and Rosen, 2020). Existing models typically distinguish four competency clusters: technical, safety, cognitive, and psychosocial. However, none provides a fully comprehensive framework for collaborative robotics (Werning et al., 2021; Simone et al., 2022; Wang et al., 2023; Potocan et al., 2024). This study addresses the gap by proposing a methodology for predicting competency weights, enabling structured learning approaches and supporting future empirical validation. Safety competencies are treated as embedded within technical, cognitive, and psychosocial dimensions, reflecting their integrative role in ISO-based HRC environments. The article develops an enhanced competency model based on task and job analysis aligned with ISO standards and applies the Analytic Hierarchy Process (AHP) to determine competency weights according to their impact on safety, efficiency, and work quality. The model integrates technical, cognitive, and psychosocial competencies, as well as ethical aspects, and emphasizes continuous professional development. To identify competencies, we combined a literature review with the Delphi method, collecting expert opinions through iterative rounds. This resulted in a comprehensive set of existing and additional competencies enabling safe and adaptive performance in collaborative environments. Employees were categorized into three groups: experts (Group A), students (Group B), and laypersons (Group C), reflecting different levels of prior knowledge and experience. Using ISO-based analysis, base competency weights were derived as follows: technical (0.30), cognitive (0.40), and psychosocial (0.30) (Romero et al., 2016; 2022). The model further combines AHP with TOPSIS, a robust multi-criteria decision-making approach (Opricovic & Tzeng, 2004). The main contribution lies in the methodological and normative development of a competency model for Industry 5.0 collaborative environments. The proposed framework establishes a foundation for empirical validation and practical application in personnel selection, training design, and task adaptation across different user groups.