The Future Competencies & Skills Development Cycle: A New Workforce Model in the Context of Digital Transformation and Artificial Intelligence

Purpose – This paper addresses the urgent need to adapt employee development practices to the requirements of digital transformation and artificial intelligence (AI). While existing literature highlights the importance of future skills, it lacks a systematic, lifecycle-based approach for strategically designing employee development across all stages of the employee journey. Aim(s) – The aim of this paper is to evaluate how employee development can be designed as an integrated, lifecycle-based process to build sustainable competencies in times of digital transformation and AI. The analysis examines how lifecycle-oriented approaches support the development of future-oriented competencies such as digital literacy, adaptability, and complex problem-solving through continuous and strategically embedded learning. Design/methodology/approach – This study applies a structured theoretical review complemented by the bibliometric analysis workflow (seven-step science mapping approach) to ensure methodological rigour. The analysis synthesises literature from 2018–2025 on future skills, learning and development strategies, and AI in HR, prioritising Web of Science– and Scopus-indexed journal articles and systematic reviews, complemented by selected practitioner studies and reports for contextual relevance. Findings – The paper identifies digital literacy, adaptability, complex problem- solving, and AI-enabled decision-making as key competencies. It introduces the Future Competencies & Skills Development Cycle, an original conceptual model developed by the author of this paper, which combines strategic learning frameworks, continuous reskilling, and competency-based development. By embedding development across the employee lifecycle, the model ensures an integrated approach. Limitations of the study – As a primarily conceptual and literature-based study, the paper includes only preliminary qualitative insights and lacks empirical validation or pilot testing. Future research should test the effectiveness of the proposed model in diverse organisational contexts and examine ethical implications such as data protection, algorithmic bias, and transparency in AI- driven HR analytics. Originality/value – This paper synthesises existing frameworks and extends them with the lifecycle-based Future Competencies & Skills Development Cycle. It offers strategic guidance for HR professionals, organisational leaders, and researchers on embedding future-oriented skills into employee lifecycles under digital transformation and AI, ensuring methodological transparency and providing a foundation for future empirical validation.