Top Occupations Based on a Strategic Taxonomy Framework of Future Skills for Workforce Development

The rapid evolution of Industry 4.0 and 5.0 requires a dynamic and predictive approach to workforce development, particularly identifying emerging occupations and the required knowledge, skills, and abilities (KSA) within critical sectors. This study addresses the research question: What are the key emerging Industry 4.0 and 5.0 occupations in Mexico's INFOCOMM (Information and Communications Technology) sector, and what KSAs do they demand? The study proposes a strategic framework that leverages AI-powered tools, specifically natural language processing (NLP) and machine learning (ML), to develop a comprehensive and adaptive taxonomy of KSAs that meets the evolving needs of the INFOCOMM sector. The methodology involves collecting and analyzing extensive datasets from job advertisements, industry reports, and educational frameworks, allowing us to automate the extraction and classification of job requirements and competencies. Using NLP and ML, it was possible to systematically categorize and rank in-demand skills, providing a scalable model that can be updated dynamically as labor market trends emerge. The framework identifies key occupations driven by advances in automation, data analytics, AI integration, and digital platform management, with the most critical skills, including problem-solving, project management, and interdisciplinary teamwork. Similarly, significant knowledge areas such as cloud computing, cybersecurity, and AI systems are identified as foundational for the future workforce. This flexible framework offers a robust tool for academic institutions and industry practitioners. For academia, it enables the alignment of curricula with the competencies necessary for Industry 4.0 and 5.0. For industry, it serves as a strategic guide for workforce development, supporting targeted upskilling and reskilling initiatives. The model's flexibility allows for continuous updates, which ensures relevance despite ongoing technological advances. Future research will extend this framework to additional sectors and regions, refine its predictive capabilities, and explore its longterm impact on education and workforce productivity.