1st World Congress 2024 Detroit

AI-Driven Workforce Automation: Redefining Skillsets and Operational Dynamics in the Future of Work

Sergio Mastrogiovanni
Publisher: IEOM Society International
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Abstract

Artificial intelligence-based technology and automation are new approaches toward changing the employment scenery. AI has triggered a transformative change in the global workforce today, whereby the essential sets of skills needed have been fundamentally altered and have changed operation dynamics within organizations. This research delves into the operational dynamics of organizations undergoing AI transformation. Furthermore, the paper analyzes the multifaceted consequential influence of AI-driven automation on job structuring and essential competencies for future labor supplies, discussing how policy frameworks can reduce disruption within the workforce, where governments, education, and private enterprise all have a role to play in collaborative support for an enabling environment that embraces AI, through a model of dynamic skillsets that features flexibility, adaptability, and interdisciplinary knowledge as core competencies of future workforces. Based on the industrial trends and case studies, this multidisciplinary research addresses the issues of the way AI technologies automate routine work, releasing human resources to employ advanced cognition, creativity, and emotional intelligence. While public-private partnerships and investing in reskilling programs are ways to ensure a future of equitable and sustainable work, proactive remedies are the advocacy of the paper. AI-driven automation is a technological development and a lever for profound organizational and social change. Only by embracing the newly defined skill profiles and operational relations can stakeholders ensure AI serves to spur innovation, productivity, and inclusive economic growth.

Published in: 1st World Congress 2024 Detroit, Detroit, United States

Publisher: IEOM Society International
Date of Conference: October 9-11, 2024

ISBN: 979-8-3507-1729-7
ISSN/E-ISSN: 2169-8767