What's Happening?
As populations age, artificial intelligence (AI) is reshaping human resource management, prompting organizations to determine the most effective AI-supported talent management strategies for older workers. This decision-making process is complicated by
conflicting criteria and often qualitative evidence, necessitating a framework to handle uncertainty. A new q-rung orthopair fuzzy LOPCOW-MARCOS decision model has been developed, incorporating a novel hesitancy-adjusted score function. This function exhibits three key properties: boundedness within a unit interval, achievement of extreme values only for fully certain positive and negative assessments, and behavior that does not decrease with membership degree or increase with non-membership degree, reducing to an ordinary normalized score for assessments without hesitancy. Objective criterion weights are derived using the logarithmic percentage change method, and alternatives are ranked by measuring and ordering them according to a compromise solution. The method was tested using a Monte Carlo computational stress test with general random q-rung orthopair fuzzy matrices and a fixed seed.
Why It's Important?
The prioritization of AI-enabled lifelong learning and reskilling for an aging workforce is crucial for maintaining economic competitiveness and social equity in the U.S. and globally. As technological advancements, particularly in AI, continue to transform industries, older workers face the risk of skill obsolescence. By investing in reskilling and upskilling initiatives, businesses can retain valuable institutional knowledge and experience, reducing recruitment costs and mitigating potential labor shortages. This approach also promotes inclusivity by ensuring that older demographics remain active and productive members of the workforce, contributing to economic growth and innovation. Failure to address this could lead to a significant portion of the workforce being left behind, exacerbating social inequalities and potentially straining social welfare systems. The development of robust decision models, like the one described, provides a structured way for organizations to strategically implement talent management strategies that benefit both employees and the broader economy.
What's Next?
The research indicates that AI-enabled lifelong learning and reskilling is the top-ranked strategy for an aging workforce, with a 99.7% probability of being ranked first in Monte Carlo simulations. This suggests that organizations are likely to increasingly focus on integrating AI into their training and development programs for older employees. Future steps will involve the practical application and validation of this model in diverse organizational settings. While the current study did not collect new assessment data or form a new expert panel, subsequent research could involve real-world case studies to refine the model's parameters and confirm its effectiveness. Businesses and policymakers may consider developing frameworks and incentives to encourage the adoption of AI-driven reskilling programs, potentially leading to new educational initiatives and partnerships between industry and academic institutions to develop tailored learning pathways for older workers.
Beyond the Headlines
The emphasis on AI-enabled lifelong learning and reskilling for an aging workforce extends beyond immediate economic benefits, touching upon deeper societal and ethical considerations. This trend highlights a shift towards a more dynamic and continuous learning paradigm, where education is not confined to early career stages but is an ongoing process throughout one's professional life. Ethically, it underscores the responsibility of organizations and governments to ensure that technological progress does not marginalize older workers but instead empowers them to adapt and thrive. Culturally, it challenges traditional perceptions of retirement and age-related productivity, promoting a more inclusive and age-diverse workforce. The integration of AI in this context also raises questions about the design of AI tools to be user-friendly and accessible for all age groups, ensuring that the technology itself does not become a barrier to learning. This development could foster a societal shift towards valuing experience combined with updated skills, creating a more resilient and adaptable labor market.
