The study compares the effectiveness of Artificial Intelligence (AI) and human decision-making influenced by cognitive biases in achieving sustainable skills development. The aim is to explore whether AI-assistance can help to mitigate biases such as ethical dilemmas and privacy issues, along with workforce planning, reduce bias in talent management and support long-term human resource organisational sustainability. Design: The study uses a secondary data-driven analytical approach using insights from the UNESCO report “Understanding the Impact of Artificial Intelligence on Skills Development” published in 2021. This report informs about the variability of skill development using AI. A thematic and comparative analysis is conducted, which involves the following steps: data familiarisation, code development and generating themes in order to evaluate how AI and human judgment influence skill development. The themes include AI-driven skill development, human cognitive bias in decision making, and ethical integrity. They also draw upon the point that decisions related to skill identification, training, recruitment and workforce adaptability are influenced by the biases.
The findings indicate that human decision-making is significantly influenced by cognitive biases, leading to inconsistencies and potentially unsustainable outcomes. In contrast, AI-assisted decision systems and skill development demonstrate higher consistency, objectivity, and alignment with sustainability goals. AI-driven systems highlight data-driven insights in identifying skill gaps and optimising training pathways. However, concerns regarding algorithmic bias, ethical governance, and unequal access to AI-driven skill development opportunities remain significant challenges.
The study contributes to the emerging intersection of AI, cognitive bias, and sustainable HRM practices by integrating skills development into the routine. It offers a perspective grounded in the UNESCO report, which is global evidence. Research Implications: The findings highlight the need for hybrid HRM frameworks that combine AI capabilities with human judgment and ethical oversight. It emphasises the importance of transparent AI systems.
