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This chapter is devoted to sustainable human resource management that leads to sustainable competitiveness. It features the ways human resources can be managed to carry out sustainable goals and the impact of sustainability on employees' attitudes and behaviours. The aim of this study is to explore the complex objectives of sustainability and human resource management and empirically investigate the dynamic relationship between human resources in science and technology and sustainable competitiveness in the case of 35 European countries. Our contribution emphasizes this interrelationship and its causality. For this research, we applied a vector auto-regression (VAR) model, and the Granger causality method to examine the relationship between human resources in science and technology and sustainable competitiveness. A panel data included 314 observations between 2012 and 2021. The panel VAR for analysing the impulse response function was enriched with the 5% and 95%, using Monte Carlo simulations. The research results revealed bidirectional causality in the European countries between human resources in science and technology and sustainable competitiveness. Human resources in science and technology trigger sustainable competitiveness and vice versa. As an element of originality, our study demonstrates that human resources in science and technology contribute to sustainable performance, and, on the other hand, a more competitive and sustainable environment contributes to the development of human resources in science and technology. Thus, the chapter outlines the role of human resources in science and technology with regard to sustainable human resource management (HRM), and how to navigate these objectives so that they can positively influence sustainable competitiveness.

Introduction: Artificial intelligence (AI) has progressed significantly over the past few years, evolving into a collection of innovative tools that provide a competitive advantage to businesses. The acceptance and investment in AI are skyrocketing over the globe. The entry of AI in the workplace automates tasks and impacts making a timely decisions. At the same time, the workforce is not ready to welcome the new technology due to the skill gap. The organisation has to face many challenges in reskilling and convincing the workforce to incorporate AI in their work.

Purpose: With this study, the authors aim to analyse and highlight the introduction of AI in the organisation and the call for the reskilling of the workforce. To figure out what skills are most important for employees to learn to advance their careers.

Methodology: Given the deductive nature of the study, the researchers used secondary data collected and compiled from research papers, publications, websites, HR blogs, survey reports, etc. Research papers from reputed journals, reports of consultancies and agencies have been considered to synthesise the information and present it in a systematic manner and to derive the conclusion.

Findings: The findings indicate AI’s capabilities and applications have grown considerably, which shows the importance of AI in a growing number of fields, yet several hurdles need to be overcome, the most prominent one being the issues concerning upskilling the workforce for the future of AI. This study reveals the change in the perceived importance of the skills in the present and future times. Reskilling and upskilling the workforce and creating new talent to meet the changing employment demands is becoming increasingly important.

New Ways of Working seems to change the leadership agenda. Activity-based working and home-based work lead to different behaviors of employees. Supervising styles will change from command-and-control toward goal-setting-and-trust. This chapter describes the trend and provides new data on the actual use and effectiveness of these new supervision styles. It appears to be a mix of different leadership styles, such as leading by vision, setting targets and control on output, providing trust.