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Purpose: This chapter aims to analyse and highlight the current landscape of performance management (PM) systems, and the benefits of integrating modern technology such as smart analytics (SA) and artificial intelligence (AI) into PM systems. The chapter discusses the application of AI in PM tasks which successively simplify many offline PM tasks.

Methodology: To carry out this analysis, a systematic literature review was performed. The review covers literature detailing PM components as well as research concerned with the integration of SA and AI into PM systems.

Findings: This study uncovers the merits of using SA and AI in PM. SA technology provides organisations with a clear direction for improvement, rather than simply state failure in performance. AI can be used to automate redundant tasks while retaining the human element of decision-making. AI also helps reduce the time required to take action on feedback.

Significance: The findings of this research provide insights into the use of SA and AI to make PM tasks fast, scalable, and error-free.

Advances in Artificial Intelligence (AI) technologies and Autonomous Unmanned Vehicles are shaping our daily lives, society, and will continue to transform how we will fight future wars. Advances in AI technologies have fueled an explosion of interest in the military and political domain. As AI technologies evolve, there will be increased reliance on these systems to maintain global security. For the individual and society, AI presents challenges related to surveillance, personal freedom, and privacy. For the military, we will need to exploit advances in AI technologies to support the warfighter and ensure global security. The integration of AI technologies in the battlespace presents advantages, costs, and risks in the future battlespace. This chapter will examine the issues related to advances in AI technologies, as we examine the benefits, costs, and risks associated with integrating AI and autonomous systems in society and in the future battlespace.

Introduction: Artificial intelligence (AI) has now become an integral part of every aspect of the corporate sector. AI may be a massive branch of computing connected to building devices smart enough and capable of performing tasks that usually require human intelligence. Integrating AI with human resources (HR) practices will improve organisations, as these applications can analyse, predict, and diagnose to support HR teams for taking better decisions.

Purpose: This chapter throws light upon the current scenario of awareness of AI and machine learning (ML) and their impact on the industry of HR. This chapter tries to describe the usage of AI in our current world and the impact of AI in the field of HRM in organisations.

Methodology: The true possibility of AI and ML in HRM has been analysed with the help of pie charts, bar charts, and histograms with the segmenting of results and interpretations. Various frequently asked questions have been answered, and a sample population has also been surveyed on their viewpoints regarding specific areas.

Findings: This chapter concludes that HR experts see the best potential in analytics, attendance, recruitment, attendance management, and compensation/payroll. AI will significantly diversify the HR sector. HR professionals need to think outside of their function.

Purpose: In recent times, ‘artificial intelligence (AI)’ has been pervasive even in organisations or at home. AI is defined as programming computers or other technological devices to act, react, respond, or assist the same way humans do. AI has undeniably made people’s lives easier. In organisations, the impact of AI is even more visible. The main aim of this chapter is to examine the significant role of future work skill’s (FWS) each component in the field of on-growing automation. The focus will be especially on emotional and social intelligence (ESI) (a key component of FWS) while adopting AI.

Need of the Study: In terms of human resource management (HRM), AI is useful for people management, payroll services, staff monitoring and improving the recruiting network, among other things. Even managers put their organisation’s job openings on the web and get applicant resumes electronically. People and employees in the organisation have become more advanced and innovative due to AI. A device obtains employee attendance, and human resource (HR) can track their employees and their organisation’s workforce data. HR has now been awarded more authority to manage and fix their employee’s problems because of AI. In a rapidly changing world, AI is affecting all aspects. AI is yearning to automate all of the jobs.

Methodology: Now a question arises how we can stay relevant in AI economic development? As humans, we learned that every issue is a problem of optimisation because we simply require human skills to develop, create and innovate new things. Therefore, researchers recognised that adopting sustainable growth skills encourages people to continue learning throughout their lives. Moreover, AI has enabled machines the ability to learn over time. Still, they will never be able to develop new ideas like human intelligence. A machine can use only one fixed data algorithm. Now humans have made significant progress in various fields with the help of FWS; without integrated computer sciences, brain science would not make such an outstanding achievement. On the other hand, human minds are masters of their intelligence, such as creativity, complex problem-solving, cognitive thinking, ESI and communication. Breakthrough human mind are masters of algorithms represented people have to understand new trends of technology around us, and the best way to move forward is to be aware, adapt and update skills.

Practical Implications: However, AI is required because, regardless of technological advancements, AI is leading Industry 4.0. The industry’s transformation is in 4.0, and hopefully, 5.0 will jump on board soon. Undoubtedly, AI should streamline the process and eliminate redundancy or administrative tasks.

Finding: AI can be more effective in organisations if they incorporate other FWS, particularly the soft human ESI skills, whereas AI is present everywhere, we can still not neglect FWS, especially ESI. So, this chapter highlights the important role of soft skills, that is, ESI and FWS, while adapting AI for an effective HRM.

Industry 5.0 focuses on human–machine collaboration and sustainability with collaborative robots (CoBots) and artificial intelligence (AI) penetration via different technology-enabled devices. Such devices have gained increasing interest to facilitate more efficient, effective, and budget-friendly outcomes in major sectors, including healthcare. The healthcare sector has been evolving at an increasing speed across the globe. In this context, challenges and opportunities have arisen in terms of improving patient outcomes and improving the efficiency of healthcare practitioners’ work. Hence, the adoption of CoBots and AI-enabled devices in this sector is now crucial, and they have been implemented in several domains in healthcare, including diagnosis, medication development, and treatment. However, the successful implementation depends on the users’ attitudes toward the adoption. While extant studies have shown that such devices have significant practical advantages from the patients’ perspective, little is known about healthcare practitioners’ willingness to adopt tech-enabled devices. Thus, this study focuses on the adoption of CoBots and AI-enabled devices in the healthcare sector by examining Turkish medical doctors’ attitudes toward adopting them in their daily operations. The study supplements current literature on Industry 5.0 in healthcare, sheds light on real-life practices, and proposes future directions.

This chapter critically examines whether it may be possible to create an AI-based authentic leader, questioning the inherent contradiction between artificial and authentic. The authors pose central research questions: Does the application of AI – even just as a powerful resource – challenge the tenets of authentic leadership? What are the possibilities and limitations of the concept of authenticity in AI-based management systems? Moreover, with the help of three vignettes illustrating practical applications of AI-based systems in leadership and management tasks, the authors illustrate how technology may be used to either control or empower workers and leaders. The authors call for research to assess whether the search for authenticity in AI-based leadership could lead anywhere, warning that it could entrap us in unresolvable existential and conceptual ambiguity, ultimately diverting our focus from the essence of leadership altogether.

In this chapter, a general introduction on artificial intelligence (AI) is given as well as an overview of the advances of AI in different engineering disciplines, including its effectiveness in driving the United Nations Sustainable Development Goals (UN SDGs). This chapter begins with some fundamental definitions and concepts on AI and machine learning (ML) followed by a classification of the different categories of ML algorithms. After that, a general overview of the impact which different engineering disciplines such as Civil, Chemical, Mechanical, Electrical and Telecommunications Engineering have on the UN SDGs is given. The application of AI and ML to enhance the processes in these different engineering disciplines is also briefly explained. This chapter concludes with a brief description of the UN SDGs and how AI can positively impact the attainment of these goals by the target year of 2030.

Introduction: AI technologies are transforming the industrial sectors, and the impact of AI technologies does not leave behind human resource management (HRM). From recruitment to development and payroll, AI has its own impact. In recruitment, selection, training and development, compensation, and remuneration AI play an important role.

Purpose: The main purpose of this chapter is to analyse the challenges in adopting AI in HRM. This chapter investigates the various strategies to overcome challenges encountered by companies while adopting AI in HR practices. Moreover, this chapter also examines the role of AI in HR practices.

Methodology: To achieve the purpose of this chapter, various case studies were analysed and literature studies with emphasis on what types of challenges are face in adopting AI in HR practices. The authors of various newspaper articles, books, published journals, and websites on AI analyse the various newspaper articles, books, published journals, and websites on AI in human resources (HR). Electronic databases are used as the most effective technique to begin a literature search for the current overview, specifically Science Direct, Google Scholar, and Emerald. In addition to this, the application of AI in HRM practices used by leading organisations globally is also reviewed. The analysis used four keywords: HRM, AI, challenges and adopting. This research has been conducted by searching scholarly papers and relevant studies using similar keywords.

Findings: The adoption of AI technologies is continuously increasing in HR practices. AI performs various HR functions such as recruitment, selection, training and development practices, scanning resume, etc. Organisations can be benefitted in many ways by adopting AI in HRM practices, such as better employees engagement and relations, accelerating competitive advantages and effectively utilising HR budgeting. The findings show that many companies like IBM, Deloitte, Amazon, etc., adopt AI in their HR practices.