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This chapter begins by exploring the critical tenets of strategic human resource management (SHRM) and then discusses what the study and practice of SHRM needs to do in a new era of sharing economy and artificial intelligence (AI) for delivering successful business and individual employee performance in a new world of technological disruptions in work and employment. Using examples from popular platforms such as Airbnb, Uber, Ola, Zomato and Swiggy in India, to name a few, this chapter illustrates the changing ways of how non-standard employees are managed in the Fourth Industrial Revolution (4IR) through the use of technology platforms and apps, including the specific use of AI, in implementing a number of these changes. We highlight the need for new skills and knowledge by HR professionals to successfully engage in the new and brave world of AI-based technological disruption that we are all facing.

Introduction: The application of artificial intelligence (AI) can substantially enhance both short- and long-term decision-making in human resource management (HRM) practices. However, academic research fails to address the dark side of AI in confluence with HRM and primarily paints a bright picture of the advantages of AI.

Purpose: The current research emphasises the challenges faced in the HRM domain in applying AI in HRM practices and further discusses the future path to maximise the effect of AI on HRM.

Methodology: The study rigorously surveyed secondary sources like the journal papers, consultant reports and other databases to critically examine the challenges encountered in applying AI in HRM practices.

Findings: Analysis of the above-mentioned sources shows that AI algorithm might bring routinisation of work. HRM ethics, data safety and integrity, biased algorithm from the programmer, fewer data to train the AI model, lack of technical skills of HR executive, neglecting values, and ignoring the creative thinking by employees are a few aspects that might cause difficulty in the adaptation of AI in the HRM domain. As a consequence, there could be unnecessary extra monitoring of employee behaviour, which in turn could lead to loss of workplace well-being and trimming of the human element in HRM.

Practical Implications: This study adds value by focusing on the challenges and suggests the path for robust HRM practices; because, the biased decision-making by AI could potentially lead to improper decision-making by the top management, and in turn, the sustainability of a firm could be at stake.

Introduction: Artificial intelligence (AI) is being extensively used to solve complex problems in the industry. AI provides several benefits such as providing visibility in the processes, reducing time, improving accuracy, saving time, helping in the decision-making process, etc. Due to the range of benefits of AI technologies, organisations readily adopt this technology. However, there are several challenges that the organisation faces during the implementation of AI. These challenges are in context to human resource (HR) development for successful implementation of AI across different functions and are discussed in this chapter.

Purpose: Although we know that AI technology is widely accepted in human resource management (HRM) due to its various benefits. But the organisations face many challenges during the implementation of AI. The focus of the study is to explore the literature on AI in HRM, identify the challenges of implementing AI and provide potential future research direction based on a systematic literature review.

Methodology: To explore the literature on AI in HRM, the study undertakes a systematic literature review. The study identifies, analyse and classifies the literature to provide a holistic view of HR challenges in implementing AI. The study is built on a review of 47 documents, including the articles, book chapters and conference papers using the Scopus database for the past 10 years (2012–27 January 2022).

Findings: The study provides an overview of the documents published in Scopus in this area through a systematic literature review. The study reveals that a significant amount of growth in the publication has been shown in the past 10 years. The maximum and continuous growth is shown after 2017. The maximum number of papers are published in India, the USA and China. The study identifies major eight challenges of AI implementation in HRM. The study also provides a secondary case to deep dive in this area based on a systematic literature review.

Research Limitation/Implication: The challenges identified in the study are not empirically tested. Each of the identified challenges should be empirically examined. This study has expanded the body of knowledge of AI in HRM. This study will help the academicians and practitioners work on the identified challenges and help the organisations ease in adopting AI.

Originality/Value: This study represents the first work that integrates AI implementation challenges in HRM.

Introduction: Human resource management (HRM) is going through a transformation phase due to the pandemic. The COVID-19 crisis compelled the employees to work virtually. To mitigate the effects of COVID-19, several organisations heavily invested in artificial intelligence (AI) in the realm of HRM.

Purpose: With limited studies on the paradigm shift in HRM post-pandemic and the role of AI, the study investigates and proposes a conceptual framework for the paradigm shift in HRM practices post-COVID-19 pandemic and the significance of AI. Furthermore, the study investigates the outcomes of the use of AI in HRM for organisations and employees.

Methodology: A comprehensive review of the literature based on the guidelines of Tranfield, Denyer, and Smart (2003) and Crossan and Apaydin (2010) has been followed. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes involved.

Findings: COVID-19-related economic disruption has led to a paradigm shift in HRM practices. AI-enabled HRM practices are now centred around remote and contingent workforce management, mindfulness, social capital, increasing employee engagement, reskilling and upskilling towards new competencies, etc. AI is making remote work seamless through smooth recruitment and selection process, onboarding, career and development, tracking and managing the performance, facilitating learning, and talent management. Post-pandemic, AI-powered tools based on data mining (DM), predictive analytics, big data analytics, natural language processing (NLP), intelligent robots, machine learning (ML), virtual (VR)/augmented reality (AR), etc., have paved the way for managing the HRM practices effectively, thereby leading to enhanced organisational performance, employee well-being, automation, and reduced cost.

Introduction: In a world characterised by volatility, uncertainty, complexity, and ambiguity, change is the only constant. Over the years, human resource management (HRM) has evolved from conventional functions of hiring and firing to being a strategic partner in organisations. Similarly, there has been a paradigm shift in the landscape of artificial intelligence (AI) from being a mere searching tool to the design and development of intelligent robots. Over the years, AI has emerged into a collection of powerful technologies re-inventing different functional areas, including HRM. The application of AI in HRM is perceived as an optimistic opportunity since it ought to bring maximum value at minimum cost. AI focuses on building tools that exhibit human-level intelligence and discernment in making decisions.

Purpose: The purpose of this chapter is to draw deeper insights into the relevance of AI in different functional areas of HRM. Integrating AI into HRM functions such as talent acquisition, training and development, performance management, employee engagement, and the like can help leverage efficiency and create an engaging employee experience. In the wake of Industry 4.0, where digitalisation has become imperative, this chapter explores the integration of AI into specific HR functions for a synergistic competitive advantage in companies. The purpose of this chapter is to signify the integration of AI into four vital functions of HRM, namely talent acquisition, training and development, performance management, and employee engagement. The objective is to chart how companies integrate various AI tools in four specific HRM functions to enhance efficiency. Also, the companies willing to implement AI in their HR functions can refer to the case studies used as exemplars in the chapter.

Methodology: This conceptual chapter is based on the secondary sources, which also build upon case studies of different companies that have implemented AI-enabled solutions and integrated them into different HRM functions and processes per needs. This chapter utilises the conceptual framework of both AI and HRM functions to give deeper insight into the challenges and implementation of technology-enabled solutions.

Findings: AI is used in HRM functions to automate repetitive and operational tasks to shift the focus to more strategic aspects. Despite many advantages of AI and machine learning, very few companies are using it, and companies may integrate technology-enabled solutions based on the size and nature of business.

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.

Digital technology has had a massive impact on business management procedures, driving modifications. The goal of this study is to figure out how human resource management (HRM) in businesses should change as digital technology develops. Via social, mobile, analytic and cloud technology, businesses can monitor and ensure that preconceptions and assumptions inside the organization drive acceptable behaviour. The outcomes of this study reveal that information transformation has influenced jobs growth, labour supply and the composition of corporate talent. Management and control in management of human resources (HR) should give way to empowerment. The goal of the current review is to examine current technological trends in HRM-related enterprises. Peer-reviewed articles from databases like EBSCO, Emerald and Sage publications provide the foundation of the review. Since there are few academic articles on the subject of the study, which covers technology in HRM and cloud in HRM, references from organizational reports have also been incorporated. Utilizing digital technologies in HR will increase organizational performance through decisions involving talent, forecasting workforce requirements and maximizing talent through planning and development. Informed decision-making will also allow HR to assist an organization in achieving corporate objectives. It also makes it possible to manage staff members through hiring, training, job happiness, productivity and task assignment based on qualifications. Additionally, it aids in determining the cause of attrition and locating high-value individuals who are departing.