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Introduction: The rapid growth of high technology has urged many organisations to dynamically look for innovative ways, ideas, testing, and ingenious solution in improving their current product, process, system, and technology. For contemporary business, artificial intelligence (AI)-based people analytics is an instrument currently employed to develop a better prosperous future.

Purpose: The study aims to investigate the usage of AI in human resource management (HRM) practices. It also examines the benefits and challenges of using AI in implementing people analytics in organisations.

Methodology: This chapter contains a systemic review of articles and papers on analytics. The presented qualitative study did a literature review based on the articles published in the last five years and extracted from the Scopus database.

Findings: This chapter indicated that AI-based people analytics is on the verge of changing various aspects of HRM practices better to furnish it for a vibrant, ever-changing workplace. It concludes different usage of AI in people analytics for better managing human resources (HR) at the workplace. Also explored the benefits and challenges of implementing AI in the people analytics domain.

Implications: This chapter will help understand ongoing practices of AI-enabled process benefits and challenges. This insight will help develop a better AI-enabled function for a better decision-making system. The future scope of the study is how to overcome the challenges.

Human resource analytics (HRA) is a practice that is emerging within the human resources function. This chapter aims to provide an overview of the knowledge that currently exists about HRA and to identify facilitators and restraints of using HRA. Based on both emerging trends in the literature and in-depth interviews with key practitioners in the field, the authors deduce recommendations for organisations to effectively employ HRA. The analysis shows that HRA is ready to add value towards organisational effectiveness, although barriers exist in realising its potential for the same. Facilitators of HRA include the right set of competencies, with relationship building identified as especially important.

Purpose: This study aims to review the existing literature on human resource management (HRM) as a major theme and sub-theme of human resource (HR) analytics. This chapter has the objective of analysing the trends in HR analytics.

Design/Methodology/Approach: It covered the publications between 2010 and 2021. There was a total of 500 articles sourced through ProQuest. The systematic literature review is applied as a research methodology. The metadata analysis was carried out to understand the trends, challenges, best practices, and scope of HR analytics. The authors have taken the help of keywords, journals, authors, domains, and topics for sourcing, screening, shortlisting, and finalising the article for review purposes.

Findings: It was found that research published in the early period concentrated on HRM’s theoretical and conceptual frameworks. In the middle phase, HR analytics gained momentum while the recent publications have reiterated on adopting various tools and technologies for optimum utilisation of resources and sustainable organisational development.

Practical Implications: This research will add to the literature in the HR analytics domain. It also provides a deeper insight to the researcher to explore and analyse more trends in the area that needs further exploration.

Originality: This study is relevant and comprehensive within the context of digitalisation, people analytics and competition management. It provides valuable insights to analyse the present scenario of the volatility, uncertainty, complexity, and ambiguity environment exploring new horizons for organisational efficiency and human capital.

Introduction: Traditional recruitment system relied heavily on the applicants’ curriculum vitae (CV). This system, besides becoming redundant, has proved to be a futile exercise leading to the hiring of candidates that eventually turn out to be ‘misfits’. CVs were the only source of candidates’ data available for the recruiters a few years back. Face-to-face interviews was considered to be the ultimate solution for hiring suitable candidates. However, evidence suggests that interview scores and job performances do not complement each other. Advancement in artificial intelligence (AI) has introduced several techniques in the recruitment process.

Purpose: This chapter underscores the drawbacks of the traditional recruitment process. Evidence suggests that the traditional recruitment process is prone to subjectivity and is time-consuming. Surprisingly, despite the disadvantages, the integration of AI into the recruitment process is still slow. This chapter highlights the need to harness AI and the advantage technology could bring to the recruitment process. Some of the techniques that are garnering attention and widely used by organisations, such as chatbots, gamification, virtual employment interviews, and resume screening are described to enable the readers to understand with less effort. Chatbots and gamification techniques are described through process flow charts. We also describe the various types of interviews that could be conducted through virtual platforms and the modality by which the resume screening technique operates. Today, we are at a juncture wherein it is pertinent to acknowledge the superiority of technology-driven processes over traditional ones. This chapter will help the readers to understand the modus operandi to implement chatbots, gamification, virtual interviews and online resume screening techniques besides their advantages.

Scope: Although chatbots, resume screening, virtual interviews, and gamification are used in other areas, too, such as training and development, marketing, etc., in this chapter, we restrict solely to employee recruitment processes.

Methodology: Scoping review is used to examine the existing literature from various databases such as Google Scholar, IEEE, Proquest, Emerald, Elsevier, and JSTOR databases are used for extracting relevant articles.

Findings: Automation and analytics in recruitment and selection remove bias which is otherwise increasingly found in manual hiring processes. Also, previous studies have observed that candidates engage in impression management tactics in traditional face-to-face interviews. However, through automated recruitment processes, the influence of these tactics can be eliminated. AI-based virtual interviews reduce human bias. It also helps recruiters to hire talents across the globe. Gamification improves the candidate’s perception of the work and work environments. Through gamified techniques, the recruiters can understand whether a candidate possesses the required job skills. Chatbots are an interactive technique that can respond to interviewees’ queries. Resume screening techniques can save the recruiter’s time by screening and selecting the most appropriate candidates from a large pool. Hence, the chosen candidates alone can be referred to the next stage of the recruitment cycle. AI improves the efficiency of the recruitment process. It reduces mundane tasks. It saves time for the human resources (HR) team.

This chapter critically evaluates opportunities and challenges associated with developing diversity and embracing inclusion of cyber security talent in a multinational consultancy firm and offers recommendations on how to optimize inclusion of young talent in this sensitive business area within a multinational company. Drawing on one of the author's experience as a young cyber security professional with a non-technical background, entering the profession through a consultancy graduate development programme, this paper offers a unique perspective on how to enhance cohesion in diversity across linear and non-linear routes into cyber security.

While the scope is limited to cyber security talent in early careers, the competency-based approach means that recommendations around developing diversity and embracing inclusion can be applied to young talent in other business competence areas. Each recommendation can be used as a building block to influence and shape future equality, diversity and inclusion (ED&I) strategy in consultancy.

Background: Human resource management (HRM) is the tactical method for a business enterprise’s optimistic and systemic administration. This study aims to identify the common and major triggering attributes and the knowledge gap between HRM and an organisation’s employee attrition rate.

Method: The employee Attrition Case Study Dataset used is an anecdotal data set that tries to figure out relevant variables that determine employee behavioural aspects towards attrition. This study investigates why attrition occurs, the major triggering attributes for employee turnover, and how it might be anticipated to employ artificial intelligence (AI) to avert corporate losses.

Results: Employees’ monthly income, age, average monthly hours, distance from home, total working years, years at the company, per cent of salary hike, number of companies worked, stock options level, job role and other factors are taken into consideration. A feature importance extraction framework was devised to investigate the various dormant factors. The findings also show feasible hypotheses that help enhance employee engagement, reinvent the worker dynamic, and higher levels of risk decrease attrition rate.

Implications: Employees’ monthly income, age, average monthly hours, distance from home, etc., are all major variables in employee attrition in the Indian IT business. This research adds to the theory development of behavioural elements in people analytics based on AI.

Purpose: Can we predict employee attrition through employee behavioural patterns advancement using AI tools.