Search results

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.

This chapter aims to present the idea of leader robots (LRs) and proposes how LRs can step in to fill various manager positions. LRs are the robots that lead robots. Robotics engineers have already produced many robots for various applications such as in manufacturing, marketing, accounting even human resources in business management. We have witnessed many significant advancements in robotics and artificial intelligence research to achieve digital transformation. There are robots for various purposes of providing digital transformation toward Industry 5.0. Some post-modern organizations have already had robot employees and artificial intelligence applications. There are many studies on human–robot collaboration, robot development, and robot employee but limited research on LR. In this study, therefore, it is discussed the possibility of LRs that may be a key concept of Industry 5.0 in perspective digital transformation. Therefore, it is believed that this study will be one of the focuses of many upcoming organizational robotics research studies.

Purpose: An examination of the uptake and application of Robotic, Artificial Intelligence and Service Automation (RAISA) technologies by the events industry.

Design/methodology/approach: Academic and practitioner literature review and analysis pertaining to the relevance of RAISA in events.

Findings: The events industry has tended to rely on automation in staging and event production and the application of RAISA in events has been limited but holds great potential for the future. Whereas, in the hospitality and tourism industries RAISA has been applied across a range of service functions. For example, in such industries, artificial intelligence, machine learning and service robotics technologies have become commonplace. Nonetheless, the same level of adoption of RAISA in events is less evident particularly in front-of-house operation, due largely to the incompatibility with the raison d'être of event attendance – the purposive congregation of people seeking an event experience.

Research limitations/implications: The findings are the views of the authors and are therefore reliant upon existing events management literature on RAISA and their interpretation of this information and its application to the events industry.

Practical implications: RAISA has the capacity to play a crucial technical function in the events industry. However, it needs to be acknowledged that an event is essentially an experiential product which is simultaneously delivered and consumed in a particular setting/venue. RAISA applications and techniques avail event management immense sustainability and growth potential.

Social implications: Events are expressions of human social interactions and activities. Given the recent trend in sports media consumption as a substitute for live event attendance compounded by barriers to event attendance such as heightened terrorism threat and high expense/cost, there is a real risk of degradation of the social significance of the events industry. The prudent uptake of RAISA has the potential to emolliate the barriers to attendance while facilitating effective marketing and industry sustainability.

Originality/value: This chapter provides a new perspective in focusing on the potential applicability of RAISA in event management practice.