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Purpose: This chapter aims to unveil the challenges of adopting and using banking chatbots in India and identify the challenges of Chat Generative Pre-trained Transformer (ChatGPT) for future banking.

Need for the study: Unveiling the challenges of chatbots and ChatGPT in the banking industry in India is crucial to understand the limitations and areas of improvement to enhance customer experience, ensure data security, and maintain regulatory compliance.

Methodology: The researchers conducted a narrative review systematically summarising and analysing existing literature on chatbots and ChatGPT, providing a comprehensive overview of the challenges faced in the industry.

Findings: The authors identify perceived risk, platform quality, connectivity and infrastructure, data privacy and security, user education and acceptance, existing legacy systems, and regulatory guidelines as the challenges of adopting chatbots. Additionally, the findings reveal that the challenges posed by ChatGPT in future banking include the potential reduction of traditional banking jobs, linguistic diversity, data privacy and security, ethical considerations and bias mitigation, explainability and accountability, integration with existing banking systems, and user trust and acceptance. However, implementing these new technologies also presents opportunities for individuals with unique human skills, such as critical thinking, empathy, and creativity, which are difficult to replace with technology.

Practical implications: By minimising the challenges of ChatGPT and chatbots, the banking industry could achieve improved customer service, cost efficiency, automation of routine tasks, and 24/7 availability, leading to enhanced customer satisfaction and operational efficiency in the banking industry. Additionally, these artificial intelligence (AI) tools enable data-driven insights, personalised experiences, scalability, and efficient handling of large customer volumes, contributing to better decision-making and enhanced customer engagement.

As the global community increasingly directs its attention towards sustainable urban development, integrating artificial intelligence (AI) into circular economy (CE) management within smart cities has become a potent strategy. This study aims to examine the potential influence of AI-based technologies on optimizing resources and minimizing waste, which constitute critical components of the principles underpinning the CE. The focus is mainly on applying these technologies within smart city environments. Artificial Intelligence can significantly enhance the processes of gathering, analyzing and decision-making by integrating internet of things (IoT) sensors, data analytics, machine learning algorithms and predictive analytics. This chapter explores the potential of AI in predicting trends, optimizing circular supply chains, improving waste management and recycling practices, facilitating sustainable product design, fostering citizen engagement and aiding policy development. The current research presents a comprehensive examination of the interrelated connection between the principles of CE and the advanced technology of AI. Doing so contributes significantly to our holistic comprehension of how these advancements might collectively influence the development of a more sustainable and resilient future for urban populations.

Artificial intelligence and augmented reality are two key tools gaining importance in the digital era due to their wide range of applications in different fields and sectors. Industry 4.0 lays emphasis principally on the technology used to help the business remain competitive and sustainable. Sustainable development goals are another important objective of the UN which has laid responsibility for every business to support addressing the global challenges. Purpose: This chapter essentially aims to present the standpoint of artificial intelligence and augmented reality in meeting the sustainability perspective of organizations. Information about the study is gathered through secondary approaches, critically reviewing published literature, scientific reports, and statistical data accessible through business reports, and corporate websites. Further analyzed to present the perspectives of the authors in the study. Globally artificial intelligence market size is predicted to reach $190 billion by 2025, while the funding for startups doubled during the period 2011–2020 globally. The investment in artificial intelligence is going to reach $500 by 2024 resulting in substantial revenue returns. The augmented reality market size could reach $97 billion by 2028. Artificial intelligence today is increasingly used in many fields and is attracting multiple applications in many sectors such as manufacturing, retail, education, IT, and health care and has also contributed to sustainable development the same time by providing energy conservation options, optimization, and reduction of resources, minimizing wastage, offering timely assistance on maintenance schedules, practices which are enabling organizations to reach closer to sustainability and transformation.

Banking traces back to 2000 BC in Assyria, India and Sumeria. Merchants used to give grain loans to farmers and traders to carry goods between cities. In ancient Greece and Roman Empire, lenders in temples, provided loans, and accepted deposits while performed change of money. The archaeological evidence uncovered in India and China corroborates this. The major development in banking came predominantly in the mediaeval, Renaissance Italy, with the major cities Florence, Venice and Genoa being the financial centres. Technology has become an inherent and integral part of our lives. We are generating a huge amount of data in transfer, storage and usage, with greater demands of ubiquitous accessibility, inducing an enormous impact on industry and society. With the emergence of smarter cities and societies, the security challenges pertinent to data become greater, impending impact on the consumer protection and security. The aim of this chapter is to highlight if SSI and passwordless authentication using FIDO-2 protocol assuage security concerns such as authentication and authorisation while preserving the individual's privacy.

This chapter reviews new technologies, new types of players and new types of financial products that together are fundamentally affecting supply and demand dynamics in the financial sector and contributing to the emerging digital financial landscape. The aim of this chapter is to set a common understanding on the underlying forces of digital disruption in the financial sector before exploring the challenges to monetary and financial stability that are arising. In later chapters, the book will examine how central banks might deal with the challenges and help shape the emerging digital financial landscape.

Intelligent real-time systems are significantly impacting several of the UN Sustainable Development Goals (SDGs) by revolutionising processes in several areas such as Industry 4.0, smart cities, transportation, agriculture, renewable energy, climate change and other economic activities. Given that much of the work to achieve the SDGs relies on information and communication technology, cybersecurity has a potentially immense role to play towards achieving these outcomes. Moreover, cyberattacks have emerged as a new functional threat for interconnected, smart manufacturers and digital supply networks, employed in intelligent real-time systems for the Fourth Industrial Revolution. The effects of cyberattacks can be much more widespread than ever before due to the interconnected nature of Industry 4.0-driven operations. Blockchain can be really useful in such situations as it provides edge protection and allows authentication of the machine-to-machine and human–machine operations, stable data share, life cycle management, access control compliance of devices and self-sustaining operations. Moreover, blockchain can be applied for tracking and tracing transactions through devices, which are performed during the operation, as well as to encrypt and transmit data securely. It is vital to establish complete trust in a technology that is being adopted so that its full potential can be exploited. It is consequently critical that the organisational and information technology strategy fully integrates secure, vigilant and resilient cybersecurity strategies such as blockchain. This will ensure that cyber risks are properly managed in the age of Industry 4.0. This chapter, therefore, analyses the application of blockchain in intelligent real-time systems such as Industry 4.0 so that the opportunities these systems present for the SDGs can be exploited safely with minimum risks to society.

Knowledge management (KM) is an important topic in the age of big data, and this study adds to the existing body of literature by providing a novel KM perspective on the technological phenomenon of artificial intelligence (AI). This study aims to discover how AI might facilitate knowledge-based business-to-business (B2B) marketing. In this chapter, the authors take a close look at the building blocks of AI and the relationships between them. Future research directions and also the effects of the various market information building components on B2B marketing are discussed. The study’s approach is theoretical; it tries to provide a framework for characterising the phenomenon of AI and its constituent parts. Additionally, this chapter provides a methodical analysis of the three categories of market information crucial to B2B marketing: knowledge of customers, knowledge of users, and knowledge of external markets. This research looks at AI through the lens of the conventional data processing framework, analysing the six pillars upon which AI systems are founded. It also explained how the framework’s components work together to transform data into actionable information. In this chapter, the authors will look at how AI works and how it can benefit B2B knowledge-based marketing. It’s not aimed at AI experts but rather at general marketing managers. In this chapter, the possible effects of AI on B2B marketing are discussed using examples from the real world.

Disruptive technologies are accelerating global growth. Artificial intelligence (AI) has the potential to transform the idea of delivering value to end users. On the other hand, the growth of Industry 5.0 has given rise to the concept of humanizing technology, and AI is a promising technology with the potential to contribute to business success. Nevertheless, the idea of value creation in the field of AI is novel, so it is necessary to define the meaning of value by understanding the context of AI applicability in different environments and industries. In this chapter, the author uses the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) procedure to conduct an SLR that provides interesting insights into the focus, industries, and methodologies and approaches used in existing research. Following the initial literature review on the state of the art of AI and value creation, the author also offers a reflection on the strategic implications of AI in the field of marketing, postulating a macrovalue creation framework that addresses the existence of implications on three different levels: emerging markets, Sustainable Development Goals, and adoption issues. Therefore, this chapter examines the value creation perspectives of AI to understand the current research focus and future directions.

The COVID-19 pandemic ushered in multiple challenges for employees, which led to employee turnover, disengagement at work, employees’ mental health issues, etc. The study tries to elucidate how artificial intelligence (AI) herald great promise in human resource management in decreasing cost, attrition level and enhancing productivity. Considering the dearth of studies on recent trends in human resource management (HRM) in the context of AI, the study elucidates the role of AI in facilitating seamless onboarding, diversity and inclusion (D&I), work engagement, emotional intelligence and employees’ mental health. Thus, a conceptual model of recent trends in HRM in the context of AI and its organisational outcomes is proposed. A systematic review and meta-synthesis method are undertaken. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes. The findings of the study suggest that using natural language processing (NLP) and robots has eased the onboarding process. D&I is promoted using data analytics, big data, machine learning, predictive analysis and NLP. Furthermore, NLP and data analytics have proved to be highly effective in engaging employees. Emotional Intelligence is applied through AI simulation and intelligent robots. On the other hand, chatbots, employee pulse surveys, wearable technology, and intelligent robots have paved way for employees’ mental health. The study also reveals that using AI in HRM leads to enhanced organisational performance, reduced cost and decreased intention to quit the organisation. Thus, AI in HRM provides a competitive edge to organisations by enhancing the performance of the employees.