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The field of Artificial Intelligence (AI) has been growing rapidly in the recent past. While in general AI and its related technologies have been around for a few decades, it is only recently AI applications have witnessed tremendous growth.

In recent years, the growth of AI has been nothing short of exponential, permeating nearly every aspect of people’s lives. From the proliferation of smart assistants shaping daily routines to the deployment of machine learning algorithms in industries spanning healthcare, finance, transportation, and entertainment, AI has evolved at an astonishing pace. Advancements in deep learning, natural language processing, and computer vision have propelled the development of sophisticated models capable of understanding context, generating creative content, and making complex decisions. The convergence of big data availability, increased computational power, and innovative algorithms has fueled this expansion, leading to AI becoming not just a technological advancement but a transformative force shaping the future of society and business alike.

The growth of AI has been staggering. Here are a few statistics showcasing this growth:

i.  Investment: According to Statista, global spending on cognitive and AI systems reached $50.1 billion in 2020, and it's projected to grow to $110.7 billion by 2024.

ii. Job Market: The demand for AI-related jobs has surged. LinkedIn reported a 190% increase in AI-related job postings between 2015 and 2019.

iii. AI Startups: The number of AI startups has significantly increased. CB Insights reported that global equity funding to AI startups reached $71.15 billion across 5,185 deals in 2020.

iv.  Patents: The number of AI-related patents has been rising rapidly. The World Intellectual Property Organization (WIPO) reported a 6x increase in the number of AI-related patents between 2010 and 2019.

v.  AI Adoption in Businesses: McKinsey found that 50% of surveyed companies had adopted AI in at least one business function in 2020, up from 20% in 2017.

vi.  AI in Healthcare: The global AI in healthcare market size was valued at $4.9 billion in 2020 and is estimated to reach $99.4 billion by 2027, growing at a CAGR of 42.8% from 2020 to 2027 (Grand View Research).

These numbers highlight the substantial growth and widespread adoption of AI across industries, reflecting the technology's increasing importance and impact on various sectors.

Strohmeier and Piazza (2015) mention that the artificial Intelligence Techniques and its subset, Computational Intelligence Techniques, are not new to Human Resource Management, and since their introduction, a heterogeneous set of suggestions on how to use Artificial Intelligence and Computational Intelligence in Human Resource Management has accumulated. While such contributions offer detailed insights into specific application possibilities, an overview of the general potential is missing. Therefore, this chapter offers a first exploration of the general potential of Artificial Intelligence Techniques in Human Resource Management. To this end, a brief foundation elaborates on the central functionalities of Artificial Intelligence Techniques and the central requirements of Human Resource Management based on the task-technology fit approach. Based on this, the potential of Artificial Intelligence in Human Resource Management is explored in six selected scenarios (turnover prediction with artificial neural networks , candidate search with knowledge-based search engines, staff rostering with genetic algorithms , HR sentiment analysis with text mining , résumé data acquisition with information extraction and employee self-service with interactive voice response).

Tambe et al. (2019) mention that there is a substantial gap between the promise and reality of artificial intelligence in human resource (HR) management. This article identifies four challenges in using data science techniques for HR tasks: complexity of HR phenomena, constraints imposed by small data sets, accountability questions associated with fairness and other ethical and legal constraints, and possible adverse employee reactions to management decisions via data-based algorithms. It then proposes practical responses to these challenges based on three overlapping principles—causal reasoning, randomization and experiments, and employee contribution—that would be both economically efficient and socially appropriate for using data science in the management of employees.

Tewari and Pant (2020) propose that artificial intelligence (AI) technology is the new normal. As everything is powered by AI in the current time, it has altered our way of living. The widespread adoption of AI across businesses and corporations is helping them in streamlining their processes, increasing productivity, boosting efficiency, and reducing costs. The integration of artificial intelligence with human resource management (HRM) practices is changing the way organizations appoint, manage, and engage their workforce. Based on

available data sets and behavioural patterns, artificial intelligence is enabling automated machines to make decisions that are more accurate than those made by people. Due to this

shift, all physical labour has been replaced by machines, which has forced HR professionals to assume more strategic roles. It is paramount for companies and professionals to understand how this technology works and its role in various HRM functions. This paper reviews the work of many eminent researchers to find out ways in which AI is bringing a change in the field of human resource management. This review highlights the key benefits and hidden challenges of AI when applied to HRM and also illustrate its future potential.

Qamar et al. (2021) present an original systematic review of the academic literature on applications of artificial intelligence (AI) in the human resource management (HRM) domain is carried out to capture the current state-of-the-art and prepare an original research agenda for future studies. Fifty-nine journal articles are selected based on a holistic search and quality evaluation criteria. By using content analysis and structural concept analysis, this study elucidates the extent and impact of AI application in HRM functions, which is followed by synthesizing a concept map that illustrates how the usage of various AI techniques aids HRM decision-making. A comprehensive review of the AI-HRM domain’s existing literature is presented. A concept map is synthesized to present a taxonomical overview of the AI applications in HRM. An original research agenda comprising relevant research questions is put forward to assist further developments in the AI-HRM domain. An indicative preliminary framework to help transition toward ethical AI is also presented.

Artificial intelligence (AI) and its related applications are being integrated into firms’ human resource management (HRM) approaches for managing people (Malik et al. 2020; Budhwar et al. 2022).  In the recent past, growth has been observed in AI-based applications proliferating the HRM function, such as the social presence of AI and robotics, effects of AI adoption on individual and business level outcomes, and evaluating AI-enabled HRM practices. Adopting these technologies has resulted in how work is organised in local and international firms, noting opportunities for employees and firms’ resource utilisation, decision-making, and problem-solving. However, despite a growing interest in scholarship, research on AI-based technologies for HRM is limited and fragmented. In response to these combined issues, the above authors present a systematic review and offer a nuanced understating of what is known, and future research directions to frame a research agenda. They develop a conceptual framework that integrates research on AI applications in HRM and offers a cohesive base for future research endeavours. They also develop a set of testable propositions that serve as directions for future research.

Basu et al. (2023) mention that the artificial intelligence (AI) systems and applications based on them are fast pervading the various functions of an organization. While AI systems enhance organizational performance, thereby catching the attention of the decision makers, they nonetheless pose threats of job losses for human resources. This in turn pose challenges to human resource managers, tasked with governing the AI adoption processes. However, these challenges afford opportunities to critically examine the various facets of AI systems as they interface with human resources. To that end, we systematically review the literature at the intersection of AI and human resource management (HRM). Using the configurational approach, we identify the evolution of different theme based causal configurations in conceptual and empirical research and the outcomes of AI-HRM interaction. We observe incremental mutations in thematic causal configurations as the literature evolves and also provide thematic configuration based explanations to beneficial and reactionary outcomes in the AI-HRM interaction process.

HRM and its Functions

Human Resource Management (HRM) is the strategic and coherent approach to the management of an organization's most valued assets—the people working there—who individually and collectively contribute to the achievement of its objectives. It involves the effective utilization of human resources to achieve organizational goals while also providing a framework for managing and developing employees in a way that maximizes their potential and contributions to the organization. HRM encompasses various functions, including recruitment, selection, training, performance management, compensation, and employee relations, all aimed at optimizing the organization's human capital and fostering a positive work environment.

Human Resource Management (HRM) encompasses several key functions that are crucial for managing an organization's workforce effectively (refer Figure 1). These functions include (https://www.umassglobal.edu/news-and-events/blog/key-functions-of-human-resources):

1. Recruitment and Selection: This involves identifying staffing needs, attracting qualified candidates, and selecting the best fit for the organization through processes like interviews, assessments, and background checks.

2. Training and Development: HRM is responsible for providing employees with the knowledge, skills, and abilities required to perform their jobs effectively. This includes training programs, workshops, and opportunities for professional development.

3. Performance Management: HRM oversees the process of evaluating and managing employee performance. This includes setting performance standards, conducting performance appraisals, providing feedback, and addressing performance-related issues.

4. Compensation and Benefits: HRM designs and administers compensation and benefits programs to attract, retain, and motivate employees. This includes salary structures, bonuses, incentives, and employee benefits such as health insurance and retirement plans.

5. Employee Relations: HRM is responsible for managing relationships between employees and the organization. This includes handling grievances, conflicts, disciplinary actions, and fostering a positive work environment.

6. Legal Compliance: HRM ensures that the organization complies with labor laws and regulations related to employment. This includes staying up-to-date with changes in labor laws, maintaining records, and handling legal issues related to employment.

7. HR Planning and Strategy: HRM develops and implements HR strategies aligned with the organization's goals. This involves workforce planning, talent management, succession planning, and aligning HR practices with the overall business strategy.

8. Employee Engagement: HRM focuses on creating an environment where employees are engaged, motivated, and committed to the organization's goals. This includes initiatives to improve morale, communication, and employee involvement in decision-making processes.

These functions are essential for managing human resources effectively and ensuring that the organization has the right talent in place to achieve its objectives.

 

 

Figure 1: HRM and its Functions

AI and its Developments

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans (https://www.investopedia.com/terms/a/artificial-intelligence-ai.asp). These machines are designed to mimic certain cognitive functions such as learning, problem-solving, and decision-making. AI technologies enable machines to analyze large amounts of data, recognize patterns, and make predictions or decisions based on that data. AI can be applied to a wide range of tasks across various industries, including robotics, healthcare, finance, transportation, and many others. It is a rapidly evolving field with the potential to revolutionize how we work, live, and interact with technology.

The evolution of artificial intelligence (AI) can be traced back to the mid-20th century when researchers began exploring the possibility of creating machines that could simulate human intelligence. Here are some key milestones in the evolution of AI (Delipetrev et al. 2020):

1. Early Concepts (1950s-1960s): The term "artificial intelligence" was coined in 1956 at the Dartmouth Conference, where researchers discussed the possibility of creating machines that could think like humans. Early AI research focused on symbolic reasoning and problem-solving using techniques such as logical reasoning and search algorithms.

2. Symbolic AI (1960s-1970s): During this period, researchers developed symbolic AI systems that used logic and rules to represent knowledge and solve problems. Expert systems, which encoded human expertise in a specific domain, were a prominent application of symbolic AI during this time.

3. AI Winter (1970s-1980s): Despite early optimism, progress in AI faced challenges, leading to a period known as the "AI winter." Funding for AI research decreased as initial expectations were not met, and some researchers became skeptical about the feasibility of achieving human-level intelligence in machines.

4. Resurgence of AI (1980s-1990s): The resurgence of AI began in the 1980s with the development of new techniques such as neural networks, which are inspired by the structure of the human brain. This period also saw advances in machine learning, a subfield of AI focused on developing algorithms that enable machines to learn from data.

5. Machine Learning and Deep Learning (2000s-Present): The 2000s marked a significant shift in AI research towards machine learning and deep learning. These approaches leverage large datasets and computational power to train models that can perform tasks such as image recognition, natural language processing, and autonomous decision-making.

6. Current State and Future Directions: Today, AI technologies are integrated into various applications, including virtual assistants, recommendation systems, autonomous vehicles, and medical diagnostics. Ongoing research focuses on enhancing AI capabilities, addressing ethical and societal implications, and achieving artificial general intelligence (AGI) that can perform any intellectual task a human can.

The evolution of AI has been characterized by periods of excitement and progress followed by periods of skepticism and stagnation. However, recent advancements in machine learning and deep learning have led to significant breakthroughs, driving the widespread adoption of AI across industries and laying the groundwork for future innovations.

Applications of AI in HRM

Artificial Intelligence (AI) is increasingly being applied in Human Resource Management (HRM) to streamline processes, improve decision-making, and enhance the overall efficiency of HR operations. Some applications of AI in HRM include (Tambe et al. 2019):

1. Recruitment and Talent Acquisition: AI can automate various aspects of the recruitment process, such as resume screening, candidate sourcing, and initial assessments. AI-powered tools can analyze large volumes of resumes and job applications to identify the most qualified candidates based on predefined criteria, thereby saving time and improving the quality of hires.

2. Candidate Matching and Selection: AI algorithms can match candidates to job openings based on their skills, experience, and qualifications. These algorithms can analyze both structured and unstructured data to identify the best-fit candidates for specific roles, reducing bias and improving the likelihood of successful hires.

3. Employee Onboarding: AI-powered chatbots and virtual assistants can provide personalized onboarding experiences for new employees. These tools can answer common questions, provide information about company policies and procedures, and guide new hires through the onboarding process, thereby improving their overall experience and reducing the burden on HR staff.

4. Employee Engagement and Retention: AI can be used to analyze employee feedback, sentiment, and engagement data to identify trends and patterns that can help improve employee satisfaction and retention. AI-powered systems can also predict which employees are at risk of leaving the organization, allowing HR teams to take proactive measures to address potential issues.

5. Learning and Development: AI can personalize learning and development programs based on individual employee needs and preferences. By analyzing employee performance data and learning patterns, AI can recommend relevant training courses, learning materials, and career development opportunities, thereby improving the effectiveness of employee development initiatives.

6. Performance Management: AI can assist in performance evaluations by analyzing employee performance data and providing insights to managers. AI-powered systems can identify performance trends, highlight areas for improvement, and provide recommendations for setting goals and development plans.

7. HR Analytics and Reporting: AI can analyze HR data to generate insights and predictions that can inform strategic decision-making. By analyzing data related to employee performance, turnover, demographics, and other HR metrics, AI can help HR teams identify trends, forecast future needs, and make data-driven decisions. 

Overall, AI has the potential to transform various aspects of HRM by automating repetitive tasks, improving decision-making, and enabling more personalized and efficient HR processes. However, it's important to use AI ethically and ensure that it complements human judgment and expertise rather than replacing it entirely.

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2. Budhwar, P., Malik, A., De Silva, M. T., & Thevisuthan, P. (2022). Artificial intelligence–challenges and opportunities for international HRM: a review and research agenda. The International Journal of Human Resource Management, 33(6), 1065-1097.

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