INTRODUCTION

• Background of the study

The advent of Artificial Intelligence (AI) has transformed the way organizations operate, and Human Resource Management (HRM) is no exception. The increasing adoption of AI in various industries has led to a growing interest in exploring the role of AI in HRM (Kumar et al., 2020). According to a report by Gartner, 77% of organizations plan to use AI-powered HR systems by 2025 (Gartner, 2020). This growing trend has sparked significant interest in understanding the applications, benefits, challenges, and future directions of AI in HRM. This increase utilization of AI in human resource management has greatly affected the productivity of organizations.

HRM productivity refers to the extent to which human resource management practices contribute to the efficiency, quality, and overall output of an organization’s workforce. Unlike traditional productivity measures that focus solely on tangible outputs (e.g., units produced per labor hour), HRM productivity emphasizes the value added by effective talent management. This includes improvements in employee skills and knowledge, enhanced motivation, and the systematic deployment of human capital to realize organizational goals (Lepak, Liao, Chung, & Harden, 2006). In effect, HRM productivity is conceptualized as a strategic outcome that stems from a well-designed HR system and results in higher levels of innovation, engagement, and competitive advantage.

The transformation of HRM from purely administrative functions to a strategic role has evolved in parallel with the recognition that integrated HR systems are critical for boosting workforce productivity. Modern HRM emphasizes the alignment of recruitment, training, performance appraisal, and incentive systems with long-term business strategy. For example, Ulrich, Younger, Brockbank, and Ulrich (2012) argue that when HR practices are integrated and strategically managed, organizations not only enhance their operational efficiency but also achieve sustainable improvements in productivity. Empirical studies have demonstrated that firms implementing a holistic HRM strategy tend to exhibit superior outcomes in employee engagement and performance metrics (Strohmeier, 2021).

Despite the recognized benefits, several challenges complicate the measurement and management of HRM productivity. One central issue is the inherent difficulty in quantifying the often intangible contributions of HR initiatives. For instance, while digital analytics provide robust data on employee engagement and turnover, they may not fully capture improvements in creative problem solving or leadership effectiveness (Boudreau & Cascio, 2017). Moreover, contextual factors such as industry-specific dynamics and cultural differences can influence the effectiveness of HR practices. Research by Hmoud (2021) suggests that supportive leadership and an adaptive organizational culture are essential moderators that enhance the productivity benefits of HRM investments. Hence, organizations are encouraged to develop hybrid models that incorporate both traditional HR metrics and advanced data analytics, ensuring that productivity gains are both measurable and sustainable.

The adoption of AI in HRM has the potential to bring about significant benefits, including improved operational efficiency, enhanced employee experience, and better decision-making (Brynjolfsson & McAfee, 2014). AI-powered HR systems can automate routine tasks, provide personalized support to employees, and analyze large datasets to inform HR decisions (Hao, 2019). For instance, AI-powered chatbots can help employees with routine queries, freeing up HR professionals to focus on more strategic tasks (Kumar et al., 2020). Moreover, AI-powered HR systems can also help organizations to identify and develop future leaders, improve diversity and inclusion, and enhance employee engagement and retention (Ford, 2015).

Despite the potential benefits, the adoption of AI in HRM also raises several concerns, including job displacement, bias, and data privacy (Ford, 2015). For instance, AI-powered recruitment tools may perpetuate existing biases and discriminate against certain groups of candidates (Dastin, 2018). Moreover, the use of AI-powered HR systems raises concerns about data privacy and security, particularly in the context of sensitive employee data (Hao, 2019). Therefore, it is essential to explore the role of AI in HRM and understand its implications for organizations, employees, and society as a whole.

The existing literature on the role of AI in HRM is limited, and there is a need for more research on this topic (Kumar et al., 2020). Most of the existing studies focus on the technical aspects of AI-powered HR systems, with limited attention to the organizational and social implications of AI adoption in HRM (Brynjolfsson & McAfee, 2014). Therefore, this study aims to explore the role of AI in HRM, including its applications, benefits, challenges, and future directions. This study will contribute to the existing literature on the role of AI in HRM by providing a comprehensive review of the current state of AI adoption in HRM, as well as an analysis of the benefits and challenges of AI-powered HR systems. The study will also provide recommendations for HR professionals and organizations on how to effectively adopt and implement AI-powered HR systems.

Company executives around the world are increasingly  looking to AI to create new sources of business value. This is especially true for leading adopters of AI, that have invested in AI initiatives and seen impressive results (Ransbotham et al., 2017). Improving business performance is the primary goal of most enterprises. Innovation in finding new tools and ways of creating better processes is an everyday challenge in today’s economy. Improvement in business from humans has led to a new development of employing machines with AI in the workforce. Digitalization has made computers and machines a must for today’s operations.

The first ideas about AI started in the 40s, when it was widely believed that machinery could function in an intelligent manner. AI is becoming the next buzz term in the plans of the largest corporations. By developing the right AI technology, a business can improve its market position by saving time and money. This happens by automating routine processes and tasks to make faster decisions based on outputs from cognitive technologies. AI has started to be integrated in several business processes with an aim to maximize the efficiency of processes. HRM constitutes an important segment of any company. The introduction of AI in HRM will have an impact on both management practices in recruitment and HR management in general.

Furthermore, this study will also explore the implications of AI adoption in HRM for employees, organizations, and society as a whole. The study will examine the potential impact of AI on employment, skills, and the future of work. The study will also explore the ethical implications of AI adoption in HRM, including issues related to bias, transparency, and accountability. The adoption of AI in HRM has the potential to bring about significant benefits, including improved operational efficiency, enhanced employee experience, and better decision-making. However, it also raises several concerns, including job displacement, bias, and data privacy. Therefore, it is essential to explore the role of AI in HRM and understand its implications for organizations, employees, and society as a whole.

• Statement of the problem

However, despite the growing trend of AI adoption in HRM, there is a lack of understanding about the role of AI in HRM, its applications, benefits, challenges, and future directions (Kumar et al., 2020). According to a report by Gartner, 77% of organizations plan to use AI-powered HR systems by 2025, but many organizations lack the necessary skills and expertise to effectively adopt and implement AI-powered HR systems (Gartner, 2020).

The adoption of AI in HRM has the potential to bring about significant benefits, including improved operational efficiency, enhanced employee experience, and better decision-making (Brynjolfsson & McAfee, 2014). However, the adoption of AI in HRM also raises several concerns, including job displacement, bias, and data privacy (Ford, 2015). For instance, AI-powered recruitment tools may perpetuate existing biases and discriminate against certain groups of candidates (Dastin, 2018). Moreover, the use of AI-powered HR systems raises concerns about data privacy and security, particularly in the context of sensitive employee data (Hao, 2019).

Despite the potential benefits and challenges of AI adoption in HRM, there is a lack of research on this topic. Most of the existing studies focus on the technical aspects of AI-powered HR systems, with limited attention to the organizational and social implications of AI adoption in HRM (Brynjolfsson & McAfee, 2014). Therefore, this study aims to explore the role of AI in HRM, including its applications, benefits, challenges, and future directions. This study will thus be aimed at examining the impact of artificial intelligence human resource management productivity in organizations in Bamenda.

1.3 Research Questions

1.3.1 Main Research Questions

What is the impact of artificial intelligence in human resource management productivity in organizations in Bamenda?

1.3.2 Specific Research Questions

• What is the impact of AI adoption rate on HRM productivity in organizations in Bamenda?
• What is the impact of Automation Intensity on HRM productivity in organizations in Bamenda?
• What is the impact of Algorithmic Decision-Making on HRM productivity in organizations in Bamenda?

1.4 Research Objectives

1.4.1 Main Research Objective

To investigate the impact of Artificial Intelligence on Human Resource Management productivity in organizations in Bamenda.

1.4.2 Specific Research Objectives

• To investigate the impact of AI adoption rate on HRM productivity in organizations in Bamenda.
• To verify the impact of Automation Intensity on HRM productivity in organizations in Bamenda.
• To analyze the impact of Algorithmic Decision-Making on HRM productivity in organizations in Bamenda.