Introduction

Background

The swift progress in artificial intelligence (AI) has transformed multiple sectors, and human resource management (HRM) is likewise affected. With the ongoing evolution of AI, its ability to change HRM practices and processes is becoming more apparent. The incorporation of AI in modern HRM practices has generated significant interest and discussion because of its ability to transform conventional HR processes and decision-making. AI, according to Zuniga et al. (2024), refers to the actual ability of non-human machines or artificial creatures to execute tasks, solve problems, communicate, engage and behave rationally, similar to biological humans. Murugesan et al. (2023) attribute the broadening of AI’s definition to its increasing use in various sectors, emphasising its flexibility and capacity to improve multiple business operations, such as HRM. The convergence of AI and HRM offers a thrilling chance to rethink conventional HR methods and adapt them to the requirements of the digital era (Sakka et al., 2022). This study aims to offer important perspectives on the changing environment of HRM practices and the role of AI in influencing the future of work by examining the possibilities of AI in HRM

The impetus for performing a narrative literature review on ‘The Adoption of Artificial Intelligence in Contemporary Human Resource Management’ arises from the increasing acknowledgement of AI as a revolutionary influence in the work environment. As companies aim to stay competitive in a more digital environment, it is crucial to know how AI can improve HR practices. Furthermore, the incorporation of AI technologies in HRM offers both opportunities and challenges that require careful investigation. For example, although AI can enhance hiring processes and elevate employee experiences, it also brings up ethical issues related to bias and data privacy. This research intends to consolidate existing literature regarding AI implementation in HRM. In doing so, it aims to offer valuable perspectives for HR experts and companies aiming to manage the intricacies of AI incorporation in their employee management strategies.

Research Problem

As organisations adopt AI technologies to enhance HRM practices, it is essential to comprehend the consequences of this integration. This investigation aims to examine the incorporation of AI in HRM and its beneficial effects on the workplace, specifically highlighting the advantages, obstacles and future consequences. Through an in-depth exploration of the qualitative dimensions of this adoption, the study seeks to reveal important insights that can assist organisations in successfully managing the incorporation of AI into modern HRM environments.

Rationale of the Research

Many research efforts have been carried out to examine AI and its effects on HRM. Nevertheless, the discussion about the positive integration of AI in HRM remains limited. This is due to the fact that merging AI with HRM, or closing the gap between humans and AI, would help organisations by improving productivity, performance, effectiveness and efficiency.

Research Objectives

1. To critically examine the positive impact of AI on workplaces with a focus on HRM practices.
2. To identify the advantages of AI integration in key HRM functions.
3. To highlight challenges and propose recommendations for optimising AI use in HRM.
4. To identify the advantages of AI in major HRM practices.
5. To highlight the possible challenges with recommendations for future studies for the best approaches to utilising AI in HRM.

Literature Review

The integration of AI in modern human resource management (HRM) has generated considerable interest in both scholarly research and practical organisational environments. This study seeks to analyse and consolidate the current literature regarding the incorporation of AI in HRM practices, highlighting its effects, the favourable outcomes of the integration, advantages, obstacles, and possibilities for transforming HRM procedures.

Many researchers have highlighted the revolutionary effect of AI on HRM practices. Jedrzejowska (2024) stated that AI technologies can simplify repetitive HR functions, like reviewing resumes and sourcing candidates, enabling HR professionals to concentrate on strategic, high-impact activities. Likewise, Faqihi and Miah (2023) emphasised AI’s impact on improving talent management and employee engagement by analysing extensive data sets to recognise trends and forecast workforce developments.

Numerous articles exist that thoroughly examine the transformative impact of AI on HR functions, such as recruitment, training, talent management and retention. Their research provides important insights into the convergence of AI and HR management currently, along with the expected influence on the HR workforce moving forward. Sousa and Dias (2020) claim that top business intelligence providers are working to incorporate business intelligence and data analytics features into HRM systems. The authors emphasise the strategic aim of positioning HR as a crucial value-enhancing department in the organisation through the integration of business intelligence.

On the other hand, worries have been expressed about the ethical and legal consequences of AI implementation in HRM. Vivek (2023) has stated that employing AI in hiring and selection could unintentionally reinforce biases found in historical data, resulting in unfair outcomes. Additionally, concerns about the transparency and accountability of algorithmic decision-making have sparked discussions, highlighting the necessity for ethical principles and regulatory structures to oversee the integration of AI in HRM.

The investigation of AI implementation in HRM also examines the changing responsibilities of HR professionals in the age of AI. Further, it is emphasised that it is important for HR professionals to cultivate data literacy and analytical abilities to proficiently utilise AI technologies in decision-making. In addition, the reassessment of HR roles and duties in overseeing AI-based processes and promoting a culture of trust and openness has been a central topic in academic discussions (Ekuma, 2024).

The literature highlights the capacity of AI to transform HRM practices by facilitating individualised employee experiences, predictive analytics for workforce management and the automation of standard administrative duties (Sathyaseelan & Srinivasan, 2024). Nonetheless, the effective incorporation of AI in HRM depends on tackling issues concerning data privacy, cybersecurity and the ethical application of AI technologies in sensitive HR procedures (Naturalista et al., 2024).

Methodology

This methodology for a literature review on the ‘Adoption of Artificial Intelligence in Contemporary Human Resource Management’ is crafted to enable a thorough and organised examination of existing academic literature, providing meaningful insights into AI and HRM practices.

The research design of this study utilises a thorough literature review approach to systematically examine and integrate the existing academic literature concerning AI and HRM. The main research goal is to examine how AI (positive impact) is being adopted to support and improve HRM practices by leveraging AI and exploring the potential future integration of human and AI methodologies.

The process of gathering data entails thorough searches of scholarly databases, academic journals, conference proceedings and trustworthy online platforms. Databases like Scopus, Research Gate, Google Scholar and major academic publisher platforms were employed to gather data from peer-reviewed articles, book chapters and research papers concerning the role of AI in HRM. A thorough examination of the overall papers found a total of 30 empirical and peer-reviewed studies concerning AI in HRM, highlighting a wide variety of research themes and methodologies. The literature review and analysis encompass a collection of articles, carefully examining their contributions to grasping AI’s impact on HRM. Additionally, the research encompasses significant recent publications from the years 2023 and 2024 as well.

Taking into account the inclusion and exclusion criteria, the inclusion criteria consist of academic works published in English, peer-reviewed articles and works published within a defined period (2020–2024) to guarantee relevance and precision. Only literature specifically focused on AI and HRM, encompassing its advantages, integration difficulties and benefits, is included. Grey literature and studies not pertaining to the research topic are explicitly excluded.

Following this, a thorough thematic and content analysis of the gathered literature was conducted to pinpoint common themes, emerging trends and varying viewpoints on AI and HRM. Thematic analysis techniques are used to derive essential insights, theoretical models and conclusions from the literature.

Thematic analysis included coding the examined studies into categories like ethical AI implementation, recruitment automation and HR analytics. NVivo 14 software was utilised to maintain uniform coding and recognise developing patterns.

Results and Discussions

Recruitment and Talent Acquisition

The literature demonstrates a fundamental change in hiring and talent acquisition due to the emergence of AI. AI-enabled applicant tracking systems (ATSs) are recognised for their effectiveness in resume evaluation, yet there are worries about their dependence on keywords and the risk of reinforcing biases (Albassam, 2023). Moreover, Koivunen et al. (2022) highlight that while chatbots for candidate communication are lauded for their convenience, the impersonal aspects of these interactions and their effects on the candidate experience need additional investigation. AI is capable of producing analytical reports regarding candidate evaluations for every job listing and assists in forming a historical database of candidates, which may be leveraged by various HR functions such as learning and development or performance management if the candidate becomes part of the organisation (Aroloye, 2024). AI’s potential to develop a historical database of candidates is encouraging, but it is essential to examine the ethical aspects of data privacy and the lasting effects these databases may have on job prospects.

Employee Onboarding

The influence of AI on employee onboarding is complex. Utilising AI can enhance the onboarding process for new employees in a company (Marr, 2023). Engaging AI-powered orientation modules provides a creative method for introducing new employees to the company culture.

Moreover, AI-driven onboarding platforms can aid in familiarising new employees with their teams and departments. This might include virtual introductions, team presentations and individual video calls with important coworkers and supervisors. Organisations can enhance the onboarding experience for new hires by leveraging AI capabilities to make it more engaging, informative and personalised (Stefanic, 2024). Nevertheless, the research indicates a lack of comprehension regarding how these digital interactions influence the social integration of new hires. The effectiveness of AI in simplifying documentation is evident; however, there is a risk that it may render the onboarding process impersonal or fail to recognise the subtleties of human discernment during this important stage.

Performance Management

The advanced AI technologies offer fresh possibilities for HRM, enhancing overall organisational effectiveness and revealing broader prospects for performance management (Hemalatha et al., 2021). HR specialists can utilise AI-driven tools to track and evaluate employees’ performance and productivity from the outset (Al Samman & Obaidly, 2024). AI technologies such as big data, machine learning and predictive analytics assess employee performance and compensate them equitably (Mer & Virdi, 2022). This method seeks to reduce biases between line managers and their staff in organisations, tackling instances where employees feel they are evaluated unfairly in performance reviews due to their rapport with managers. Moreover, AI can act as a feedback and feedforward tool to enhance performance appraisal review processes as well as the entire performance management and evaluation framework (Nyathani, 2023). Bauer et al. (2023) state that by using AI to ease the tone of feedback and feedforward, and applying natural language processing (NLP) to evaluate input from line managers, peers and employees, organisations can discover important trends and areas needing enhancement. Garg et al. (2021) indicated that NLP refers to the capability of machines to interact with humans in their native language, along with their proficiency in understanding spoken and written content and formulating appropriate responses to human input.

Talent Management

Talent management plays an essential role in HRM, and AI can be efficiently applied in this field. It covers the full range of an employee’s experience, such as hiring, retention, advancement, growth, succession planning and opportunities (Surve & Singh, 2024). AI can be incorporated into the recruitment and talent acquisition process to preserve and monitor historical records of employees, such as their training, skills, preferences, learning styles and advancements. This can be associated with AI for learning and development, tying it to performance management to develop customised learning trajectories based on performance evaluations and anticipatory insights (Takyar & Takyar, 2023). This approach allows organisations to greatly minimise the time required for annual training needs assessments and pinpoint skill deficiencies, thus simplifying the process of sourcing training options. Additionally, this method can be utilised to create customised career trajectories, retention strategies and advancement plans for high achievers and skilled personnel, effectively reducing talent attrition within the organisation (Urme, 2023). Additionally, training powered by AI transforms organisations into knowledge-centric entities that can address individual training requirements and enhance the quality of learning (Chen, 2022)

HR Analytical Data and Insights

As per Sangu et al. (2024), HRM professionals can utilise algorithms and robotics to assess HR data, enhancing human abilities and instigating changes in operational frameworks by uncovering relevant patterns, trends and correlations. This promotes data-informed choices and allows predictive analytics to estimate upcoming workforce needs, turnover rates and skill deficiencies. Consequently, HR can proactively tackle organisational challenges and enhance performance by anticipating workforce needs and possible issues. However, the literature demands a more thorough scrutiny of the assumptions that support these predictive models, the potential for algorithmic bias and the clarity of AI-generated decisions.

Employee Engagement

HRM experts employ AI-driven surveys as analytical instruments to evaluate employee satisfaction and engagement rates (Sari et al., 2020). Studies show that employees can gain from AI through the automation of repetitive tasks, enhancing their access to tools and resources for analysing performance, and ultimately boosting organisational efficiency and customer experiences, along with rethinking products or business models (Gaani & Chhibber, 2022). Different AI tools, such as chatbots, are utilised to boost employee engagement by facilitating instant feedback and communication, thus improving involvement and quickly resolving issues. The literature review indicates that the depth and genuineness of insights obtained through AI tools relative to traditional methods are not completely grasped. The influence of AI on the qualitative dimensions of employee engagement is still a topic that requires more investigation.

The Advantages of Implementing AI in HRM Practices

The incorporation of AI into HRM offers numerous benefits that could transform HR practices and improve organisational results (Mer, 2023). As stated by Luz and Olaoye (2024), utilising AI allows HR professionals to enhance processes, improve employee experiences and increase operational efficiency, thus transforming the HR domain in today’s work environment. AI transformation aims to redefine HRM within their organisations. The broad range of benefits linked to the use of AI aims to improve the efficiency of HR personnel and raise the standard of services in organisations (Abdulla, 2024).

According to Duggal (2024), instead of focusing on the drawbacks, people can focus on the benefits of AI, understanding its potential to enhance their mental faculties, improve engagement with customers and staff, and provide them the chance to focus on advanced tasks and enhance their skills to broaden their abilities. Moreover, AI offers staff greater flexibility, allowing them to discover new areas that enhance motivation, education and the quick implementation of fresh knowledge, thereby boosting satisfaction and preventing boredom in the workplace (Luhana et al., 2023). These results are set to generate a beneficial return on investment for the organisation while enhancing both employee happiness and customer satisfaction. Additionally, the incorporation of AI is expected to improve the overall quality of decision-making processes.

Elaborating on the possible benefits of integrating AI into HRM practices highlights the significant influence it can exert on organisational dynamics as well as the career development and contentment of employees (Ganatra & Pandya, 2023). This focus on the beneficial aspects of AI aims to shift attention to the opportunities that AI brings in transforming HRM practices and cultivating a more vibrant and effective work setting.

Implications

While integrating AI into HRM offers advantages, it is crucial to carefully tackle the possible challenges, such as system biases and ethical implications, as numerous researchers have agreed that AI might face difficulties in recognising emotions or biases (Tuffaha, 2023). Nonetheless, Chen and Ibrahim (2023) emphasised that many organisations have successfully incorporated emotions into AI analysis. AI systems have been employed to examine and modify customer and employee reactions according to emotions, in addition to serving educational functions. The optimal method includes blending the knowledge of HRM experts with the benefits of AI, acknowledging that some facets still need human supervision rather than total reliance on AI (Li, 2024). Thorough testing, monitoring and evaluation of AI systems are essential, along with incorporating insights from people of various generations who have valuable knowledge and skills, while also tackling the main worry of HRM professionals concerning the potential rise in layoffs. AI recruitment tools can show algorithmic bias by depending on historical data that disproportionately highlights predominant gender or ethnic groups, like biased keyword filtering or imperfect facial recognition models.

Conclusions and Recommendations

Conclusion

The incorporation of AI would significantly enhance HRM procedures and enable HRM professionals to boost organisational productivity and the efficiency of HRM services (Sakka et al., 2022). Nonetheless, it is crucial to take into account the subsequent suggestions to tackle the difficulties. AI technology should be employed mindfully, emphasising transparency in line with organisational guidelines (Wren, 2024). Consequently, organisations must assess the AI algorithms they plan to implement and modify them as necessary

Recommendations

Organisations must promote transparency and ethical management when implementing AI in HRM. Ongoing surveillance, staff education and equitable algorithms must steer execution. HRM experts need to create distinct legal and ethical guidelines, perform bias evaluations and promote interdisciplinary teamwork to achieve responsible and effective AI incorporation in HR operations.

• Promote transparency: Keep updated on optimal practices for applying AI in HRM and pinpoint successful strategies for collecting data to enhance AI algorithms.
• Provide training: Deliver ongoing education for HRM professionals and employees (end users) on the safe and effective use of AI in HRM.
• Observe and assess: Evaluate the application of HRM practices, track employee advantages, and guarantee proper updating and improvement.
• Responsible and equitable AI utilisation: Adhere to a legal and moral structure to reduce biases and prevent discriminatory results.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.