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Artificial intelligence in tactical human resource management: a systematic literature review.

Digitization within Human Resource Management (HRM) has resulted in Artificial Intelligence (AI) becoming increasingly prevalent in Human Resource Management Systems (HRMS) and HR Information Systems (HRIS). The tactical procedures of recruitment, employee performance evaluation and satisfaction, compensation and benefit analysis, best practice analysis, discipline management, and employee training and development systems have seen a growth in the incorporation of AI. To better understand this evolution, we seek to explore publication sources and literature that feature the application of AI within HRM. By utilizing a systematic literature review methodology, this paper identifies which tactical HRIS (T-HRIS) components are featured in literature and how each T-HRIS component is represented. This paper gives insight to which component of tactical HRM/HRIS receives attention and identifies gaps in research to give direction to future research agendas.

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Management Research Review

ISSN : 2040-8269

Article publication date: 4 January 2021

Issue publication date: 16 July 2021

The purpose of this paper is to examine the use of artificial intelligence (AI) in human resource management (HRM) in the Global South.


Multiple case studies of AI tools used in HRM in these countries in recruiting and selecting as well as developing, retaining and productively utilizing employees have been used.

With AI deployment in HRM, organizations can enhance efficiency in recruitment and selection and gain access to a larger recruitment pool. With AI deployment in HRM, subjective criteria such as nepotism and favoritism are less likely to come into play in recruitment and selection of employees. AI deployment in HRM also has a potentially positive impact on the development, retainment and productive utilization of employees.

Research limitations/implications

AI is an evolving technology. Most HRM apps have not gained enough machine learning capabilities with real-world experience. Some of them lack a scientific basis. AI in HRM thus currently affects only a tiny proportion of the population in the GS.

Practical implications

The paper explores the roles of AI in expanding recruitment pools. It also advances our understanding of how AI-based HIRM tools can help reduce biases in selecting candidates, which is especially important in the Global South. It also delves into various mechanisms by which AI helps in the development, retainment and productive utilization of employees.


We provide details of various mechanisms by which AI brings input and output efficiencies in recruitment and selection in these countries.

Kshetri, N. (2021), "Evolving uses of artificial intelligence in human resource management in emerging economies in the global South: some preliminary evidence", Management Research Review , Vol. 44 No. 7, pp. 970-990.

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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Artificial Intelligence in Human Resource Management

Artificial Intelligence in Human Resource Management

Industry Advice Management

While, in the past, artificial intelligence may have been thought to be a product of science fiction, most professionals today understand that the adoption of smart technology is actively changing workplaces. There are applications of AI throughout nearly every profession and industry, and human resources careers are no exception.

A recent survey conducted by Oracle and Future Workplace found that human resources professionals believe AI can present opportunities for mastering new skills and gaining more free time, allowing HR professionals to expand their current roles in order to be more strategic within their organization.

Among HR leaders who participated in the survey, however, 81 percent said that they find it challenging to keep up with the pace of technological changes at work. As such, it is more important now than ever before for human resources professionals to understand the ways in which AI is reshaping the industry.

Read on to explore what artificial intelligence entails, how it is applied to the world of human resources management, and how HR professionals can prepare for the future of the field today.

What is Artificial Intelligence?

At a high level, artificial intelligence (AI) is a technology that allows computers to learn from and make or recommend actions based on previously collected data. In terms of human resources management, artificial intelligence can be applied in many different ways to streamline processes and improve efficiency.

Uwe Hohgrawe , lead faculty for Northeastern’s Master of Professional Studies in Analytics program explains that “we as humans see the information in front of us and use our intelligence to draw conclusions. Machines are not intelligent, but we can make them appear intelligent by feeding them the right information and technology.” 

Learn More:  AI & Other Trends Defining the HRM Industry

While organizations are adopting AI into their human resources processes at varying rates, it is clear to see that the technology will have a lasting impact on the field as it becomes more widely accepted. For this reason, it is important that HR professionals prepare themselves for these changes by understanding what the technology is and how it is applied across various functions.

Interested in becoming a strategic business partner in your organization?

Learn more about earning an advanced degree in Human Resources Management

3 Top Applications of AI in HR

Among the numerous applications of AI in the human resources sector, some of the first changes HR professionals should expect to see involve recruitment and onboarding, employee experience, process improvement, and the automation of administrative tasks.

1. Recruitment and Onboarding

While many organizations are already beginning to integrate AI technology into their recruiting efforts, the vast majority of organizations are not. In fact, Deloitte’s 2019 Global Human Capital Trends survey found that only 6 percent of respondents believed that they had the best-in-class recruitment processes in technology, while 81 percent believed their organization’s processes were standard or below standard. For this reason, there are tremendous opportunities for professionals to adapt their processes and reap the benefits of using this advanced technology.

During the recruitment process, AI can be used to the benefit of not only the hiring organization but its job applicants, as well. For example, AI technology can streamline application processes by designing more user-friendly forms that a job applicant is more likely to complete, effectively reducing the number of abandoned applications.

While this approach has made the role of the human resources department in recruitment much easier, artificial intelligence also allows for simpler and more meaningful applications on the candidate’s end, which has been shown to improve application completion rates .

Additionally, AI has played an important role in candidate rediscovery. By maintaining a database of past applicants, AI technology can analyze the existing pool of applicants and identify those that would be a good fit for new roles as they open up. Rather than expending time and resources searching for fresh talent, HR professionals can use this technology to identify qualified employees more quickly and easily than ever before. 

Once hiring managers have found the best fit for their open positions, the onboarding process begins. With the help of AI, this process doesn’t have to be restricted to standard business hours—a huge improvement over onboarding processes of the past .

Instead, AI technology allows new hires to utilize human resources support at any time of day and in any location through the use of chatbots and remote support applications. This change not only provides employees with the ability to go through the onboarding process at their own pace, but also reduces the administrative burden and typically results in faster integration.

2. Internal Mobility and Employee Retention

In addition to improvements to the recruitment process, HR professionals can also utilize artificial intelligence to boost internal mobility and employee retention.

Through personalized feedback surveys and employee recognition systems, human resources departments can gauge employee engagement and job satisfaction more accurately today than ever before. This is incredibly beneficial considering how important it is to understand the overall needs of employees, however there are several key organizational benefits to having this information, as well.

According to a recent report from the Human Resources Professional Association, some AI software can evaluate key indicators of employee success in order to identify those that should be promoted, thus driving internal mobility. Doing so has the potential to significantly reduce talent acquisition costs and bolster employee retention rates. 

This technology is not limited to identifying opportunities to promote from within, however; it can also predict who on a team is most likely to quit. Having this knowledge as soon as possible allows HR professionals to deploy retention efforts before it’s too late, which can strategically reduce employee attrition.

3. Automation of Administrative Tasks

One of the key benefits of leveraging artificial intelligence in various human resources processes is actually the same as it is in other disciplines and industries: Automating low value, easily repeatable administrative tasks gives HR professionals more time to contribute to strategic planning at the organizational level. This, in turn, enables the HR department to become a strategic business partner within their organizations. 

Smart technologies can automate processes such as the administration of benefits, pre-screening candidates, scheduling interviews, and more. Although each of these functions is important to the overall success of an organization, carrying out the tasks involved in such processes is generally time-consuming, and the burden of these duties often means that HR professionals have less time to contribute to serving their employees in more impactful ways.

Deploying AI software to automate administrative tasks can ease this burden. For instance, a study by Eightfold found that HR personnel who utilized AI software performed administrative tasks 19 percent more effectively than departments that do not use such technology. With the time that is saved, HR professionals can devote more energy to strategic planning at the organizational level.

Preparing For the Future of Human Resources Management

While it is clear that artificial intelligence will continue to positively shape the field of human resources management in the coming years, HR professionals should also be aware of the challenges that they might face.

The most common concerns that HR leaders have focus primarily on making AI simpler and safer to use. In fact, the most common factor preventing people from using AI at work are security and privacy concerns. Additionally, 31 percent of respondents in Oracle’s survey expressed that they would rather interact with a human in the workplace than a machine. Moving forward, HR professionals will need to be prepared to address these concerns by staying on top of trends and technology as they evolve and change.

“People will need to be aware of ethical and privacy questions when using this technology,” Hohgrowe says. “In human resources, [AI] can involve using sensitive information to create sensitive insights.”

For instance, employees want their organizations to respect their personal data and ask for permission before using such technology to gather information about them. However organizations also want to feel protected from data breaches, and HR professionals must take the appropriate security measures into account.

To prepare for the future of human resources management, professionals should take the necessary steps to learn about current trends in the field , as well as lay a strong foundation of HR knowledge that they can build upon as the profession evolves.

Earning a Master of Science in Human Resources Management

Staying up to date with industry publications and networking with leaders in the field is a great way to stay abreast of current trends like the rapid adoption of artificial intelligence technologies. Building your foundational knowledge of key human resource management theories, strategy, and ethics, on the other hand, is best achieved through higher education.

Although there are many certifications and courses available that focus on specific HR topics, earning an advanced degree like a Master of Science in Human Resources Management provides students with a more holistic approach to understanding the connection between an organization and its people.

“At Northeastern, we highlight the importance of three literacies: data literacy, technological literacy, and humanic literacy. That combination is one of the areas where I believe we will pave the way in the future,” Hohgrawe says. “This also allows us to explore augmented artificial intelligence in a way that appreciates the relationship between human, machine, and data.”

Students looking to specialize in AI also have the opportunity to declare a concentration in artificial intelligence within Northeastern’s human resource management program . Those who specialize in this specific aspect of the industry will study topics such as human resources information processing, advanced analytical utilization, and AI communication and visualization. Similarly, those who seek a more technical master’s degree might consider a Northeastern’s Master of Professional Studies in Enterprise Intelligence , which also includes a concentration in AI for human resources.

No matter each student’s specific path, however, those who choose to study at Northeastern will have the unique chance to learn from practitioners with advanced knowledge and experience in the field. Many of Northeastern’s faculty have previously or are currently working in the human resources management field, enabling them to bring a unique perspective to the classroom and educate students on the real-world challenges that HR professionals face today.

Between the world-class faculty members and the multitude of experiential learning opportunities provided during the pursuit of a master’s degree, aspiring HR professionals will graduate from Northeastern’s program with the unique combination of experience and expertise needed to land a lucrative role in this growing field .

Interested in advancing your career in HR? Explore Northeastern’s Master of Science in Human Resources Management program and consider taking the next step toward a career in this in-demand industry. 

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Artificial intelligence and HRM: identifying future research Agenda using systematic literature review and bibliometric analysis

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The present research aims to identify significant contributors, recent dynamics, domains and advocates for future study directions in the arena of integration of Artificial Intelligence (AI) with Human Resource Management (HRM), in the context of various functions and practices in organizations. The paper adopted a methodology comprising of bibliometrics, network and content analysis (CA), on a sample of 344 documents extracted from the Scopus database, to identify extant research on this theme. Along with the bibliometric analysis, systematic literature review was done to propose an Artificial Intelligence and Human Resource Management Integration (AIHRMI) framework. Five clusters were recognized, and CA was conducted on the documents placed in the group of articles. It was found that vital research concentration in this arena is primarily about AI embeddedness in various HRM functions such as recruitment, selection, onboarding, training and learning, performance analysis, talent acquisition, as well as management and retention. The study proposes an AIHRMI framework developed from various studies considered in the current research. This model can provide guidance and future directions for several organizations in expansion of use of AI in HRM.

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artificial intelligence in hrm research paper

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artificial intelligence in hrm research paper

( Source : Authors). Note TP = Total publications, CoC = Co-citation count, CoA = Co-authorship, CoCu = Collaboration of countries, KF = Author key-word frequency, AJG = Academic journal guide, SNA = Social network analysis, NV = Network visualization, BtwCA = Between centrality analysis, PRA = Page rank analysis, TSA = Thematic structure analysis

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Artificial Intelligence in Human Resources Management: Challenges and a Path Forward

34 Pages Posted: 1 Nov 2018 Last revised: 6 May 2022

Peter Cappelli

University of Pennsylvania Wharton School - Center for Human Resources; National Bureau of Economic Research (NBER); University of Pennsylvania - Management Department

Prasanna Tambe

Wharton School, U. Pennsylvania

Valery Yakubovich

ESSEC Business School; University of Pennsylvania

Date Written: April 8, 2019

We consider the gap between the promise and reality of artificial intelligence in human resource management and suggest how progress might be made. We identify four challenges in using data science techniques for HR tasks: 1) complexity of HR phenomena, 2) constraints imposed by small data sets, 3) accountability questions associated with fairness and other ethical and legal constraints, and 4) possible adverse employee reactions to management decisions via data-based algorithms. We propose practical responses to these challenges and converge on three overlapping principles - causal reasoning, randomization and experiments, and employee contribution—that could be both economically efficient and socially appropriate for using data science in the management of employees.

Keywords: artificial intelligence, human resource management, algorithmic management, big data, machine learning, data science

JEL Classification: C55, C63, J00, M12

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Applied Artificial Intelligence

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Artificial Intelligence and Human Resources Management: A Bibliometric Analysis

Artificial Intelligence: A New Paradigm in Human Resource Management

Concept of artificial intelligence, artificial intelligence applied to people management, benefits and challenges of artificial intelligence in human resources management, methodology, conclusions.

Artificial Intelligence (AI) is increasingly present in organizations. In the specific case of Human Resource Management (HRM), AI has become increasingly relevant in recent years. This article aims to perform a bibliometric analysis of the scientific literature that addresses in a connected way the application and impact of AI in the field of HRM. The scientific databases consulted were Web of Science and Scopus, yielding an initial number of 156 articles, of which 73 were selected for subsequent analysis. The information was processed using the Bibliometrix tool, which provided information on annual production, analysis of journals, authors, documents, keywords, etc. The results obtained show that AI applied to HRM is a developing field of study with constant growth and a positive future vision, although it should also be noted that it has a very specific character as a result of the fact that most of the research is focused on the application of AI in recruitment and selection actions, leaving aside other sub-areas with a great potential for application.

The supposed “Fourth Industrial Revolution” or “Industry 4.0” has introduced intelligent technologies like Artificial Intelligence (AI) (Kong et al. Citation 2021 ). The increased development of information and communication technologies (ICT) allows phenomena like AI to greatly influence different parts of society (Bolander Citation 2019 ) becoming one of the most relevant elements of all possible changes in various aspects of life in this era (Aloqaily and Rawash Citation 2022 )

Although different departments of multiple organizations have adopted or integrated AI-based tools, the Human Resources (HR) department still cannot implement them (Vrontis et al. Citation 2022 ). Despite there being many people in the HR department of organizations that recognize the importance of applying AI, they also point out that they have not taken any actions regarding this. This is a reality that shows that even though AI in the HR area is still a developing revolution and is mostly limited to large companies (Bolton Citation 2018 ), it is already unstoppable.

Due to the relative novelty of this technology and its application in different areas of the organization, many of the scientific developments in this field have mostly occurred in recent years. For this reason, although AI has been presented as a powerful tool in HRM, academic research on the subject is not very extensive (Pan et al. Citation 2022 ).

In this context, we consider that based on a bibliometric approach, the article aims to identify and analyze the connection of the AI phenomenon with the human resource management (HRM) of organizations to study (1) the level of knowledge and training of their managers, (2) the benefits and challenges in its implementation, and (3) identify the subareas with greater development and implementation in HRM.

The connection between AI and HRM allows us to establish the following research questions for this work. The first research question is related to previous AI reflections and challenges. However, authors seem unclear how AI will affect or benefit employees and societies (Mitchell and Brynjolfsson Citation 2017 ). Other authors point out to the need for more data about on the speed of AI progress (Nedelkoska and Quintini Citation 2018 ). Especially its impact on every HRM-related task.

Does the scientific community consider AI to be a commonly used tool in HRM?

The second proposed question has been studied by several published works that indicate the benefits of AI technology in different HRM sub-tasks (Qamar et al. Citation 2021 ).

Does AI have a similar impact on all HRM sub-areas?

Are employees in HR areas prepared to meet the challenges posed by AI in people management?

Does the application of AI in HRM help to improve the company’s competitiveness?

The answer to this research questions derived from the results obtained together with the discussion and the most relevant conclusions support the theorization presented in this paper. Regarding the originality of this work, this study, based on quantitative and qualitative research, from the combined use of the most relevant scientific databases, Web of Science and Scopus, allows us to focus on how IA has been integrated into organizations in HRM and its influence on the approach of organizations and Human Resources. The results obtained will allow make the following contributions. First it will serve the research community in the AI field and its applications in the management of people and talent in organizations as a starting point for future related research work. Also important will be the implications for the people responsible by allowing the knowledge of the main uses and applications of new resources and tools in the HRM of organizations will also be relevant and current trends in their application.

artificial intelligence in hrm research paper

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Table 1. definitions of artificial intelligence..

Despite the ambiguous origin of the concept of AI, two authors stand out in its development. On the one hand, we have A.M. Turing, the father of modern computation, while on the other hand, there is J. McCarthy, the father of AI. Turing ( Citation 1937 ) introduced the concept of algorithms and laid the foundation of computer science. Later, Turing ( Citation 1950 ) proposed the Turing test, which tests whether a machine has the capacity to be as intelligent as the person performing its functions. However, J. McCarthy coined the term “artificial intelligence” during a conference in Dartmouth (Paesano Citation 2021 ). In the 1950s and 1960s, AI was expected to develop rapidly into computers and robots with human-level cognitive capabilities, but that did not happen until it recently gained prominence (Bolander Citation 2019 ; Pillai and Sivathanu Citation 2020 ).

Human capital is a differentiating element of an organization as it is an intangible resource that is difficult for competitors to imitate, thus giving a potential competitive advantage to any organization (Kearney and Meynhardt Citation 2016 ).

HRM has become a strategic trend in organizations due to economic, political, social, and especially technological changes (Jatobá et al. Citation 2019 ). Not all departments have embraced this new role, and strategic positioning remains slow and sometimes problematic (Poba-Nzaou et al. Citation 2020 ). In these cases, incorporating technologies like AI requires the need to evolve with the other facets of society (Michailidis Citation 2018 ).

The role of AI in an organization is to improve efficiency and effectiveness of the HR function by making the various management processes agile and accurate (Nankervis et al. Citation 2021 ). For HRM, IA will enable the understanding and control of a data collection process so that this process is included in an organizational and economic efficiency strategy (Varma et al. Citation 2022 ). Among the different areas that make up the HRM in an organization where AI is starting are: (1) talent search and recruitment, (2) training and development, (3) performance analysis, (4) career development, (5) compensation, and (6) staff turnover (Abdeldayem and Aldulaimi Citation 2020 ; Nawaz Citation 2020 ; Qamar et al. Citation 2021 ; Yahia, et al. Citation 2021 )

Qamar et al. ( Citation 2021 ) showed that AI has been implemented in HRM in various organizations via the following techniques:

Expert Systems: They are programs designed to configure expert knowledge into logical structures that solve unstructured problems and help develop complete information systems by providing easy access to knowledge. It is applied mainly in HR planning, compensation, recruitment, and labor management (Malik et al. Citation 2022 ).

Fuzzy Logic: This technique is used in different research fields (Salmerón and Palos-Sánchez Citation 2019 ). In the case of HRM, it’s based on set membership levels, whose values vary between 0 and 1. A value of 0 indicates no membership, while a value of 1 shows full membership. With these sets, fuzzy logic can quantify data uncertainty and foresee future scenarios to facilitate decision-making (Kimseng et al. Citation 2020 ). Its application began in 2000 and was used in personnel selection and optimal workforce design (Qamar et al. Citation 2021 ).

Artificial Neural Networks: This application is a simplified model developed to mimic the function of a human brain. Its structure comprises a processing element, a layer, and a network to recreate the human learning process (Huang et al. Citation 2006 ). It is one of the most popular techniques for prediction and is mainly used in selection, recruitment, and personnel performance management (Qamar et al. Citation 2021 ).

Data Mining: It is the extraction of valuable but hidden information. Through its application, organizations can transform useful information and patterns into competitive advantages (Huang et al. Citation 2006 ). Data mining was used in HRM in 2006 and has been applied mainly for recruitment, competency and performance evaluation, and talent management.

Genetic algorithm: These information search techniques based on replication, mutation, and gene crossover arrive at optimal solutions to mathematical problems. It is used mainly in workforce planning and personnel performance evaluation (Zhang et al. Citation 2021 ).

Machine learning: It is the learning process by which a machine can learn by itself without being particularly programmed to do so (Rąb-Kettler and Lehnervp Citation 2019 ). Several papers agree that the use of machine learning in decision-making is quite beneficial for HR managers and turnover prediction (Hamilton and Davison Citation 2022 ).

As with any technological advance, AI brings both benefits and challenges, and its application in HRM is no different (Vrontis et al. Citation 2022 ). These can be approached from three points of view: employees, company, and society.

We highlight the following potential benefits:

The automation of repetitive and time-consuming tasks allows HR managers to focus on those tasks that add value and require unique skills and abilities (Pillai and Sivathanu Citation 2020 ). The reduction or minimization of errors owing to machine learning also helps improve decision-making by providing more and better-processed information (Michailidis Citation 2018 ). According to a 2019 survey, 61% of companies were using AI to improve HRM in key AI-transformed HRM areas. This task will include time-consuming and labor-intensive processes in recruitment, such as reading many CVs, sorting through them and identifying the best candidates and detect employees who need some training (Rykun Citation 2019 )

For companies, AI means greater effectiveness and efficiency as it streamlines management processes and reduces associated costs (Nankervis et al. Citation 2021 ). It enables greater candidate outreach as it reaches passive candidates who are not in active job search but might become interested in the position (Black and van Esch Citation 2021 ). Another important element for companies is the improvement of communication and interaction possibilities among employees (Michailidis Citation 2018 ). Research articles looks at how AI help to improve the successive stages of the recruitment process: identifying, selecting and retaining talented people (Allal-Chérif et al. Citation 2021 ).

The creation of new professional profiles linked to AI, like robotics specialists, data scientists, deep learning experts, generate new scenarios which can benefit the public (Michailidis Citation 2018 ).

As far as challenges are concerned, the following can be highlighted:

The application of AI may contribute to burnout, with some employees being worried about their career uncertainty, since machines may replace them, thereby creating anxiety and job insecurity (Kong et al. Citation 2021 ). There is also dehumanization of personal relationships, as some of the HRM processes may be performed entirely by machines, like the use of chatbots (Fritts and Cabrera Citation 2021 ). This implies the continuous need for training in technological matters. Finally, it is necessary to point out that the “techno-stress” is a consequence of excessive and continuous use of any type of technology (Malik et al. Citation 2021 ).

The need for highly qualified personnel to manage and acquire the necessary skills to keep up with the increasing technological developments (Abdeldayem and Aldulaimi Citation 2020 ) is a reality in AI. Even though it has high implementation costs, it can reduce costs in the processes where they are applied (Michailidis Citation 2018 ). Another challenge is the existence of biases due to the use of small and non-representative data volumes (Soleimani et al. Citation 2022 ) and the increased exposure of the company leading to increased risk of its data security breach (Malik et al. Citation 2021 ).

One of the main challenges in this area is the “technology gap” Since technology in general and AI has divided the world, it has created greater technological inequality. This is because not all countries can implement and maintain technological infrastructure (Abdeldayem and Aldulaimi Citation 2020 ). Potential job losses in certain professions are also important in the face of these challenges (Hamilton and Davison Citation 2022 ).

The methodology used was bibliometric analysis using the Bibliometrix application. This tool was developed by Aria and Cuccurullo ( Citation 2017 ) to carry out comprehensive analyses of the scientific mapping of a topic. It is an open-source tool to perform a comprehensive analysis of the scientific literature. It was programmed in R language to be flexible and facilitate integration with other statistical and graphical packages. Bibliometrix enables the structured analysis of large amounts of information to infer: (a) trends over time, (b) which topics are being investigated, (c) changes in the boundaries of disciplines, etc., thus summarizing a topic (Guleria and Kaur Citation 2021 ).

The first step was to determine the databases to be used for the document search. The databases being queried were Web of Science and Scopus, as they are currently the most relevant within our research field (Parris and Peachey Citation 2013 ). The search keywords on both bases were “ Artificial Intelligence ” and “ Human Resources ” in the search field (Article Title, Abstract, and Keywords) (Macke and Genari Citation 2019 ) for the period 2018–2022. This period was chosen based in previous authors. For Kshetri ( Citation 2021 ) AI-based HRM applications can bring about significant changes in human resource management practices. However, previous researchers have observed a substantial gap between the promise and reality of AI in HRM (Michailidis Citation 2018 ; Tambe et al. Citation 2019 ). The research domain of AI in HRM is relatively nascent (Strohmeier and Piazza Citation 2013 ). Garg et al. ( Citation 2022 ) note the narrowing of the gap between the number of journal and conference papers from 2017 onwards: a decrease in conference papers with a simultaneous increase in journal papers shows the increasing confidence, interest, and acceptance for AI, especially Machine Learning (ML).

Based on all this, the choice was justified because (1) it had the highest number of publications on the issue, (2) it had an interest in the topic, and (3) the previous literature does not correspond to the current technological level.

Subsequently, the scientific fields selected for the query were (1) Business, (2) Management and Accounting, (3) Arts and Humanities, (4) Social Sciences, (5) Economics and Finance, and (6) Psychology and Research Management. These areas were chosen since they were directly related to our current scenario. The scientific fields that could contribute the least to research, such as physics, biology, medicine, etc., were eliminated. The analyzed works were those written in English to cover a larger number of publications (Gutiérrez and Maz Citation 2004 ) and limited to those publications that were only articles (Podsakoff et al. Citation 2005 ) excluding works corresponding to the following types of documents: (a) book, (b) book chapter, (c) proceedings paper, (d) review and (e) editorial material (Vlačić et al. Citation 2021 ).

Figure 1. Identification of articles to be analyzed.

The final articles were exported from the databases in their respective formats; Plain text and BibTeX for Web of Science and Scopus, respectively. They were then integrated into a single format to be imported later into the Biblioshiny platform and further data analysis was carried out. Before processing the data with this software, the following steps were adopted: (1) Download and install the latest version of R and RStudio ( ) (2) Open RStudio and in the console window type the following command to finish the installation of Bibliometrix; install. packages (“bibliometrix”) (3) Type the following command to be able to run the Biblioshiny program: library (bibliometrix) biblioshiny ()

According Iden and Eikebrokk ( Citation 2013 ) and to the established inclusion and exclusion criteria the data extracted from each study were as follows: (1) the journal and full reference, (2) the authors and their institutions, (3) the countries where they were situated, (4) the keywords, (5) classification of the research methods, (6) theoretical frameworks and references theories used, (7) main topic area, (8) research questions and (9) a summary of the study.

The critical examination of the content of each article (Bellucci et al. Citation 2021 ) together with the use of the Bibliometrix tool, in particular, by means of Multiple Correspondence Analysis (MCA), made it possible to establish three thematic clusters: (1) AI in HRM, (2) Digital Recruitment and (3) Electronic HR. According Paul et al. ( Citation 2021 ) the systematic review of a topic in depth and with rigor favors both the theory on an area and the research methodology in that field can benefit, this is our purpose with the development of this work in the field of IA and HRM.

AI is undoubtedly one of the most important innovations. Both academics and practitioners hope that IA can solve this problem and offer a solution to support and streamline innovation processes. However, the literature on this topic is fragmented (Pietronudo et al. Citation 2022 ). These authors concluded that AI renews the organization of innovation and AI triggers new challenges. That is, they suggest that AI is not a tool that uniformly optimizes innovation management and decision-making but is better understood as a multifaceted solution.

Table 2. Previous AI and HRM Literature Overviews.

Figure 2. Annual evolution of publications.

Table 3. Summary of bibliographic information.

The annual distribution of the number of articles shows the general state of research and trends, with exponential growth occurring only in the last five years. Advances and growth in the importance of AI in both academia and HR (Jatobá et al. Citation 2019 ) have sparked increased interest in investigating the influence of one topic on the other. Although there were only two articles in 2017 addressing the concepts in a connected way, the number increased to 10 in 2019. The trend line shows that AI will soon persist in the future as one of the top world innovations (Qamar et al. Citation 2021 ) with an annual growth rate: 64.38%.

Analysis of Sources

Table 4. sources with the largest number of related publications..

Figure 3. Bradford Law.

Figure 4. Source Growth.

Table 5. Impact of sources.

The g-index is calculated from the distribution of citations of an author’s publications, which results in a set of articles ranked in decreasing order by the number of citations they have. The Hirsch index (h-index) uses the set of the author’s most cited articles and the number of citations it has received in other publications. The m-index is defined as H/n, where h is the h-index and n is the number of years elapsed since the scientist’s first publication (Aria and Cuccurullo Citation 2017 ).

Analysis of Authors

Table 6. relevant authors., table 7. author impact factor., table 8. authors affiliations., table 9. scientific production by countries., table 10. average number of citations of articles by country., analysis of documents, table 11. most cited articles..

Spectroscopic analysis: According to Marx et al. ( Citation 2014 ), Reference Publication Year Spectroscopy (RPYS) is a quantitative method for identifying the historical origins of a research field. It creates a temporal profile of cited references for a set of papers, thus highlighting the period in which relatively significant findings were published along with the temporal roots of a discipline.

Figure 5. Annual Spectroscopic Analysis of publications.

The first upturn occurred in 2000, and disruptive technology gained widespread importance during the early 2000s. Ever since then, changes have been observed in how organizations operate and how HRs are managed (Minbaeva Citation 2021 ). Until the early 1980s, 70–90% of the company’s value was linked to tangible assets. However, since 2000, the value linked to intangible assets has increased to 65%, with people being the “cogs in the wheel of intangible assets” (Black and van Esch Citation 2021 ). Two AI techniques are being used in HRM: fuzzy logic and artificial neural networks, both of which aid in the optimal workforce design and performance management.

The second upturn occurred in 2006 when knowledge management became a field of greater importance even though it was already being studied. Since intangible factors had already become more important, there was a greater need for HRM to obtain competitive advantages. Using data mining will be the key to correctly assessing competencies and performance. Through these evaluations, it will promote the exchange of knowledge among employees, along with the generation of new ideas and business opportunities.

Finally, the greatest upturn occurred in 2018, since it is from this year that the study on AI being applied to HRM began gaining importance. The endless possibilities of AI automation generate interest in its application in HRM (Jatobá et al. Citation 2019 ).

Keyword analysis: Keywords are essential for a bibliographic search. Their identification and analysis are crucial for gaining in-depth knowledge of the articles’ content and the topics being analyzed.

The most impactful frequent keywords related to AI application in HRM are AI, HR, Management, and Machine Learning. The importance of the AI concept stands out, but to a lesser extent than that of HRM. AI is experiencing an increase in its application in various fields, but as far as HRM is concerned, it has not yet occurred completely.

Knowledge Structures Analysis

Conceptual structure: It refers to what the science is about, the main themes, and trends. Specifically, multiple correspondence analysis (MCA) helps analyze categorical data to reduce large sets of variables into smaller sets to synthesize the information in the data (Mori et al. Citation 2014 ). To do this, the data are compressed into a low-dimensional space to form a dimensional or three-dimensional graph that uses planar distance to reflect the similarity between keywords.

Cluster 1 (AI in HRM): In this first cluster, the AI tools being applied in HRM are addressed to highlight big data and machine learning. With big data, this might support decision-making processes, since large amounts of varied data from various sources can be quickly analyzed, resulting in a stream of actionable knowledge (Caputo et al. Citation 2019 ). As for machine learning, the last decade has accelerated its use and applicability owing to the availability and variety of data (Hamilton and Davison Citation 2022 ). This type of learning provides systems with the ability to learn (Soleimani et al. Citation 2022 ) and mimic human skills (Bolander Citation 2019 ). Machine learning can learn from the current context and generalize what it has learned to a new context. There are many organizations that, despite not comprehensively using AI in HRM, use this type of algorithm (Nankervis et al. Citation 2021 ).

Cluster 2 (Digital Recruitment): It is the use of ICTs to attract potential candidates, keep them interested in the organization during the selection processes, and influence their employment choice decisions (Johnson et al. Citation 2021 ). Pillai and Sivathanu ( Citation 2020 ) point out how talent acquisition has become a crucial function for HR managers, with organizations going to great lengths to attract the best talent.

For van Esch and Black ( Citation 2019 ), talent acquisition has changed from a tactical HR activity to a business priority. The basis of competitive advantage has shifted from tangible assets to intangible assets, thereby increasing the strategic importance of human capital to make it the key driver. The shortage of talent in the labor market has intensified the need for human capital.

The traditional method of searching for candidates used to be a slow and costly process. However, today, due to technological advances and digital recruitment, it is much easier and cheaper. Furthermore, since nowadays most of society is spending increasing time in the digital space, if companies want to attract and recruit talent, they have to do it in that space (Black and van Esch Citation 2021 ).

3) Cluster 3 (Electronic HR): This cluster presents a much more “futuristic” vision of HR which involves complete digitalization and the use of robots in daily functions.

While electronic HR management stands out in using technology to facilitate HRM processes like, recruitment, selection, training, performance management, human resource planning, compensation, etc. (Johnson et al. Citation 2021 ). Through ICTs, it is possible to achieve better control of performance and over the employees’ behavior for greater strategic and effective management.

Using robots in HRM also stands out. Future forecasts are that in 20 years, robots will be in charge of making some analytical decisions that are now being made by human managers, while humans will continue to be in charge of tasks like creativity (Stanley and Aggarwal Citation 2019 ).

Social structure: It shows how authors or countries are related in a research field; the most commonly used is the co-authorship network (Aria and Cuccurullo Citation 2017 ). The authors who stand out for having the highest number of shared publications are Black & van Esch and McNeese & Schelble. In general terms, there is a high degree of cooperation between authors in the publication of articles, and very few publications are being written by a single author.

In terms of collaboration between countries, the USA is the country with the highest number of collaborations. Whereas with New Zealand and with France, it should be noted that collaboration with the first country is much greater than with the second. Also, there are other collaborations between Brazil and Portugal, China and the United Kingdom, Germany, and Norway-Tunisia.

The research questions initially raised after the results were obtained and the studies analyzed can be answered as follows.

Q1: AI is not yet commonly used in HRM. However, its use has acquired greater relevance in the last five years, with 2021 being the year with the highest number of publications. Authors like Cappelli et al. ( Citation 2019 ) assert that the application of AI in HRM has not advanced as expected. Among the main barriers are: the complexity of HR phenomena, associated data challenges, equity and legal constraints, and employee reactions. Poba-Nzaou et al. ( Citation 2020 ) states that even though the “Fourth Industrial Revolution” again highlighted the need for people to be at the center of organizations, it seems that HR departments remain unprepared to take advantage of this new opportunity. Nankervis et al. ( Citation 2021 ) point out that as technology advances, it will be impossible for the traditional HRM approach to not advance as well; in fact, the forecast is that over the next decade there will undergo a significant change. However, any research article indicates that social entrepreneurship will use the opportunities of Industry 4.0 to optimize its processes until 2030, but will decline complete automation, using human intellect and AI at the same time (Popkova and Sergi Citation 2020 )

Q2: The results obtained show that the literature has largely focused on the analysis of the application of AI in personnel selection. Qamar et al. ( Citation 2021 ) pointed out that, although AI is becoming increasingly important for HR, instead of trying to take advantage of this tool to apply it to the entire people management process, they focused only on a specific sub-area. It is meaningless to attract the best talent if you don’t have the tools to manage it. As highlighted by Nankervis et al. ( Citation 2021 ), the automation of certain complex processes will require increasingly highly trained and qualified personnel.

Q3. The reviewed literature highlights that most employees still do not welcome the application of AI in HRM. Nankervis et al. ( Citation 2021 ) show that many HR professionals lack the necessary skills and competencies to meet the challenges of AI application in HR processes, hence their possible contrary attitude. Fritts and Cabrera ( Citation 2021 ) highlighted the concern of HR professionals against the use of recruitment algorithms, as they can dehumanize the hiring process. Vinichenko et al. ( Citation 2019 ) highlighted how many employees lacked confidence in the integrated use of machines in the management processes because they feared being replaced by machines. However, this is unlikely, since even if some tasks are fully automated, the human factor will not disappear completely (Johnson et al. Citation 2021 ; Kong et al. Citation 2021 ).

Q4. A company gaining a competitive advantage involves several factors like customer satisfaction through quality service, cost optimization, innovation, productivity, etc. The primary function of any technology, specifically AI, is to improve the efficiency and effectiveness of the HR function to help make recruitment, retention, and management easier and more accurate, automate repetitive tasks, and reduce labor costs (Nankervis et al. Citation 2021 ). The innovation processes is a strategical practice in business companies (Bonilla-Chaves and Palos-Sánchez Citation 2022 )

All this will result in an innovative organization full of talent with high labor welfare, which will provide quality service to customers, obtain customer satisfaction, and lead to higher productivity. It has also been observed that the use of AI helps predict staff turnover to avoid the reduction in productivity derived from it.

Theoretical Contributions

Our study contributes significantly to the literature on IA and HRM implications. It is noteworthy how we introduce the framework of previous research on AI and HRM. Through the results obtained by applying the methodology of bibliometric analysis and systematic review of the literature, it has been possible to ascertain the relative and insufficient attention by the academy to these two phenomena together.

Given the lack of similar studies applying bibliometric analysis in this field of study, it can be the first starting point on the same. This will help future researchers as a reference point for expanding and developing the content of this study. It can also be useful for those in HR who want to investigate and learn more about the subject and analyze the current situation to have a minimum number of references in case they want to enter this world.

Practical Contributions

Also important are the practical implications derived from the results of this work for the management and administration of organizations, specifically for HRM. The results obtained provide some very important ideas that can be of great use to HR managers and experts related to the area to understand what the main behaviors and trends have been so far when companies adopt HRM connected to IA.

As noted Pan et al. ( Citation 2022 ), an important fact is a need for managers of organizations to encourage the development and implementation of specific resources in the field of AI in such a way that the adoption of AI in the company is favored.

Limitations and Future Research

This research work, like others, is also subject to a series of limitations. The main limitation has been marked by the dispersion of information and, sometimes, limited to particular issues that do not favor a general view of the topics in a connected way.

Research Agenda

Regarding the main lines of research derived from this work, it is important to highlight the relevance of conducting studies that focus not only on the application of AI in the recruitment and selection of personnel, but also on the rest of the areas in HR management. It would also be opportune to conduct studies that analyze AI’s effect on HRM in the employees of organizations.

The most relevant conclusions derived from the results obtained and their analyses are:

First, there has been an extraordinary development in technology in recent years, especially AI. Despite its development, importance of its impact in the HRM field has not been as expected. AI application in HRM is a very specific field of study, since most of the research has focused on its application in the recruitment and selection of personnel, besides important functions like training, development, or personnel rotation. There is indeed an increasing interest in talent and the recruitment of highly qualified personnel, which is necessary for facing the changing environment and high competition. But it should be noted that talent must not only be found, but also maintained and developed to turn it into a competitive advantage. For this reason, it is essential to use AI technologies in other functions and extract the maximum added value from each process.

Second, based on the results obtained, it can be seen that there are still fears and negative feelings in HR employees and managers about the AI application. These feelings can complicate or slow down the use of AI in this area. Although technology has strongly disrupted the labor market and has helped create new businesses and develop existing ones, it has also eliminated many others, thus causing greater concern. But it should be noted that AI technologies need people for their proper management. Despite being faster, working 24 hours a day, optimizing time and tasks, etc., AI does not have the essential soft skills for any work environment.

Like any new technology, AI has its strengths and weaknesses. This makes it essential for HR departments to carry out an effective AI implementation strategy to integrate it safely within organizations, thus eliminating the potential damage. It is obvious that in the long term, the use of disruptive technologies will no longer be optional but rather necessary to remain competitive among other organizations; otherwise, they will lose their market positions or worse, will disappear.

a 1: ISI Web of Science; 2:Scopus; 3: Business Source Ultimate (EBSCO); 4: Science Direct; 5: AIS; 6: ABI; 7: Journal Quality List (JQL) database; 8: Emerald; 9:Taylor & Francis; 9: Springer; 10: Sage Publications; 11: Massachusetts Institute of Technology Sloan Management; 12: Harvard business; 13: Science

TC: Total citations. PY_start: Year of publication start

Disclosure Statement

No potential conflict of interest was reported by the author(s).

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Artificial Intelligence in the Labor Market

Technology has taken a dramatic leap forward in the last 100 years, taking center stage in every industry. All the people in the world face technological advances: some are more affected by them, while others are confused by technology. Nevertheless, progress is happening almost every day, and the leading player in this field is artificial intelligence. What is this machine, and why has it already infiltrated human life? Where do its competencies begin, and when does its influence end? These are the questions that computer scientists and inventors who want to move progress forward are asking. But more importantly, outline the ethical component and address the central question: will artificial intelligence replace humans? There is no single answer to the question because it affects several areas of human life at once.

Artificial Intelligence is an intelligent machine capable of performing many tasks thanks to unique coding. The coding sets parameters and algorithms that lay the groundwork for functioning, and then the AI builds on them with new ones. This sort of thing seems handy and valuable until the AI begins to perform all of humanity’s functions, from working in a factory to selling a phone. There will probably be no complete mastery of human life by machines, but no one can underestimate the impact of AI. The market economy, political mechanisms, and health care system situation lead to a narrower question: what impact will widespread AI have on humans? Intelligent machines work in many industries and change people’s lives, but what if the moment comes when AI starts to harm humanity?

Artificial Intelligence is no longer a technology of the distant future since it is quickly being improved, and the job market will become the first area to be radically transformed by AI. First of all, it is important to define the notion of AI. According to research, Artificial Intelligence can be described as a computer system capable of executing tasks such as decision-making and prediction (Agrawal et al., 2019). In other words, AI constitutes a machine-based alternative to people’s intelligence and ability to engage in complex cognitive activities. As research shows, the further advancement of AI is expected to cause unemployment in the skilled labor market to increase (Lu, 2021). Such data can be explained by the fact that AI is primarily intended to solve comprehensive issues and tasks, which are usually in the purview of skilled workers.

As a result, the growth in the use of AI will cause more people to change their jobs and seek alternative employment options. Moreover, AI is forecast to replace more than 30% of jobs, including white-collar positions such as market research analysts, accountants, and auditors (Chelliah, 2017). Essentially, millions of people will risk losing their jobs due to the adoption of AI by businesses and will have to experience a career change shortly. Additionally, researchers outline a possibility that AI also, to a certain extent, will replace tutors and educators and will also adjust curriculums to each student’s needs (Chelliah, 2017). Under such circumstances, society has to implement certain mechanisms to avoid facing a considerable socio-economic crisis due to the radical changes in the labor market. Therefore, one of the possible solutions developed by researchers is the introduction of basic-income programs and the imposition of taxes on industries utilizing AI (Bruun & Duka, 2018). Such policies will potentially help governments to reduce the impact of AI advancements on the economy and lives of people.

On the other hand, the very ability of an AI-based system to replace human workers is a matter of heated debate. From one perspective, there are cases of technology taking the place of people, resulting in job shortening. Such a situation has been observed since the end of the 20th century when robots were implemented to work at conveyor belts and other production sites (Borland & Coelli, 2017). Nevertheless, this particular scenario is associated with manual labor, which does not imply intense cognitive activities. Instead, robots handle routine tasks, in which they have an unquestionable superiority over weaker human bodies. Moreover, Borland and Coelli (2017) argue that “the pace of structural change and job turnover in the labor market has not accelerated with the increasing application of computer-based technologies” (p. 377). In this regard, it may be possible that the technophobic ideas are exaggerated.

However, artificial intelligence is an entirely new step toward the use of technology. In an optimal state, its range of applicability extends robots by far, making it possible to delegate even complex planning and decision-making tasks to a computer. As a result, humans will experience a job shortage of an unprecedented level that pales in comparison to the use of robots for manual labor. In this case, the superiority of computers in such positions is not as undisputed. Their computing power and the ability to process immense amounts of information may, indeed, help to make data-driven forecasts and decisions. Nevertheless, even a fully developed AI will lack the inherent features of an advanced human mind, the list of which includes emotional intelligence and reflexivity. This is why Jarrahi (2018) suggests that the key to the future is not a competition but symbiosis. An effective nexus of artificial and human intelligence may form a synergy that will yield outstanding results in the new age of development.

Overall, the continuous improvements in terms of developing artificial intelligence make render this technology close to reality. Previously, it could be seen as an element of science fiction or a distant invention, but now AI is as close to completion as it can be. The full implementation of this technology will mark a milestone in humanity’s scientific development, while profoundly transforming the landscape of most industries. The role of artificial intelligence in labor relations is projected to be of unparalleled importance. In other words, the lives of most people will see an effect of this invention, which is why relevant research is a subject of paramount importance for society in general. As per its definition, an AI is a computer system that can predict various outcomes, performing self-learning and decision-making in addition to the usual preprogrammed task execution processes (Agrawal et al., 2019). In a way, AI is to mimic the pinnacle of nature’s design which is human intelligence.

The development of such advanced technology is expected to cause repercussions for the labor market. As computers become capable of complex cognitive processes, unemployment within skilled professions is likely to increase. Outweighed by the computing power of AI, up to 30% of employees will have to seek another area of expertise, which translates into millions of people. As experts and researchers remain centered on the capabilities of artificial intelligence, a logical question arises of whether there should be human-made limits to it. It is recommended that future research takes into account the full specter of AI’s potential impact on humanity’s employment and professional expertise. While such limitations do not align with the overarching concept of AI, they may be necessary to preserve the integrity of society and help it transition to the new reality with fewer losses. With them in place, humanity and AIs can potentially work together on a predictable basis, forming a positive synergy of efficient, data-driven decision-making.

Agrawal, A., Gans, J., & Goldfarb, A. (2019). Artificial intelligence: The ambiguous labor market impact of automating prediction. The Journal of Economic Perspectives, 33 (2), 31–50.

Borland, J., & Coelli, M. (2017). Are robots taking our jobs? The Australian Economic Review, 50 (4), 377–397. Web.

Bruun, E., & Duka, A. (2018). Artificial intelligence, jobs and the future of work: Racing with the machines. Basic Income Studies, 13 (2), 1–15. Web.

Chelliah, J. (2017). Will artificial intelligence usurp white collar jobs? Human Resource Management International Digest, 25 (3), 1–3. Web.

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61 (4), 577–586. Web.

Lu, C. (2021). Artificial intelligence and human jobs. Macroeconomic Dynamics, 25 (8), 1–40. Web.

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