Coalesce of artificial intelligence and human resource management: a conceptual study

Introduction

Human resource management (HRM) was first connoted at the end of the 19th century. It is an enactment of various activities such as recruitment, hiring, assigning, and superintending the employees of an organization. As a result of globalization, HRM has laid its feet into strategic ingenuities such as industrial relations, expansions, diversity, inclusions, performance management, mobility management, training, and development too, though it was initially dominated by payroll and administration only. Every organization has a designated team that works on the human capital requirements and is accountable to govern policies in order to maintain a cohesive environment in the organization. Global competition has increased the need for HRM automation with a supposition to curb various challenges. Artificial Intelligence (AI) has been a captivating topic for research since the 1950s. It is the intelligence displayed by machines in contrast with the intelligence displayed by sapiens. AI is a booming field of application that serves the aforementioned purpose with “Human Resource” being one of the industries affected by AI. The emergence of brain jobs has paved the way to utilize AI in various arrays of HRM, thus reducing monotonous human intervention. The contemporary study discusses various supremacies of blending HRM practices and AI by developing a conceptual framework. “Machine-being” teamwork is studied for further scope in application along with an effort to comprehend the confines and ways to overcome the same.

Literature review

The researcher has poured exertions to diligently review and recapitulate existing and relevant literature. Literature was derived from eminent databases such as Research Gate, Emerald, Elsevier, and Science Direct. Referenced journals and research articles encompassed in the study include management books, published articles, journals, study conducted by corporate firms, reports related to the influence of AI on various fields of HRM, practical application of AI, benefits of AI implementation to the organization, and so on. Studies conducted by authors abroad are also included along with Indian authors to ascertain the imminent applicability of AI currently lacking in the Indian system.

The study entitled “Artificial Intelligence in Human Resource Management” by Merlin and Jayam (1), published in the International Journal of Pure and Applied Mathematics, mentions about varied potentials of AI in greatly renovating Human Resource tasks such as recruitment, employee/talent management, training, and development including employee retention. This study cites real-time cases to substantiate the same along with defining communal of Human Resource and AI. Secondary research of this study also states insights into the coalesce including the impact of AI implementation on Human Resource practices.

The study entitled “Recruitment through artificial intelligence: A conceptual study” by Geetha and Bhanu Sree (2) has the objectives of understanding the role of AI in the recruitment process, reasons to justify the adoption of AI in HRM, identification of possible business outputs of AI implementation, and necessary conditions for man–machine collaboration. It identifies eight ways of implementing AI in recruitment, namely, Screening Candidate, Candidate Engagement, Re-Engagement, Post-Offer Acceptance, New Hire On-Boarding, Career Development, Employee Relations, and Scheduling. In addition to the above, it mentions AI implementation saves time and costs of recruiting for the organization laterally improving the quality of hire and ensuring unbiased recruitment. Particular research is completely based on secondary sources of data such as journals, conceptual studies, and websites relevant to the topic.

The study entitled “The Future of HR in the presence of AI: A Conceptual Study” by Rana (3) discusses about the extensive usage of AI in differential functions of HRM along with deliberating the diminishing interference of humans in the organization’s functions. It also highlights the constant fear and threat to HR professionals of being replaced by technology, robots, or machines. Contemporary research tries to high point the improvement in the decision-making process of the organization by implementing AI and facilitating better appropriateness. This study strongly propagates that AI should only be considered a supportive structure to perform functions of HRM and not as an alternative to human workforce. The paper also recommends study on AI-powered functions in HR and structured differences between “before and after implementation of AI” in the system.

In the study entitled “Artificial intelligence in human resources management: Challenges and a path forward” proposed by Cappelli et al. (4), a detailed discussion on actuality and assurance of AI in the field of HRM is provided. The study identifies four challenges in the implementation of data science techniques in HRM, namely:

a. Restraints caused by comparatively small data sets

b. Intricacy of HR processes

c. Fairness and ethical and legal liabilities

d. Resistance and opposing employee responses to management decisions based on algorithms.

Considering AI lifecycle, causal reasoning is the first principle followed to address the aforementioned challenges followed by randomization and experiments and employee contribution.

The study entitled “Realization of Artificial Intelligence in Human Resource Management Best Practices,” published in Mukt Shabd Journal (5), implies the application of AI in HR as the purpose of the study and substantiates it through secondary data in addition to relevant case studies of an organization. It mentions recruitment, selection, and performance management as areas of HR where the application of AI is vital and argues that training, development, and compliance are fields of HR which require the essence of human touch and compassion, thus concluding that application of AI in these fields is a challenge. It highlights the drift of organizations toward AI implementation in data collection that generates reports online and eliminates human intervention in reporting. Research also states the fear residing in employees regarding the automation of processes and machine learning as it is perceived to replace human work. Organizations have to convince employees to view AI as a growth-contributing factor and not a threat. The study also identifies a need to upgrade the skills of employees in the organization for the smooth implementation of AI technologies, thus creating a WIN–WIN situation.

In the study entitled “Effects of artificial intelligence on human resources management – A study of IT sector in Pakistan,” Khushnooda (6) mentions how big firms in the industry explore and adopt AI in their HR operations and try to outline the possible potential applications. This report is based on various secondary sources of data including primary research of “Cognition X’s directory of AI-powered HR”—products, interview responses of experts, and survey results. It also outlines the budget required for the implementation of AI technology equipped enough for ROI generation, technical expertise required by HR personnel, and various other ethical implications. However, it concludes that, for an organization to survive the cut-throat competition, digitalization and incorporation of the recruitment process are mandatory.

The study entitled “Artificial intelligence in recruitment: Assessing flipside” by Mirji (7) has discussed the contrary with reference to other research papers. This study has followed a phenomenal approach and is based on qualitative data. It discusses the various possible deficits of AI implementation such as errors in language processing, skepticism, improper commendations due to data insufficiency, and so on, which is in complete divergence from this study. Opinion of human touch being necessary in fields such as training, employee relations, and budgeting is argued upon along with proposing a framework for efficient man–machine collaboration in the organization. Further scope of the study is prescribed for software developers to study the development of efficient AI tools for recruitment and carefully understand its limitations.

The study entitled “The Janus Face of Artificial Intelligence Feedback: Deployment versus Disclosure Effects on Employee Performance” by Tong et al. (8) discusses how rapidly organizations are deploying AI-powered software for employee performance management/feedback by automating performance appraisals and analyzing employee behavior and recommend further job enhancements. Debating on two aspects, “deployment effect” discusses how AI-powered data analytics enhances the quality of feedback and also employee performance and “disclosure effect” discusses the probable harms to employee productivity due to negative perception of AI feedback. Aforesaid are theoretically examined and empirically tested with data from field experiments as a base. The study confirms the co-existence of both the effects with robust evidence from the study and how adverse implications of “disclosure effect” can be mitigated through proactive communication with employees about the objectives and scope of AI implementation. Implementation of AI in tiered custom is suggested as it presumes that the adverse effect of disclosure varies between veteran and novice employees.

Research methodology

This study is conceptual and descriptive in nature. The data are collected through several research papers, websites, HR blogs, survey reports, and other publications. This secondary research has made an attempt to capture the essence of AI in all the activities of HRM. Furthermore, the study tries to deliberate on different issues that come up in the process of blending AI and HRM.

Objectives of the research

The objectives of the research are as follows:

• To understand the role of AI in HRM

• To highlight the application of AI in various activities of HRM

• To recognize the challenges in the implementation of AI in HRM activities

• To suggest ways to overcome the challenges in the implementation of AI in HRM activities.

Artificial intelligence is a part of computer science that accentuates making of intelligence technologies that perform or mimic human behavior. Also, to be mentioned is a subdivision of AI—Machine Learning which has a niche focus on data interpretation and learning by computer programs. With growing competitiveness on a global scale, it is imperative for every organization to adapt to the recent advancements in the market. As a result of this, HRM has witnessed an enormous wave of modernization as automation of HR practices subverts repetitious processes concerned with its operations (Hmoud and Várallyai, 2020). Implementation of AI endures friction in the organization as it paves the way to technical employment over human employment and possesses an immense need for technological upgradation (Khatri et al., 2020). The triumph of an organization is reliant on technological implementation and digitalization. Various techniques of AI have a significant impact on many HR activities such as recruitment, selection, performance management, training and development, and compensation management [24]. The research discusses diverse ways of amalgamating HR practices and AI thus gaining a competitive advantage. For the aforementioned purpose, distinct activities of HRM are classified into the below areas, and applications of AI are explained consequently:

1. Recruitment and onboarding

2. Workforce management (WFM)

3. Learning and development

Recruitment and onboarding

Recruitment is a fundamental function of Human Resource that involves aspects from identifying of talent to onboarding of employees. Attracting talent, shortlisting profiles, interviewing, and hiring are in between this process. After India witnessed technological advancements, recruitment slowly started to drift toward scientific ways and got popular in the name of “e-recruitment.” Numerous corporate websites (Naukri.com, Monster.com, etc.) and job commercials that provided a platform for hiring were utilized in order to speed up the recruitment process in addition to traditional consultancies. David Mallon mentioned recent technologies like Robotic Process Automation are capable of taking away human intervention in routine work with respect to the human resource department as it mimics human skills. Gradually, a colossal change in HRM functions is observed concerning job description, job specification, job analysis, applicant screening, onboarding process, competency mapping, performance management, compensation, and so on. With the utility of AI, an organization can ensure a prominent recruitment process at reasonable costs along with making the recruiters more agile and efficient (Chanda, 2019). The prevailing study is in parallel with the above-said literature. Going forward, the research enumerates AI implementation at various phases of recruitment.

Process of recruitment

Recruitment planning refers to a controlled approach to hiring employees in an organization (Figure 1). Planning draws timelines for organizations to recruit competent applicants for the accomplishment of goals without instigating a slowdown in the process (Breaugh, 2008). Effective recruitment ensures cost-saving and lower time lag between prerequisites originated by vacancy created and applicant placed (Price, 2000). “You cannot find what you need, if you don’t know what you need.” Hence it involves all the necessary information concerning a vacant position. AI has the capacity to maintain huge databases with the help of big data and cloud technologies because of which it gauges the requirement of the vacant position. Big data denotes voluminous, hard-to-handle structured and unstructured data that deluge operations on a daily basis. Big data demonstrates how efficiently data can be analyzed to enhance strategic business decisions based on 3Vs—Volume, Variety, and Velocity.

• “Is the job newly formed or recently emptied?”

• Job description and job specification—defines KRA’s and skills required

• Analyze gaps in the current team—to curb the gaps by hiring new talent

• Analyze workload—to ensure efficient bandwidth maintenance.

Artificial intelligence has drifted the way of human resource with its constructive features such as big data and analytics that provides eventual utility to recruiters by automating and streamlining intricate and repetitive time-consuming tasks at every stage. Sentiment analysis, a technique of Natural Language Processing is utilized to draft error-free job descriptions (Abhishek and Agarwal, 2017). Sentiment analysis is an opinion mining technique used to understand whether data are positive, neutral, or negative.

Going forward, we investigate and suggest various AI techniques that can be employed in recruitment along with its benefits. AI provides a talent intelligence platform that can be otherwise understood as an applicant tracking system that builds a strong pipeline of candidates for current and future roles. Real-time updating of these platforms provides increased visibility and quick optimization to recruiters. With the help of AI and ML intuitions, ‘Intelligence Heuristics’ ranks candidates based on differential parameters such as availability, employability, and recency or a combination as preferred by the recruiter. Talenture is one such platform that performs candidate sourcing from career sites, social platforms, job boards, and so on. In addition to it, it also accelerates the candidate’s decision-making by providing a 360-degree view of business relationships. AI recognizes the prospective candidates with right skill sets either through a targeted campaign or an organic search action and persuades them to apply for open requisitions.

Along with the above, recruitment Chatbot is a great boon to recruiters. Chatbot is a conversational interface that acts like an instant messenger window that carries out the basic filtering of candidates based on qualification, work experience, notice period, and so on, along with answering Frequently asked questions, thus converting the passive candidate pool into the prospective pool. Interview scheduling is also done automatically by analyzing the calendar of candidates and recruiters.

“Touchless hiring” has been a recent tendency with the help of AI due to the following reasons:

• Voice-enabled personal assistance to candidate

• One-way video interviews

• Machine vision

• Typing assessment (sentiment analysis)

• AI proctoring.

Candidates with the most exceptional potential only are shortlisted, and time and resources are utilized to interview them eliminating the dangling profiles. The unique feature of AI-powered technology unlike traditional techniques is the “Post-offer engagement” offered. Post-offer engagement refers to being in constant touch with candidates post-offer, thus analyzing the risk of candidate flight or offering dropout with the help of sentiment analysis.

Workforce management

Workforce management is a cohesive set of procedures that an organization uses to augment the efficiency of employees. It involves numerous functions within the broader outline of HRM such as payroll, compensation, and benefits, forecasting labor requirements, employee engagement, performance appraisal, leave management, bench management, and so on.

Payroll management

Payroll management is one of the repetitious tasks performed by HR personnel. With the technological touch of AI, it is easy for personnel to keep a check on payroll operations. The process is highly time-consuming, and large volumes of data need to be processed. These task leads are overwhelming and result in errors if adequate tools are not employed. AI tools and automation platforms offer smart data management that addresses these issues more conveniently. By employing application program interfaces, facilitation of data transfer between employed payroll software and administrative system database is done. Contemporary data can be scrutinized and analyzed to obtain spontaneous, error-free reports without human interference. Using algorithms, it is tranquil to spot anomalies in payroll as it relies not only on the decision of payroll analysts but also on historic data. By analyzing payroll history, algorithms calculate earnings, deductions, and taxes of an employee. Any significant disparity will be identified as an anomaly that needs to be resolved. Exclusive dashboards provide an overview of the payroll that shows the following:

• Number of normal runs

• Number of anomalies

• Anomalies resolved utilizing business rules

• Anomalies considered acceptable.

Artificial intelligence-powered roster management has facilitated managers to automate roster maintenance process by analyzing attendance records, work patterns, and employee performance in the past, thus paving the way for overall optimization. Staffing requirements are accurately predicted considering numerous factors such as workload, stress levels, and so on so that managers can plan whether to hire additional workforce or manage with existing ones.

Compensation and benefits

Compensation framework is an imperative aspect of the organization that demands cautious consideration of various factors as it plays a major role in retaining top workforce and increases employee motivation and job satisfaction, thus enhancing the productivity of the organization. With careful analysis, AI provides data analytics on employees’ skills, market rate for skills, current and future demand for skills, and so on, thus assisting the organization to decide a mode of rewarding skills (base pay rise or bonus). Initiating AI-based compensation paves the way to saving several work hours spent on manually handling compensation cycles along with eliminating any bias and personal prejudice in the compensation process. Few benefits of implementing AI-based compensation management are as follows:

• Impartiality

• Customization

• Cost reduction

• Responsive programs

• Enriched employee experience

• Retaining top talent.

Artificial intelligence, through data analytics, ensures “right compensation for the right position” that evades paying too less or too much. There is a deliberate need for every organization to work tactically and understand competitor trends so as to counter them efficiently. AI modifies the way compensation is determined. Organizations need to rely on larger databases to create strategies that satisfy the differential expectations of employees with various skill sets and roles. AI provides in-depth vision to market insights and premeditated view of role banding by analyzing competitor data and relative trends of the local market.

Organizations have started to benchmark their compensation practices by integrating them with machine learning. With the help of AI, it is easy for organizations to identify in prior-flight risks of employees and administer compensation in such a way that employee turnover can be mitigated. Cost of replacing an employee will cost nearly 30% of their CTC. To exterminate this risk, AI data-driven recommendations make sure that the finest talent is retained in the organization by means of accurate rewarding. Employees are empowered to use the chatbots and get their queries addressed in real time. Manpower involved in these monotonous support activities is reduced in addition to a prodigious increase in HR responsiveness.

Bench management

Employee “bench” refers to employees on organizational rolls who are currently not working on any billable projects of the company but are still entitled to receive the compensation and benefits of other employees. “Bench” is a blessing to employees and a disguise to employers as it generates an idle workforce in the organization who are neither working nor developing any skills. It is an agony to the organizations as the employees cannot be terminated keeping future requirements in mind nor let them stay on bench for long. AI-supported bench management has algorithms to understand the requirements and also analyze the skills of employees on bench. AI effectively matches the employee and requirements also gauging the matching percentage. Depending upon the matching percentage, the hiring manager can decide on considering a near-fit or waiting for an exact fit. “Urgency detection” technique of AI can be employed here. The hiring manager can list the requirement with a timeline before which the position has to be filled and AI software provides a list of employees on bench or who will be coming available on bench including their skill sets. Unnecessary external hiring costs can be eliminated through this, also reducing the idle workforce.

Learning and development

Learning and development was the most disregarded zone in any organization. At the present time, organizations have realized how dictatorial and beneficial learning can be to the organization and have started to implement innovative ways of learning through AI-blend. World Economic firm forecasts that the automation of processes will displace nearly 75 million jobs and generate 133 million jobs worldwide by the end of 2022. Hence, professionals are left with no choice other than to adapt to the recent technology, or else will be considered obsolete.

Organizations have mandated minimum learning hours for every employee based on their grades to ensure continuous learning, eliminate skill gaps, and retain top talents within. With the help of AI, Learning Management System (LMS) can get data about employee’s individual learning interests, skill gaps, work style, and allocated projects and provide appropriate course recommendations to increase the competency of the employee. Personalized learning is delivered in a promising manner leading the organization toward competitive advantage. In addition to this, collaborative learning can be facilitated with AI tagging. AI tagging expedites cross-functional learning, thus smoothening organizational operations and excelling team performance. AI reduces the hardship of administrators by developing curriculums based on line of business, role expectations, and so on along with apprehending learning outcomes. Leadership has narrowed its efforts of managing learning efficiency as it is automatically extracted by AI algorithms and Metadata. Content uploaded on LMS is enriched through AI technologies that provide the following advantages:

• Easily locatable training modules

• Accessibility

• Re-visitable content

• Flexibility

• Improved course recommendations

• Identification of skill gap

• Return on investment

• Integrated “work-learning.”

Learning is made more flexible as it can be accessed anywhere irrespective of location and time which helps to reduce “learning anxiety” in employees. Every individual’s inclination to learning is clearly understood by visualizing dashboards. Any skill gap identified is curbed through undifferentiated learning, thus aiding the organization in strategic skill acquisition.

With the help of LMS, learning is happening at both individual and organizational levels. L&D programs lay better personal and career development paths at the individual level. Recognition of skills also helps the organization in

• Succession planning

• Smart learning

• Personalized attention

• Up-skilling of employees.

Artificial intelligence is one of the gifted technologies that can transform learning and development. Several organizations have already set aside investments for the implementation of AI in their systems. AI has proved beneficial showing a massive rise in the rate of course completion ensuring efficient knowledge transfer. AI has not only enhanced learning but also increased interactivity, efficiency, and engagement.

Challenges: AI and HR amalgamation

Substantial research has been conducted on challenges tangled with implementing AI in HR-related activities from various perspectives. According to a paper published by ESSEC Business School and Wharton School, it is proven that there exists a gap between what AI promises to perform in the field of HR and what it actually does. Though it is agreed upon that AI is a solution to various challenges, it paves the way for various other modern challenges that require modern solutions. Mounting admiration of AI is forcing organizations to allocate funds for implementation. Implementation of AI is not hassle-free, and they could be any of the following points discussed below:

Determining data

As mentioned earlier, AI performs decision-making functions based on the data fed to the system. Organizations have to take utmost care to leverage quality data without which AI implementation is an aggravation. Justifying “Which, When, why?” will help in filtering the most appropriate content among various data sets of the organization. As AI lacks human touch, improper data can lead to drastic mistakes in the decision-making process. Scarce or biased data could lead to inaccurate recommendations by algorithms.

Infrastructure

Infrastructure is a set of vital facilities and structures that supports the efficient functioning of a firm. AI strains substantial resources and infrastructure along with accumulating costs as the technology becomes more complex. As Hybrid Cloud Solutions (HCS) are considered a foundation for AI, it requires a considerable amount of data notch up. HCS ensures that technology matches the organization’s workload and demands along with ensuring that it is accomplished at an appropriate cost level. The high computing capacity of a system is a mandate to take utmost advantage of AI. The necessity of graphics processing unit (GPU) is greatly necessary over central processing unit as GPU accelerates higher workloads and data sets. A challenge is that deep learning involves installing scalable neural network algorithms and high-performance storage systems. Networking is one more mandate of AI infrastructure. As algorithms are highly reliant on networks and communications, scalability is of intensive rank. Low-latency network and high bandwidth are obligatory. Along with the aforementioned, a high cost of maintenance is involved to stabilize operations. Integration of AI with the existing system turns into a major hurdle without efficient infrastructure.

Data privacy and integrity

Though the implementation of AI is a boon, it rolls along like a snowball with unexpected issues. One such issue is data storage. AI-based decision-making is prevalently based on huge amounts of data. This gives rise to storage issues and an added responsibility for data security. As AI handles sensitive data, an organization has to be extremely careful about various cyber threats and viruses that are dominant and allocate funds for securing them through firewalls or anti-virus software.

Ineffective change management

Employees of the organization must be trained prior to using the integrated AI systems without which it is a failure. Organizations have to set expectations with employees by conducting relevant training and development programs that keep them updated on the implemented technology. Keeping aside the technological aspect, employees consider AI as a threat to their jobs and might pose resistance to change themselves according to the need. Concerned departments have to keep the employees well-informed and educated through effective communication. Organizations will have to designate an efficient change management team to cope with these challenges and make implementation effective.

Financial barrier

Implementation of AI in organizational systems is usually postponed in small organizations due to a lack of funds required for training, change management, technological requirements, or acquiring necessary software related to the same. In most of these cases in the absence of skills in-house, businesses are forced to outsource process, which is more expensive. The complexity of data makes technology expensive, and smart technologies tend to accrue additional costs of maintenance. The cost of developing and implementing a training module cannot be ignored. Organizations that are middle/large scale can consider implementation through proper planning and allocation of funds along with maintenance. Regular upgradation of software is an inevitable expense without which there is a risk of data breach as restoring is more costly and time-consuming. These risks can be mitigated only through proper budgeting and efficient application of funds.

Human interference

AI is completely based on the analysis of data that is fed into the system. Though accurate, it lacks emotional touch. AI lacks the ability to read an individual’s viewpoint irrespective of whether they are employees of the organization or prospects. Hiring is based not only on technical skills but also on various other psychological traits. In spite of sentiment analysis, AI cannot accurately gauge human emotions, passion, and ambitions. Decision-making is largely based on skills, ignoring if the person suits the dynamics of the team and how well his/her personality amalgamates to contribute to group cohesiveness. Thus, it is most effective when there is an appropriate utilization man–machine collaboration and not only machine decisions.

Ethical and legal constraints

Ethical prospect is another major area that cannot be ignored. Some of the ethical issues include generation of less jobs, elimination of current jobs, compulsion to train employees to keep them aboard, wealth distribution, adverse effects of man–machine collaboration, algorithmic bias instigating out of human bias in the data fed to the system, safekeeping of AI systems that pose a potential threat, and so on. Though AI systems learn and develop independently, there is a great need to mitigate unintentional consequences through responsible implementation and gain a competitive advantage.

Legal issues can range from lack of accountability for tribulations to accountability issues. Unfairness, bias, lack of algorithmic transparency, intellectual property rights issues, data protection and privacy, human aspect-adverse effect on employees, cybersecurity threats, and discrimination are the common legal matters of concern and can be handled only through careful consideration of root causes of vulnerabilities and effective ways to curb them.

Ways to overcome the challenges in implementation of AI in HRM activities

The world economic survey has indicated the following aspects which limits an organization’s ability to adopt AI tools. The respondents indicated the following as their concerns:

• 57% of the respondents indicated a lack of experts in the field of AI

• 45% of them indicated cost of implementing AI tools

• 46% felt quality of data and its availability is a concern

• 41% of the respondents were concerned about the effectiveness of the organization

• 34% were apprehensive about the bias in AI

• 49% were anxious about privacy and security issues

• 46% also felt adaptation of AI tools could impact the organizational culture.

The fear of using AI is there among all the stakeholders. Resistance to change is a common aspect when anything new is introduced. A planned change is the need of the hour for AI tools implementation.

Involvement of the key stakeholders will enable them to understand and apply the AI tools appropriately. The system should build trust and confidence among the users to ensure a smooth transition to AI. There is a need to educate the employees on the importance of data-driven decision-making and the opportunities provided by AI for optimization. With the pandemic around, organizations are making use of the recent technologies for various activities in the firm. Hence, it becomes essential for the policy makers/top management to help in developing robust HRM practices that can utilize and adopt emerging technologies.

Conclusion

Artificial intelligence will play a major role in the years to come, and all the tools of AI will become indispensable in the functioning of organizations. AI will assist in discovering unique capabilities, finding hidden patterns, converting uncommon terms, making the system smarter, and bringing in consistency. AI will assist HR activities such as Talent discovery and Employee referral network, match employees to knowledge, skills, and abilities, background verification, track employee development and employee engagement, and many more. AI will help both the sourcing activity and people who are looking for jobs. Especially in the field of Recruitment, AI has proved its importance time and again. Implementation of AI must be viewed positively and should be used as a tool to make quicker decisions in all the functions of HRM. With the technological revolution around, it is imperative to maximize the benefits of AI, but with adequate human intervention to critically evaluate the unique features and the decisions taken by AI.

Websites

• https://www.weforum.org/agenda/2021/12/how-to-keep-human-in-human-resources-with-ai-based-tools/

• https://www.sas.com/en_in/insights/big-data/what-is-big-data.html).

• https://analyticsindiamag.com/10-ai-based-recruiting-tools/

• https://www.oracle.com/a/ocom/docs/applications/hcm/oracle-ai-in-hr-wp.pdf

• https://sightsinplus.com/practices/rewards/the-impact-and-potential-of-ai-in-compensation-benefits/

• https://www.beqom.com/artificial-intelligence-driven-compensation#:∼:text=Artificial%20Intelligence%20based%20on%20Machine,driven%20compensation%20technology%20can%20mitigate

• https://www.azilen.com/blog/payroll-management-using-advanced-ai-capabilities/

• https://www.ibm.com/downloads/cas/AGKXJX6M

• https://blog.leaseweb.com/2019/07/04/infrastructure-requirements-ai/

References