Human Resource Management and Artificial Intelligence
Author: Sanika Kulkarni
Adoption of AI in HRM
Kaur, et. al. (2021) says that the Human Resource Management is going through a phase of transformation, which is result of the integration of Artificial Intelligence into HR processes. The adoption of AI technology in HRM enables organizations to hire and retain key talent. This study recommends a framework for the adoption of Artificial Intelligence in the Human Resources function. It distributes crucial inputs to gauge factors that impact adoption of AI technology in the Human Resources function. There wasn’t adequate research on the adoption of AI in HR and they identified this as a research gap. The objective of their research is to bridge this gap. This research recommends the model to investigate the incorporation of the technology related to AI in HRM. The study’s contributions will strengthen the theoretical foundation in this area, as it highlights the variables that influence the adoption of Artificial Intelligence in Human Resources function. This research has clearly exhibited that the TOE framework combined with the TAM can be fully used to enhance the evaluative power for technology adoption. The research puts forth a set of variables to be referenced to decipher aspects related to the study of future adoptions of innovations related to technology. The research is of relevance to multiple stakeholders. Future empirical studies, based on the proposed model, could provide insights to top management, leaders, managers, HR teams, and HR professionals, who will benefit from the adoption of Artificial Intelligence in HR. The research adds value to the AI technology developers and vendors. Further, since the proposed model focuses on the adoption of AIT for HRM, from the perspective of HR professionals, future research could be designed to extend the scope of research to the top management, managers, AI developers, and HR application vendors, to bring forth their inputs. This study is a forerunner to the research that could be pursued to explore the impact of AIT adoption related to organizational performance, managerial effectiveness, and employee experience.
Humanizing People Management:
Rogers, (2018) says that the technology has helped HR evolve from an administrative function to a strategic business partner, especially focusing on modifying the way of people management. It is found in a nationwide study that the employee-manager relationship is the key for satisfaction of work. Hence, this paper focuses on ways in which AI can help many of HCM solutions in order to address some of the most common problems in employee-manager relationship, by using machine learning and AI tools to improve human interactions. By putting people first, with the help of evolving HCM technology, it will help companies of all sizes and industries to make life better for their employees, and simultaneously improving the business. AI tools help filter resumes, schedule interviews, and identify the best candidates based on its sophisticated algorithms. Along with this, the advanced sentiment analysis using AI technology helps to enhance human understanding and keep a pulse on the thoughts and feelings of the workforce. Also, Artificial intelligence enables organizations to combine a variety of data sources, such as performance ratings, salary increases, and skillsets to help leaders better understand their people and make more informed decisions on everything from individual development plans to company succession planning.
Digital HR
Wes wu. (2016) says that all the capable HR departments will feature a natural language-based interface, which will be different from simply coding. There is a need of deeply intuitive AI in the background. Humans make errors all the time, but AI now has the ability to correct for those errors. Increasingly, one doesn’t need to know the right questions to ask because the AI will actually be able to assist one in asking the right questions. So, the key components of the future of HR include not only traditional query mechanisms, but also a natural language interface coupled with AI, machine learning, and calculation engines. Instead of being static, such interaction will be interactive. In the HR world of today, connecting productivity outcomes to what staffers were shown in such a class would be tenuous at best. HR today has only the most rudimentary means of eventually determining whether or not the course led to a spike in productivity, let alone sustained improvement. Moreover, any such measures would provide only a static, look-back view of organizational performance. Now HR can query and analyze the data. with fast-arriving technologies, HR will have the ability to evaluate performance against objectives, again, in real-time. Using structured and unstructured data, HR can listen to employees as they go through their day-to-day working routines. Also, rather than waiting for another year before re-engaging with individual employees, HR can identify problem areas and intervene in time to make a real difference in each worker’s career development. Though each of the individual capabilities above is already a reality, only now are various leading practitioners and providers seeking to refine and consolidate such tools into a comprehensive set of solutions for HR. This is indeed advanced HR technology in the current day. But, one must recognize that the pace of technological innovation is so profound that those who have yet to embrace the advances of the past few years are now in a position to do so.
Humanoid robot adoption and labour productivity
Del Giudice, et. al. (2022) concludes that their present research contributes to the existing HRM and management literature by investigating the effect of humanoids’ interaction on labour productivity and by enforcing the continuous relevance of explorative routines in product innovation. Companies are making efforts to be adaptable, that is, adapt to the Industry 4.0 revolution. From a managerial point of view, senior leaders need to interfere in order to promote the most efficient routines and get the best balance among them in a skilful environment or setting. On the basis of their findings, product innovative companies should pursue explorative innovation by developing significantly new goods and services that would fulfil the needs of emerging customers and markets. More in general, this would encourage new research on the role of senior leaders in an ambidextrous context that incorporates the transforming effect of Industry 4.0. For instance, new rewards or a gradual acceptance can be evaluated as a means for embracing AI introduction into a company (Chang, et. al.; Davenport & Ronanki in Del Giudice, et. al. 2022). Also, it is observed that there is a general sense of fear and reluctance towards the use of humanoids. This paper analyses the effect of humanoid interaction on labour productivity, which no significant association between them. On the other hand, it also investigated as to how robot automation impacts other firm dimensions, for e.g. production costs, product quality and design, process organization and control. It showed the benefits that the robotics can be an alternative solution for AIs’ embracement and acceptance. The authors specifically mentioned that Qualitative research can be employed because a deep investigation on this new phenomenon is needed. Apart from this, new analyses can address the need for highly skilled people and how to recruit and engage with them. To conclude, exploiting the existing domain lingers, and the current business scenario provokes the demand for renewal and a transformative attitude (Del Giudice, et. al. 2022). Lastly, this research supported a positive opinion of human robot adoption that has the scope to improve human resource management.
AI in Recruitment process:
Garg, et. al. (2021) emphasizes on the recruitment process, which is one of the most important task for any company. There is a need of error free work in order to avoid any kind of mistake that will result into the recruitment candidate matching the exact profile. AI can be used in 3 steps while following this recruitment process- Sourcing, Screening and Matching. Firstly, the sourcing process involves gathering data of the professionals that can be a perfect match for the opening in the organization. It is one of the task that becomes tiresome for any HR and hence with the AI and machine learning, this task can be shared and the work can be reduced which makes it easy for the organization to get the right list of applicants for the right job. The second one is Screening, it has already laid its hands through ATS system, that is, Applicant Tracking System where few keywords are fed into that system and the resume of the applicants are matched with the help of keywords. The ATS system matches the keywords with the words in the resume and if the content holds the exact words the application is shortlisted. This helps in screening of the resume and makes the task easy to complete within the given time. Lastly, once the list is prepared for the suitable candidates the last step before interviewing is the matching of the profile. ATS tend to shortlist all the resumes that match with the keywords however, exact profiles may not just contain the keywords for the namesake and for that the Matching of the Profile plays an important role. While matching the complete profile resume is scanned along with the required details such as Salary, Location and the Core Competencies. When the HR department conducts the complete procedure it takes a lot of time but when half of the work is done by the AI, it helps in reduction in the workload. Also, AI is not just limited to the recruitment or the selection process, it has found its ways beyond that and has now widen its horizons to the next level of assisting HR teams in performance management and payroll process.
AI based employee recruitment
Pan, et. al. (2022) concludes that AI has the potential to significantly influence the workplace and offer companies a chance to gain a competitive advantage. Their findings provide some important insights that may help experts better understand companies’ AI adoption behaviors. Experts believe that AI will bring great potential advantages to companies, and AI development attracts tremendous levels of investment as a result. However, it is AI’s complexity, not its relative advantage, which influences companies’ AI adoption decisions. Company resources are crucial to AI adoption and yet large companies who possess greater general resources show little difference in levels of AI usage. This is because even mature organizations are not strategically ready for the implications of AI (Ransbotham, et al. in Pan, et. al. 2022). Instead of emphasizing on general company resources, managers should take alternative strategic approaches to increase AI asset specificity and technology competence, which will in turn help to increase AI adoption. HR managers can develop specific HRM procedures and routines tailored to the specifications of AI. It is also important for top management to launch specific strategies regarding AI initiatives. In order to do so, the practitioners and researchers need to understand the constraints and the facilitators of AI adoption. Drawing from the TOE model and the transaction cost theory, they have developed and empirically tested a model of antecedents and boundary conditions for AI usage in employee recruitment. Survey findings from China demonstrate that various elements in the contexts of technology, organization, and environment have direct effects on AI usage, and that transaction costs partially moderate these relationships. The study confirms the importance of government support and relevant technological resources in AI adoption and encourages IT developers to reduce the technological complexity of AI. This integrated theoretical framework will inspire future scholars to seek to better understand technology adoption behaviours in HRM.
Multidisciplinary nature of AI-enabled HRM
Vrontis, et al. (2022) says that they conducted a review of the literature in order to systematize the academic inputs, clarifying what it means for HRM to utilize the intelligent automation. They clarify the complex nature of intelligent automation technologies and HRM at both firm and employee level, focusing on the short-term and long-term positive outcomes and challenges of these technologies at the different levels of HRM strategies and activities. Through their study, it becomes clear that how HRM is progressively shifting from e-HRM towards an HRM defined by intelligent automation. They provide an organizing framework for previous research that draws linkages between AI, robotics and advanced technologies with firm performance and future of employment. They also highlight the role of intelligent automation in supporting HRM and suggested how HRM managers can overcome the obstacles that arise at both local and international level through employees’ involvement in technological implementation processes and collaboration between human and machines. Lastly, this paper sheds light on a number of streams of multidisciplinary research, involving HRM, GM, IM and IB fields. In essence, they consider that the incorporation of intelligent automation in the HRM field is multidisciplinary in nature, and, thus, HRM, GM, IM and IB knowledge domains should be assimilated.
Comparison of AI technology
Jones, (2020) emphasizes on how past AI technology differs from present AI technology. Along with this how changes took place in various field, especially the field of Human Resource Management is what the author mentions in this paper. The previous generations software had a lot of ‘if-then’, that is, if X happens, then do Y. This kind of coding is present in the HR software. But according to the author and other experts, this is not intelligence. It is coded in the software, the steps are prescribed, and will be carried out in the same way until someone revises the software. This is how AI is different: it ‘learns’. Without anthropomorphizing too much, an AI program analyses data – ordinarily a lot of it — then ‘decides’ what should happen next to complete a task. Thus, machine learning is a fundamental aspect of any AI program. Much as AI permeates the social platforms so widely used by kids and adults alike, similar “smart” algorithms are embedded in current HR and people management products. Many talent acquisition solutions today have algorithmic insights that can help recruiters ascertain apt candidates, other algorithms are frequently deployed to suggest career options both to candidates and existing employees within the organization. Based on history, the software is able to predict which applicants are likely to accept a job offer and which employees are presenting current flight risks. Employee education and upskilling are ripe for AI use, by suggesting learning by role, interests, and applicability to trainees. AI just provides a new smart underbelly for applications and processes that are in widespread use in HR today.
Effects of AI-enabled HRM at work
Budhwar, et. al. (2022) visualizes the comprehensive picture of how AI and AI-based technologies affect HRM and human-machine configurations at work and their influences on employee and organizational level outcomes. They extend the knowledge base on the drivers and consequences of the adoption of AI and AI-based intelligent technologies in international HRM and inform the research audience on the growing potentiality for further research. This research area is relatively emerging yet timely needed to be further explored through robust conceptual and empirical research keeping up with dynamic changes of technological advancement and changing business environment. They develop a broad conceptual framework through their literature review. They reviewed the extant literature and derived the main focus areas as four key themes- AI and intelligent technologies in HRM functions, impact of AI-enabled HRM applications on business and employee outcomes, configurations of human-AI-enabled technologies interplay at the workplace, and ethical and legal challenges in using AI and intelligent technologies in an international HRM context. It enables them to focus on AI-enabled HRM functions by concentrating on sub-functional domains, such as human resource (HR) planning, recruitment and selection, training and development, compensation and benefit and performance management, and also analyze how these AI-enabled digitalized functions provided novel opportunities for firms and employees. Also, they found from the current literature that AI-enabled HRM has both positive and negative effects on employees as well as organizations. The favorable employee outcomes are job satisfaction, commitment, employee engagement, and participation, which increases their performance. Along with these positive ones, several negative issues have also been identified. These negative consequences can be high employee turnover, decreasing job satisfaction, loss of customer satisfaction, incurring high costs, and eventually affecting organizations’ overall business performance and goodwill. Apart from this, some ethical and legal challenges are also identified concerning ethics, accountability, trust, fairness and legal implications of using AI-driven technologies and autonomous systems at workplaces.
HR in managing the Amoeba
The workforce of the future cannot be thought of independently from those other people, those other entities, and those other things, that is, robots and AI doing work on behalf of an organization. All of them affect the employee experience, the organization’s culture, its brand and its overall efficiency, effectiveness and success. If HR is serving as the facilitator of work design and execution, is taking it out of its lane. Not at all. It is expanding its scope and influence, yet it is making the function more relevant, powerful, and impactful. Additionally, no other function has yet filled the demand for this role and no other function is as well-suited to do so. Thus, as more organizations choose to use employee experience design techniques, the opportunity arises to ask and answer some questions regarding the work that is needed to be done, the capability of work, the capacity of the work within the stipulated time, the work plan, and the workplace and location strategy. All interact, thus contribute to the culture. It would be beneficial, but the functional thinking has anchored what’s possible to an old mindset. This mindset has to change. It has to shift to a more integrated, systematic way of thinking, one that looks at a diverse array of how work can get done. The amoeba is changing even more rapidly, and like an amoeba, an organization’s primary mission is to survive, and to survive, it must adapt.
Conclusion:
The integration of artificial intelligence (AI) in Human Resource Management has, thus, led to significant advancements in various aspects of HR functions. AI technologies such as machine learning algorithms and natural language processing have enabled HR departments to automate routine tasks such as resume screening, candidate sourcing, and scheduling interviews. This has resulted in increased efficiency and time savings for HR professionals. Hence, AI has the potential to revolutionize HRM by streamlining processes improving decision making and enhancing the employee experience. However, its successful integration requires careful planning, continuous monitoring, and a strong focus on ethical considerations to harness its full potential while eliminating the risk factors.
References:
ADAMSEN, A. (2017) The Workforce of the Future: HR’s Role in Managing the Amoeba. Workforce Solutions Review, [s. l.], v. 8, n. 3, p. 20–23, 2017. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=8d2a6aa9-600a-3fff-ae54-d147bc38bdab. Acesso em: 20 fev. 2024.
BUDHWAR, P. et al. (2022). Artificial intelligence – challenges and opportunities for international HRM: a review and research agenda. International Journal of Human Resource Management, [s. l.], v. 33, n. 6, p. 1065–1097, 2022. DOI 10.1080/09585192.2022.2035161. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=43f7716e-45f3-3843-b16c-edc88c475161. Acesso em: 14 fev. 2024.
DEL GIUDICE, M. et al. (2022) Humanoid robot adoption and labour productivity: a perspective on ambidextrous product innovation routines. International Journal of Human Resource Management, [s. l.], v. 33, n. 6, p. 1098–1124, 2022. DOI 10.1080/09585192.2021.1897643. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=025f9450-db6f-3e9e-92bb-fc2c3779090a. Acesso em: 20 fev. 2024.
GARG, A.; GAUR, S.; SHARMA, P. (2021) A Review Paper: Role of Artificial Intelligence in Recruitment Process. ANWESH: International Journal of Management & Information Technology, [s. l.], v. 6, n. 1, p. 33–37, 2021. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=badeef8d-9077-33a4-a7b1-ee5572491dda. Acesso em: 19 fev. 2024.
JONES, K. (2020) AI in HR: From Transactions to Innovation. Workforce Solutions Review, [s. l.], v. 11, n. 4, p. 10–11, 2020. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=b9c6694d-eb39-347c-bb5e-3921a8c47ce8. Acesso em: 14 fev. 2024.
KAUR, M.; A. G., R.; VIKAS, S. (2021) Adoption of Artificial Intelligence in Human Resource Management: A Conceptual Model. Indian Journal of Industrial Relations, [s. l.], v. 57, n. 2, p. 331–342, 2021. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=41c3c9b3-e528-3d8f-a698-266ae2505b17. Acesso em: 14 fev. 2024.
PAN, Y. et al. (2022) The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. International Journal of Human Resource Management, [s. l.], v. 33, n. 6, p. 1125–1147, 2022. DOI 10.1080/09585192.2021.1879206. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=be2b352e-4df2-3b2c-9b99-45580c3bd5fc. Acesso em: 14 fev. 2024.
ROGERS, A. (2018) How AI is Humanizing People Management. Workforce Solutions Review, [s. l.], v. 9, n. 3, p. 25–26, 2018. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=3ee55f5a-47d2-3227-8ef0-742d3efd58f5. Acesso em: 19 fev. 2024.
VRONTIS, D. et al. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. International Journal of Human Resource Management, [s. l.], v. 33, n. 6, p. 1237–1266, 2022. DOI 10.1080/09585192.2020.1871398. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=4199ccc4-7290-35c9-aa8f-f4b1ae475a0f. Acesso em: 14 fev. 2024.
WES WU. (2016) Digital HR: The Business of People is fast-becoming the Business of Bytes. Workforce Solutions Review, [s. l.], v. 7, n. 5, p. 16–19, 2016. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=b14a5c83-c7e7-3083-b775-f6525047677c. Acesso em: 19 fev. 2024.
