HR Technology

Sep 08, 2022 11 min read

How to Harness the Power of AI in HRM


Abstract: The rise of Artificial Intelligence (AI) presents a confounding dilemma for HR professionals. On the one hand, AI has tremendous potential for HR to improve workflows, enhance productivity and enable data-driven decision-making. On the other, AI-based tools can lead to unfair discrimination, lack of transparency, breach of privacy and erosion of employee trust. Given these contrasting aspects, HR managers should take a balanced approach while implementing AI-based HRM. This article provides a framework that can guide HR managers in this process. The model considers the employees' perspective and a firm's stage of AI technology assimilation. Any decision to transition to AI should be contingent on the inherent complexity of the HR activity and the level of technological readiness. By channelling their focus, HR managers can provide a great employee experience and ensure better decision-making.

Keywords: Artificial Intelligence, Human Resources, Ethics, Employee Trust, Technology in HRM

Artificial intelligence (AI) is the ability of machines to solve problems or make predictions based on massive volumes of data in complicated, structured as well as unstructured environments (Prikshat et al., 2021). In general, AI refers to a broad class of technologies that allows a computer to perform tasks that ordinarily require human cognition (Budhwar et al., 2022). AI-based tools have a wide range of applications for essential HR functions like recruitment and selection, performance management, training and development, and coaching (Dutta et al., 2022).

The nature of the HR domain makes it different from many other fields where AI applications have been used. Tambe et al. (2019) highlight three reasons: (i) the complexity of HR outcomes, (ii) scarcity of data for accurate modelling, and (iii) legal and ethical repercussions of HR decisions that make implementing AI-based tools in the HR domain difficult. Moreover, recent research has debunked the claim that AI applications are de facto-neutral (Prikshat et al., 2022). Research has found AI techniques to exacerbate power imbalances, inequalities and a culture of discrimination (Sloane, 2018). Besides, the technical naivety of most HR professionals makes AI implementation problematic. Generally, organisations outsource the technical task of creating such tools to developers who often lack the necessary domain knowledge. This lacuna creates a mismatch between the desired and actual outcomes. Many a times, HR managers find themselves in a spot of bother when they are required to explain the outcomes of such tools to employees and other stakeholders when they are unaware of the inherent assumptions used during development. Sometimes, organisations procure the technology from an outside vendor. The problem gets quite complex when large organisations have to implement different parts of HRM from multiple vendors.

HR Technology applications have traditionally focused on improving process efficiency and reducing costs. Consequently, human touch points in employee processes in the organizations are becoming lesser and lesser. It appears that employee perspective has been largely ignored while incorporating technology in HR. Taking feedback from employees and understanding their expectations regarding AI is crucial. The general assumption that employees expect their organisations to be as technologically savvy as possible should not be generalised to all HR functions. Indeed, employees would expect automation for mundane, regular activities, such as downloading payslips or knowing their medical insurance status. However, they may prefer human interaction for more personalised transactions like reporting a grievance or discussing an appraisal-related issue.

In a study on employees' perception of AI-based interview evaluation, Mirowska and Mesnet (2022) have shown that employees desire the maintenance of human elements in the interview evaluation process. During their research on gig workers, Duggan et al. (2021) found algorithmic management detrimental to individuals' long-term careers. AI-augmented HRM applications in different functions raise concerns regarding privacy, fairness, confidentiality, predictability, relatability and validity (Prikshat et al., 2022). HR managers must therefore be mindful of using an AI "black box" for decision-making that can affect employees' perceptions of organisational justice and fairness. HR managers must answer some basic questions before embarking on the AI journey:

What are the employees' expectations from AI-based HR tools?

Do employees expect all HR functions to be automated?

The conflicting aspects of AI highlighted above drive home the point that HR practitioners must be measured and cautious as they incorporate AI-based HRM. This paper presents a framework that can help HR managers in this process. Grounded in employee perceptions of trust and organisational readiness for using AI, this framework provides HR managers with a blueprint of which activities to focus on at what stage of AI technology assimilation. The typology proposed here is not intended to provide an exhaustive list of HR activities but to give managers an intuitive basis for selecting functions basis their inherent complexity and maturity of the AI technology of the firm. Further, the model incorporates feedback at every stage and a certain degree of flexibility, thereby aligning with the firm's overall business and HR strategy.

Drawing on the theory of innovation assimilation (Zhu et al., 2006), three stages of AI-HRM assimilation in an organisation – initiation, adoption and routinisation – are specified. During the initiation phase, organisations must evaluate AI's potential impact and benefits in the HR function. Initiation is a crucial step that will determine the success or failure of the entire process. HR practitioners need to understand the nuances of AI and its fitment with the current technology and HRM systems. The involvement of leaders is essential to understand the business perspective and get their buy-in. Further, HR managers should incorporate employee feedback.

In the adoption stage, organisations decide to use AI and allocate resources for procuring the technology. Here, HR managers must partner with developers and help them with the employees' perspective required to build the models. The final phase of routinisation involves widespread usage of AI across a gamut of HR activities.

Figure 1: A Conceptual Framework of AI Implementation in HRM Services of a large organization

Once the technology has been acquired and pilot tested, a staggered implementation within HR functions cutting across the technology assimilation stages of adoption and routinisation (Figure1) is advisable. The heuristic used here is that the profile of AI-based HRM functions should correspond to the level of technology (AI) assimilation leading to a five phase implementation plan for HRM services in a large organisation (Figure 1). As mentioned earlier, this is not a watertight classification. Every organisation is different and context plays a vital role. Thus, HR managers should use their discretion while making this evaluation.

  • Phase 1 (Quick Wins): Initially, only the administrative activities such as payroll, employee records, leave, and absence would transition. AI can reduce payroll inaccuracies, improve compliance and quickly adapt to changes in labour legislation. Successful implementation of these tasks would relieve HR of the routine activities, get support from leaders and enhance employee experience.

  • Phase 2 (Drive Engagement): Subsequently, employee engagement, workforce planning and attrition management functions would move to AI tools. Using AI chatbots, NLP and sentiment analysis can give organisations a real-time pulse of employee engagement and help identify high-risk attrition employees. AI-based models for workforce planning and attrition management would enhance organisational ability to predict and measure workforce needs more effectively

  • Phase 3 (Customised Learning): HR managers can leverage AI techniques to identify skill gaps and deploy customized employee learning and development programmes. Employees can benefit from customised training content and capability programs that supplement their learning goals. In the compensation and benefits space, AI can bring greater visibility into financial planning and budgeting by mapping compensation levels against employee performance. AI tools can provide employees with the option of choosing a benefits package within a prescribed limit based on their individual needs

  • Phase 4 (Spread with Speed): HR managers can leverage AI recruitment tools like semantic analysis for applicants' screening that will speed up the hiring process and improve the chances of hiring better candidates. In the performance management, AI tools can help managers provide objective feedback by periodically reporting KPIs and pre-empting biases. AI applications can support talent management practices by mapping employees' skills and interests. These tools can also analyse the trajectory of employees in the organization and provide recommendations for succession planning and building a leadership pipeline

  • Phase 5 (Create Routines): This phase coincides with the routinisation of AI usage in the organisation. Performance management ratings and employee selection will of focus here. As these activities have complex outcomes that have ethical and legal repercussions for organisations, AI technology must be institutionalized before these functions are incorporated. HR managers can judiciously utilise AI tools to engage with applicants at various stages of selection and improve the hiring process. Similarly, HR managers can utilise the functionalities of AI for employee performance ratings. However, given the sensitive nature of employee selection and performance ratings, AI should be used for analysis and prescriptive advice only. The ultimate decision-making and discretion should be with managers.

In line with recent research (e.g., Tambe et al., 2019), the conceptualization of AI based HRM described here is conservative and cautious. Given that 81% of HR leaders find it challenging to keep up with the pace of technological changes at work (Oracle & Future Workplace,2019), this is an appropriate strategy at this point. Of course, other limitations like labour regulations will preclude a complete transition to AI in the HR domain. For example, AI-driven domestic enquiry or grievance redressal mechanism is more likely to happen after some years of maturation of AI in the field. Contrary to popular belief, artificial intelligence is not mechanising human behaviour or replacing people with robots. It is about improving the employee experience and enhancing managerial potential through data-driven decision-making. HR has an important role to play in this transformation.
 

References

Budhwar, P., Malik, A., De Silva, M. T. T., & Thevisuthan, P. (2022). Artificial intelligence – challenges and opportunities for international HRM: A review and research agenda. The International Journal of Human Resource Management, 33(6), 1065–1097. https://doi.org/10.1080/09585192.2022.2035161

Dutta, D., Mishra, S. K., & Tyagi, D. (2022). Augmented employee voice and employee engagement using artificial intelligence-enabled chatbots: A field study. The International Journal of Human Resource Management, 1–30. https://doi.org/10.1080/09585192.2022.2085525

Prikshat, V., Malik, A., & Budhwar, P. (2021). AI-augmented HRM: Antecedents, assimilation and multilevel consequences. Human Resource Management Review, 100860. https://doi.org/10.1016/j.hrmr.2021.100860

Prikshat, V., Patel, P., Varma, A., & Ishizaka, A. (2022). A multi-stakeholder ethical framework for AI-augmented HRM. International Journal of Manpower, 43(1), 226–250. https://doi.org/10.1108/IJM-03-2021-0118

Sloane, M. (2018, July 6). Making artificial intelligence socially just: Why the current focus on ethics is not enough [Online resource]. British Politics and Policy at LSE; London School of Economics and Political Science. http://blogs.lse.ac.uk/politicsandpolicy/artificial-intelligence-and-society-ethics/

Zhu, K., Kraemer, K. L., & Xu, S. (2006). The Process of Innovation Assimilation by Firms in Different Countries: A Technology Diffusion Perspective on E-Business. Management Science, 52(10), 1557–1576. https://doi.org/10.1287/mnsc.1050.0487

 Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910

Oracle & Future Workplace (2019), From Fear to Enthusiasm : Artificial Intelligence is Winning More Hearts and Minds in the Workplace, AI@Work Study 2019. https://www.oracle.com/a/ocom/docs/applications/hcm/ai-at-work-ebook.pdf

Mirowska, A., & Mesnet, L. (2022). Preferring the devil you know: Potential applicant reactions to artificial intelligence evaluation of interviews. Human Resource Management Journal32(2), 364-383.

Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2021). Boundaryless careers and algorithmic constraints in the gig economy. The International Journal of Human Resource Management, 1-31.


 

     

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