In today's world, many organizations, including healthcare companies, choose the use of AI in operational functions as part of digital transformation. The case study analysis explores the use of AI in the hiring process of a fictitious company named Moya. This chapter captures the various perspectives of hiring algorithms and the leverage of AI in various HR processes. It examines the impact on the employees, the organization, its culture, the organizational theories at play, the risk it poses, and various solutions that could be applied while considering such emergent technologies for adoption and the change management effort associated with it. The chapter concludes with a few practical suggestions that can be implemented to remediate the current scenario while also providing a framework for future leverage. The chapter recommends a few areas of study to develop checklists and other guardrails to protect the interests of the company while successfully applying this digital transformation, and also extending the same in other operational processes.
TopIntroduction
As we journey towards the end of the first quarter of the 21st century, organizations are facing unprecedented changes and competition, which has led to the increasingly intensive job market, and having an effective and nimble Human Resource Management (HRM) function is a strategic priority for firms (Luthans et al., 1997). Business realities are placing ever more demands on the newly recruited talent to be productive sooner than later, placing ever more emphasis on the recruitment function to hire the right candidates in the first place (Wright, 2013). In a CEO survey, the Conference Board respondents indicated that attracting and retaining talent is a strategic priority (Mitchell et al., 2018).
In such a high job demand market, today’s HR department must accomplish several voluminous tasks during the hiring cycle, ranging from sifting through several resumes, conducting screening tests, organizing interviews, and doing a lot of paperwork, including collecting feedback and report-out (Goel & Jhawar, 2018). Such an overload on HR functions, coupled with a younger workforce relying more on digital media, has expedited the technological transformation in HRM function, moving AI-enabled recruiting from a peripheral curiosity to a critical capability (van Esch & Black, 2019). This has, in turn, paved the way for the holy grail of using automation and Artificial Intelligence (AI) in recruitment technologies, thereby ensuring the reach out to the top-notch, digitally savvy jobseekers with less time and effort and reduced hiring costs while meeting the modern and diverse talent needs effectively and efficiently (Mehmood, 2017).
AI automation presents significant potential as it enables us to harness the advantages of automation in business processes, such as heightened speed, efficiency, time savings, and scalability, while also leveraging the insights, adaptability, and computational prowess of AI technology (Shani, 2021). The rapid advancements in the field of Generative AI with ChatGPT have prompted several novel uses, including chatbots, virtual assistants, and content creation, with applications garnering over 100 million users (US GAO, 2023). These AI technologies have sparked multiple debates on the leverage of AI in several fields, including in the hiring industry (Foster, 2023).
Problem Statement
In a 2019 study, the researchers noted the rapid adoption of AI in the job market and hiring (Raghavan et al., 2019). From a 2020 study on workplace global trends report, 55% of HR leaders use AI in hiring (Bravery & Mercer, 2020). This has risen to 79% in 2022, as per a Society of Human Resource Management survey (SHRM, 2022). The role of HR in Digital recruiting has evolved from leveraging AI-enabled tools from a nicety to a necessity (Black & van Esch, 2020).
Several top companies, including Microsoft and Amazon, deploy various AI-based technologies in their hiring process (Curry, 2023). The pertinent question is whether the AI-based tools help handle biases in the hiring cycle, and in general, within the field of HRM, or if the AI tools perpetuate the biases further to the point that removing them is difficult (White, 2021; Engler, 2021).
In a joint economic study by the European Union and US governments, it is noted that the organizations who participated in the study expressed their concern about the introduction of bias in every stage of the hiring process, with an increased leverage of AI-based hiring algorithms (CEA - The White House, 2022). Fully automated hiring practices, driven by AI algorithms, could reproduce real-world bias in hiring processes and could very well begin even before the candidate applies (Bogen, 2019). The most common form of AI bias comes in with the training data that is chosen and the lack of human intervention to neutralize the deficiency in the training model, commonly referred to as “human in the loop.” The bias can get into the hiring pipeline through the hiring lifecycle, from sourcing, screening, and selection to offer phases (Lawton, 2022).