Artificial Intelligence for Human Resource Management

This study explores the application of Artificial Intelligence technologies (AI) as applied to human resources management (HRM) in Singapore. The conceptualisation of this research arises at a time of increasing attention on digital innovation as well as the ubiquitous need to better develop talents in a resource scarce Singapore. The research’s overarching objective is to gain insights on the value of AI to organisations and to AI solution providers, being the key stakeholders in ensuring successful AI adoption for human resources (HR) practices.

As AI is a relatively new research area, a mixed method: sequential approach was adopted. Phase 1 qualitative research elicits HR and AI industry experts’ opinions and is guided by questions around contemporary HRM issues, opportunities and challenges associated with AI, critical AI adoption factors, and lastly the state of AI adoption in HRM in Singapore. The emerging themes from Phase 1 findings and relevant technology adoption frameworks informed the development and the structuring of Phase 2 questionnaire. A total of 109 valid responses were received from the 116 prospective respondents, which constitutes 94% response rate by the end of the two weeks data collection period.

Although the findings revealed that several HR tasks could be enhanced through AI, low levels of familiarity do affect stakeholder perceptions of its effectiveness. That, coupled with a relatively conservative outlook, imposes challenges in adopting and integrating AI in HRM. Early stages of AI adoption in the industry further inhibit organisations from effectively assessing the potential returns on investments (ROI) in AI.

Opportunities exist however for early adopters to get ahead if they could leverage digital technology and develop adoption strategies supported by effective change management. A fast-growing suite of commercial applications provide effective solutions for certain HR tasks, with some of these tasks already lending itself to automation and would hence result in lesser disruptions. For AI solution providers, ROI, performance expectancy and data security should guide the design and marketing of AI applications.