Global Journal of Management and Business Research, A: Administration and Management, Volume 23 Issue 5

organization. This allows in creating meaningful work experiences for employees, better growth assistance, and building more robust pipelines for critical businesses. The interactive user interface uses natural language processing to engage in active discussion with the employees (Litman, 2016). This is integrated with precisely the employee's historical information about various aspects. Literature also suggests that AI- enabled job-opportunity match functionality will be of use in suggesting suitable roles for employees, based on their profiles (Tambe, 2019; Nocker and Sena, 2019). Employee queries can be resolved by the AI tools. Provision of built-in alerts on job opportunities help employees to know about internal opening matching to their current profiles and tailored to their aspirational roles. We can see that majority of research related to AI in HRM focuses on talent acquisition, resource allocation, and training and development. Very few studies have explored the adoption of AI in other HR functions such as employee retention, compensation and seperation. Additional research is needed on different aspects of AI in HRM, including adoption challenges, impact studies, new skill requirements etc. In the following section, we discuss the future research directions based on the findings. d) Implications and Future Research Directions As indicated by the findings, despite continual research progress is being made related to AI technologies for the HRM function, there are areas which need further attention and in-depth understanding. The study findings indicate possible follow-on ideas and future studies. For example, research is required on how AI and related technologies in the HRM function have impacted vital aspects of employee engagement, retention, growth, compensation, reward, and recognition. There have been very few studies conducted related to these aspects. How this phase of HR transformation and the strategic development of HR has impacted business performance is a key area that has not been investigated. The transformation of HR function by the adoption of AI technologies is an emergent field of focus. Despite the benefits of AI adoption, there is a huge variance in terms of adoption of AI. Research needs to be conducted on what are the influencing factors that impact adoption. Though AI adoption is key aspect of technology adoption in organizations, there is lack of technology adoption model-based research, related to adoption of AI across all domains of HR. Further, detailed research work is recommended to be conducted on the design and implementation of change management in adoption. Another good avenue for future research would be industry-specific and cross- industry comparisons to support further research. Many areas merit additional investigation, even though a significant work on AI in HRM has been conducted, especially in the last decade. AI in HR has enabled the HRM function to be transformed and acknowledged as a strategic partner of the business. For example, technological advancements have created opportunities in the talent acquisition domain that links strategic HR management with business strategy (Walford and Scott, 2018). By enabling digital engagement, HR provides a competitive advantage to the organizations (Jesuthasan, 2017). There is a lack of research, as to what is the impact of HR’s transformation by leveraging AI and how does this contribute to enabling organizations to achieve business success and leverage strategic advantage for hiring and retaining key talent. Further, in the domain of strategic HRM, cognitive enabled insights can facilitate drawing optimal outcomes. While humans contribute to a more thorough and intuitive approach to managing uncertainty and complexity in organizations (Jarrahi, 2018), AI can enhance humans' cognition when handling complex problems. There is a dearth of research and detailed studies on this aspect. The use of AI tools/applications has led to questions related to the authenticity of people/talent decisions made basis AI algorithms and logic. Especially in the talent acquisition domain, the fairness and objectivity of hiring decisions based on the logic of an AI based algorithm or an AI based decision rule is questioned, as to whether these decisions are objective (Bogen, 2019). Aspects regarding the authenticity of employee data – both current and potential, are a cause of concern, as its validity is questionable. The authenticity of algorithms designed based on the data could be imperfect, as it could reflect society's ingrained prejudices and biases. Also, the aspect of inherent or unconscious bias which could be part of the logic of the algorithm or seeded in the decision rule and driving biased decision related to hiring needs further exploration. Data sets could be structured in advance to be aligned with historical precedents and patterns, which could even be part of an organization's culture and can be hardwired into code (Gulliford and Dixon, 2019). Questions regarding talent decisions made basis this data, whether it further strengthens exclusions and existing biases, is imperative to be researched, as these are sensitive topics to be addressed. Also, interlinked to this, there is a requirement for more comprehensive insights and counsel in the form of additional research to help address ethical concerns and acceptance of talent decisions based on applications of AI in the human resources functions. Concerns related to the security and privacy of employee sensitive information also needs deeper exploration. Capelli (2019) has highlighted some of the drawbacks of data science being applied in HR, including concerns related to infringing on privacy, usage of social media posts as a determinant factor for Research on Artificial Intelligence in Human Resource Management: Trends and Prospects 41 Global Journal of Management and Business Research Volume XXIII Issue V Version I Year 2023 ( ) A © 2023 Global Journals