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Employee engagement (EE) can result in multiple positive impacts not only on the individual and his/her team but also on the organisational and financial outcome of the business. If artificial intelligence (AI) can be used as a tool to facilitate EE, organisations will be more than satisfied to adopt it. The paper aims to study the penetration of AI for EE in corporate India.

Based on the information gathered through secondary research, a framework of questions was built and sent to some senior people in the area of AI and HR to check for its completeness. Respondents based on inclusion criteria were selected through random purposive sampling to be a part of the study. A total of 23 respondents participated in the study. Qualitative data analysis of the transcripts was conducted using MAXQDA 2022 (Verbi Software, Berlin, Germany), which is a qualitative data analysis software. Multiple readings were undertaken to identify the patterns and relationships in the data.

The participants described a variety of issues while using or planning to use AI for EE. Some of the issues mentioned were related to cost, challenges, mindsets and attitudes, demography of employees, comfort in the use of technology, size of the organisation, change management strategies, software vendors and vendor support. The most common responses were grouped into headings such as Organisation, Process, Employee and Software Choice Related aspects.

Lately, the overall work environment, work and personal life balance, and quality of life have become more desirable than earning a good salary. AI is becoming a part of various aspects of business but its role in HR is yet to be explored. AI’s capabilities to predict may result in more employee work satisfaction. The paper explores the possibility of using AI as a tool in every aspect of employee life cycle, thereby attempting to make HR processes more productive and enhance EE.

The purpose of this study is to provide an overview of artificial intelligence (AI) in the talent management sphere. The study seeks to contribute to the body of knowledge with respect to human resource management and AI by conducting a literature review on the integration of AI in talent management, synthesising existing approaches and frameworks, as well as emphasising potential benefits.

The study adopts desk research, computational literature review (CLR) and uses topic modelling [with bidirectional encoder representations from transformers (BERTopic)] to throw light on the diffusion of AI in talent management.

The study’s main finding is that the area of AI in talent management is on the verge of gradual development and is in tandem with the growth of AI. We deduced that there is a link between talent management practices (planning, recruitment, compensation and rewards, performance management, employee empowerment, employee engagement and organisational culture) and AI. Though there are some known fears with regards to using the innovation, the benefits outweigh the demerits.

The current study has some limitations. The scope and size of the sample are the primary limitations of this study. No form of qualitative analytics was used in this study; as a result, the information obtained was limited. The study provides a snapshot of AI in talent management and contributes to the lack of literature in the joint fields. Also, the study provides practitioners and experts an overview of where to target investments and resources if need be.

The originality of this study comes from the combination of CLR methods and the use topic modelling with BERTopic which has not been used by previous reviews. In addition, the salient machine learning algorithms are identified in the study, which other studies have not identified.