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

is considered a challenge by many organizations. The term co-occurrence network showed that workforce planning and resource management are a key research area in AI and HR. Literature suggests that AI technology and applications enable organizations for efficient workforce management and optimization. For example, a variation of unsupervised Competitive Learning neural networks algorithm was proposed by Leem (1996) to overcome the obstacles that conventional analyses face. The deployment of Analytic Hierarchy Process (AHP) and Fuzzy Mathematics systems ensures appropriate management decisions for employees' most suitable role assignments (Subramanian and Ramanathan, 2012; Saaty et al., 2007). A fuzzy input-output optimization method was proposed by Aviso et al. (2018) for HR allocation during a crisis. AI enables a comprehensive evaluation of human resource allocation and continuous improvement. The ability of AI to analyze a large amount of data and draw inferences and detect patterns assist in resource planning optimally (Lengnick-Hall et al., 2018). It also enables multi-criteria human resource allocation, which involves allocating limited availability human resources among many demands by optimizing current objectives (Lin & Gen, 2008). Data related to employee performance and succession and analysis of it provides insights on employee engagement and related challenges (Smith, C. 2019). Resource efficiency can be enhanced by forecasting, which is critical in decision making, based on which the financial costs of the workforce can be optimized. b) Cluster 2 - Talent Acquisition Literature shows that AI enhances recruitment efficiency through balanced job descriptions, job requisition prioritization, profile databases, programmatic recruitment advertising based on machine learning, attracting potentially suitable candidates (Albert, 2019; Upadhyay and Khandelwal, 2018). A human resource selection system constructed using FNN is discussed in Huang et al. (2004). AI assistants support the engagement of candidates through digital platforms (Van and Black, 2019). Specialized chatbots can be used for candidate attraction and to have an insightful conversation to engage candidates in deeper conversation and recommend jobs relevant to their skills and experience (Leong, 2018). The chat topics provide informative answers about aspects related to the organization – compensation, vacation guidelines, culture, locations, dress code, business, and assessment process. It enhances the scope of converting prospective candidates into active job seekers. Thus, creating a positive recruiting experience and facilitates accepting a job offer by the right candidate. Selection efficiency is improved by proper candidate identification with data analysis (Bongard, 2019). Predictive analysis helps forecast the future performance of a prospective candidate, basis the profile and information collated and analysed during the automated aspects of the job application process. Profile matching, Optical Character Recognition (OCR), CV Parsing are key applications enabled by AI (Singh and Finn, 2003; Bizer et al., 2005). Initial screening can be automated by neural networks, data mining techniques, and AI chatbots by conducting interpretation/validation of candidate responses. Intelligent interviews are conducted with reduced unconscious bias, assisted by AI tools to "listen"/prompt questions and Robotic Process Automation (RPAs) (Madakam, 2019; Nawaz, 2019). Background checks are automated, based on different reviews required, basis the profile of the candidate. From the literature it can be seen that the digitally enabled on boarding process focuses on two aspects. One is to automate, and the second is to personalize. Functionalities like Chatbots support new hires in knowing relevant details about their new role, team hierarchy, and overall organizational landscape (Ernst & Young, 2018; Nunn, 2019). The efficiency of the on boarding process improves by the collection of employee information, joining forms completion, and assistance in online registration. Another key aspect is that through digitally enabled on boarding, the new hires have access to tools facilitating them to connect and socialize in their new organization, which positively impacts their learning, productivity, and engagement, as they settle in their new roles (Sheikh et al., 2019;Upadhyay & Khandelwal, 2019). c) Cluster 3 - Training and Development Cluster 3 focuses on training and development applications of AI. Literature suggests that AI-based tools provide more personalized and enhanced digital learning experience (Ong and Ramachandran, 2003; Maity, 2019). An employee can access their skill profiles, to build their skill journey helping to have ownership in building their skills path and their career path and thereby supporting the acceleration of skill development (Wang and Lin, 2020). The search engine capabilities in the Learning tools architecture helps in making intelligent recommendations, for the learning road map, for an employee. Through metadata, AI tagging of all content in the learning modules supports user-friendly interfaces through content channels (Guenole and Feinzig, 2019). Intelligent data matching and AI-enabled individual profile analysis provide insights that help identify the right talent for key roles and succession planning (Barboza, 2019; Bersin, 2017; Nunn, 2019). Interactions with employees for growth and future opportunities are enabled by AI tools, which can act as a personalized digital career advisor, allowing employees to advance their careers within the Research on Artificial Intelligence in Human Resource Management: Trends and Prospects 40 Global Journal of Management and Business Research Volume XXIII Issue V Version I Year 2023 ( ) A © 2023 Global Journals