HCM AI Agents and Data-Driven Decision Making

Mohammad Afghanul Khair

Western Governors University

Millcreek, Utah, United States 84107

afghantheone@gmail.com

Nusrat Shaheen

Western Governors University

Millcreek, UT 84107, United States
nusratsha92@gmail.com

Mohammed Misbahul Khair

Western Governors University

Millcreek, UT 84107, United States

misbakhair93@gmail.com

Shalu Jain
Maharaja Agrasen Himalayan Garhwal University

Pauri Garhwal, Uttarakhand

mrsbhawnagoel@gmail.com

Abstract

In the modern enterprise landscape, Human Capital Management (HCM) is undergoing a paradigm shift, leveraging artificial intelligence (AI) agents and data-driven decision-making. AI-powered HCM solutions optimize talent acquisition, workforce planning, and employee engagement by harnessing machine learning, natural language processing, and predictive analytics. These intelligent agents enhance efficiency by automating repetitive HR tasks, such as recruitment screening, performance evaluations, and employee sentiment analysis. Furthermore, data-driven insights empower HR leaders to make strategic decisions regarding workforce productivity, skill gaps, and employee retention. By integrating AI with HCM, organizations can achieve greater accuracy in predictive workforce analytics, enabling proactive interventions that improve employee experience and operational efficiency. However, implementing AI in HCM also presents challenges, such as data privacy, ethical concerns, and bias mitigation, which require robust governance frameworks. This paper explores the transformative impact of AI agents and data-driven methodologies in HCM, emphasizing their role in optimizing HR processes and driving organizational success. By examining real-world applications and industry trends, this study provides a comprehensive understanding of how AI-driven HCM solutions contribute to workforce agility, strategic talent management, and long-term business sustainability.

Keywords

AI-driven HCM, intelligent HR automation, workforce analytics, predictive talent management, AI agents in HR, data-driven decision-making, employee retention strategies, ethical AI in HR, HR process optimization, human capital intelligence.

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Nagender Yadav, Antony Satya Vivek, Prakash Subramani, Om Goel, Dr. S P Singh, Er. Aman Shrivastav. (2024). AI-Driven Enhancements in SAP SD Pricing for Real-Time Decision Making. International Journal of Multidisciplinary Innovation and Research Methodology, 3(3), 420–446. https://ijmirm.com/index.php/ijmirm/article/view/145
Yadav, Nagender, Abhijeet Bhardwaj, Pradeep Jeyachandran, Om Goel, Punit Goel, and Arpit Jain. (2024). Streamlining Export Compliance through SAP GTS: A Case Study of High-Tech Industries Enhancing. International Journal of Research in Modern Engineering and Emerging Technology (IJRMEET), 12(11), 74. https://www.ijrmeet.org
Yadav, N., Aravind, S., Bikshapathi, M. S., Prasad, P. (Dr.) M., Jain, S., & Goel, P. (Dr.) P. (2024). Customer Satisfaction Through SAP Order Management Automation. Journal of Quantum Science and Technology (JQST), 1(4), Nov(393–413). https://jqst.org/index.php/j/article/view/124
Gangu, K., & Pakanati, D. (2024). Innovations in AI-driven product management. International Journal of Research in Modern Engineering and Emerging Technology, 12(12), 253. https://www.ijrmeet.org
Govindankutty, S., & Goel, P. (Dr) P. (2024). Data Privacy and Security Challenges in Content Moderation Systems. Journal of Quantum Science and Technology (JQST), 1(4), Nov(501–520). Retrieved from https://jqst.org/index.php/j/article/view/132
Shah, S., & Khan, D. S. (2024). Privacy-Preserving Techniques in Big Data Analytics. Journal of Quantum Science and Technology (JQST), 1(4), Nov(521–541). Retrieved from https://jqst.org/index.php/j/article/view/129
Garg, V., & Khan, S. (2024). Microservice Architectures for Secure Digital Wallet Integrations. Stallion Journal for Multidisciplinary Associated Research Studies, 3(5), 165–190. https://doi.org/10.55544/sjmars.3.5.14
Hari Gupta , Dr Sangeet Vashishtha Machine Learning in User Engagement: Engineering Solutions for Social Media Platforms Iconic Research And Engineering Journals Volume 8 Issue 5 2024 Page 766-797