5 Stage Transformation Model In Use Of Agentic AI Framework In Human Resources

Authors

  • Vishwanadh Raju Kurchellapati Author
  • Dr. Saisree Mangu Author

DOI:

https://doi.org/10.64252/vq2gjx09

Keywords:

Agentic AI, Human Resource Management (HRM), AI Transformation Model, Autonomous HR Agents, Metaverse, Conversational AI, Intelligent Automation, Generative AI in HR, Responsible AI, Ethical AI; AI-Augmented Decision Making, AI Governance, Human-AI Collaboration and AI Maturity Models.

Abstract

The integration of Artificial Intelligence (AI) in Human Resource Management (HRM) has evolved from basic automation to intelligent augmentation. However, the emergence of Agentic AI — AI systems capable of autonomous goal setting, reasoning, adaptation, and collaboration — marks a paradigm shift in how HR functions are designed and delivered. Unlike traditional AI that supports decision-making, Agentic AI actively initiates and executes complex HR tasks, from intelligent talent acquisition to dynamic workforce planning, with minimal human intervention.

This transformation demands a structured framework to guide organizations through the progressive maturity of AI capabilities. A 5-Stage Transformation Model provides a roadmap to evolve from rule-based automation to an adaptive, human-centric HR ecosystem. The model encapsulates stages from digitization and data-driven insights to conversational AI, autonomous agents, and finally, a fully adaptive HR architecture that balances efficiency with ethics and empathy.

As organizations strive to remain competitive and employee-centric in an increasingly complex environment, adopting Agentic AI within a transformation model becomes not only a strategic advantage but a necessity. The structured evolution in harnessing the full potential of Agentic AI will redefine the future of Human Resources.

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Published

2025-07-26

Issue

Section

Articles

How to Cite

5 Stage Transformation Model In Use Of Agentic AI Framework In Human Resources. (2025). International Journal of Environmental Sciences, 1064-1075. https://doi.org/10.64252/vq2gjx09