From Automation to Autonomy: Leveraging Oracle HCM Cloud’s AI Capabilities to Create Autonomous HR Processes and Decision-Making

The transition from automation to autonomy in human resource (HR) processes marks a significant shift in how organizations manage their workforce. Leveraging Oracle HCM Cloud’s AI capabilities, this transformation enables HR processes to evolve from rule-based automation to data-driven, autonomous decision-making. By utilizing machine learning, natural language processing, and predictive analytics, Oracle HCM Cloud allows HR systems to make real-time decisions without manual intervention. This shift not only streamlines operations but also ensures that HR practices are more responsive, adaptive, and personalized. Key areas where Oracle HCM Cloud’s AI is revolutionizing HR include talent acquisition, employee engagement, performance management, and retention strategies. The AI-driven insights provided by Oracle HCM Cloud empower HR professionals to make informed decisions that align with organizational goals, ultimately enhancing productivity and employee satisfaction. This paper explores the capabilities of Oracle HCM Cloud, emphasizing the role of AI in reducing administrative burdens, improving decision-making accuracy, and fostering a more proactive HR environment. By shifting towards autonomy, organizations can unlock new potential for growth and efficiency while minimizing human errors and biases in HR processes.

Authors

Publication Details

Journal
International Journal of Research in Humanities and Social Sciences
Published
2025-03-01
DOI
https://doi.org/10.63345/ijrhs.net.v13.i3.15
Citations
2
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
4.57
Controls
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article

From Automation to Autonomy: Leveraging Oracle HCM Cloud’s AI Capabilities to Create Autonomous HR Processes and Decision-Making

Mohammad Afghanul Khair, Nusrat Shaheen, Mohammed Misbahul Khair, Dr Shantanu Bindewari
2 citations
International Journal of Research in Humanities and Social Sciences
Big Data and Business Intelligence
4.57
article

From Automation to Autonomy: Leveraging Oracle HCM Cloud’s AI Capabilities to Create Autonomous HR Processes and Decision-Making

Mohammad Afghanul Khair, Nusrat Shaheen, Mohammed Misbahul Khair, Dr Shantanu Bindewari
article en
2 citations

Abstract

The transition from automation to autonomy in human resource (HR) processes marks a significant shift in how organizations manage their workforce. Leveraging Oracle HCM Cloud’s AI capabilities, this transformation enables HR processes to evolve from rule-based automation to data-driven, autonomous decision-making. By utilizing machine learning, natural language processing, and predictive analytics, Oracle HCM Cloud allows HR systems to make real-time decisions without manual intervention. This shift not only streamlines operations but also ensures that HR practices are more responsive, adaptive, and personalized. Key areas where Oracle HCM Cloud’s AI is revolutionizing HR include talent acquisition, employee engagement, performance management, and retention strategies. The AI-driven insights provided by Oracle HCM Cloud empower HR professionals to make informed decisions that align with organizational goals, ultimately enhancing productivity and employee satisfaction. This paper explores the capabilities of Oracle HCM Cloud, emphasizing the role of AI in reducing administrative burdens, improving decision-making accuracy, and fostering a more proactive HR environment. By shifting towards autonomy, organizations can unlock new potential for growth and efficiency while minimizing human errors and biases in HR processes.

International Journal of Research in Humanities and Social SciencesVol. 13(3)
Openalex Percentile: Top 7%
Big Data and Business Intelligence
4.57
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From Automation to Autonomy: Leveraging Oracle HCM Cloud’s AI Capabilities to Create Autonomous HR Processes and Decision-Making — Mohammad Afghanul Khair, Nusrat Shaheen, et al. · International Journal of Research in Humanities and Social Sciences (2025) | TGRS Research Map | TGRS