Authors:
Manoj Parasa, Prameela Durga Bhavani Katari

Addresses:
Department of Information Technology Services, Southern Arkansas University, Magnolia, Arkansas, United States of America. Department of Information Technology Services, Ernst and Young, Dallas, Texas, United States of America. Department of Information Technology Services, Accenture, Hyderabad, Telangana, India.

Abstract:

The rapid evolution of workforce technologies has accelerated the need for intelligent, ethical, and scalable approaches to job architecture design. This research presents a governance-oriented framework for integrating Generative Artificial Intelligence (AI) into the SAP SuccessFactors ecosystem to automate and optimise job description creation. The proposed model employs Natural Language Generation (NLG) within the Job Profile Builder and Talent Intelligence Hub to produce competency-driven, bias-mitigated, and contextually adaptive job content. A mixed-methods design was applied, encompassing system prototyping, linguistic analysis, and expert validation across global HR and compliance teams. Empirical evaluation demonstrates that AI-generated job profiles achieve higher accuracy in competency alignment, a 38% reduction in biased or exclusionary language, and a 70% improvement in content development speed compared to traditional authoring methods. Beyond operational gains, the research highlights the importance of human-in-the-loop governance, explainable AI logic, and transparent ethical checkpoints to sustain trust and regulatory compliance. By framing Generative AI as a co-creative partner within SAP SuccessFactors rather than a fully autonomous author, this study establishes a replicable blueprint for responsible AI deployment in Human Capital Management (HCM). The findings position responsible job design as a critical enabler of digital transformation, workforce inclusivity, and organisational resilience in the era of intelligent enterprise systems.

Keywords: SAP SuccessFactors; Generative Artificial Intelligence; Natural Language Generation; Job Profile Builder; Intelligent Job Design; Inclusive Job Descriptions; AI-Driven Content Generation.

Received on: 30/08/2024, Revised on: 20/11/2024, Accepted on: 07/02/2025, Published on: 09/09/2025

DOI: 10.69888/FTSML.2025.000442

FMDB Transactions on Sustainable Management Letters, 2025 Vol. 3 No. 3, Pages: 86-94

• Views : 143
• Downloads : 10

Download PDF