Authors:
Manoj Parasa

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.

Abstract:

In today’s enterprise environment, Learning Management Systems (LMS) must evolve from static repositories into intelligent platforms that enable dynamic, individualised development. This study presents a scalable, ethically governed AI framework integrated into SAP SuccessFactors LMS to enable hyper-personalised learning journeys that align employee development with strategic business goals. By leveraging SAP Business Technology Platform (BTP), SAP AI Core, the Learning Recommendation Service, and the Talent Intelligence Hub, the proposed solution delivers real-time, AI-powered learning content recommendations tailored to user behaviour, competency gaps, and aspirational roles. Using a mixed-methods approach that includes SAP architecture modelling, semi-structured expert interviews, and simulation with synthetic workforce data, the framework demonstrates improvements in content relevance, time-to-skill efficiency, and internal mobility outcomes. Key findings reveal that personalised learning journeys not only increase learner engagement and course completion rates but also support organisational agility by enabling cross-functional upskilling and career pathing. The model also incorporates responsible AI practices, including explainable recommendations, opt-in personalisation, and fairness-aware logic to ensure transparency and trust. This research fills a critical gap in enterprise learning analytics by offering a validated, SAP-native blueprint for embedding intelligent personalisation within LMS environments. The implications reinforce the strategic role of learning in enabling adaptive, future-ready workforces while delivering measurable business alignment.

Keywords: SAP SuccessFactors; Artificial Intelligence; Learning Management System; Workforce Agility; Predictive Learning Analytics; Organisational Development; Machine Learning.

Received on: 26/11/2024, Revised on: 03/02/2025, Accepted on: 16/03/2025, Published on: 05/09/2025

DOI: 10.69888/FTSIN.2025.000537

FMDB Transactions on Sustainable Intelligent Networks, 2025 Vol. 2 No. 3, Pages: 152-163

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