AI-driven SAP S4/HANA, advancing firm operational efficiency, decision-making and resource optimization

Tarek Samara ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 3, 4795-4811

Abstract: This study examines how AI integration into SAP S/4HANA enhances information system effectiveness in meeting firm needs, including operational efficiency, decision-making, and resource optimization. It aims to provide valuable insights for businesses leveraging AI-powered ERP capabilities in modern business environments. This study employs archival analysis using a qualitative multiple case study approach, triangulating insights from three sources for depth and rigor: a literature review for theoretical grounding, SAP’s official proposals, and case studies from several firms. Selection criteria include relevance, credibility, and comprehensiveness. This comparative study evaluates AI’s impact on efficiency, decision-making, and resource optimization. Thematic analysis identifies key patterns, challenges, and business outcomes. The findings confirm that AI integration into ERP systems enhances operational efficiency, decision-making, and resource optimization. Archival analysis demonstrates tangible benefits, including reduced downtime, improved supply chain management, automated financial operations, and enhanced predictive analytics. This research bridges theory and practice by connecting academic concepts with real-world AI-driven ERP integration and the implications of AI in SAP S/4HANA, offering a comprehensive perspective. It provides valuable insights for both academics and practitioners. These strengths highlight the study’s relevance, originality, and potential impact in the evolving field of AI-integrated enterprise systems.

Keywords: Artificial intelligence; Decision-making; ERP; Information system; Machine learning; NLP; Operational efficiency; Predictive analytics; Resource optimization; SAP S/4HANA. (search for similar items in EconPapers)
Date: 2025
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