AI-Driven Governance Control Plane for Multi-Vendor SAP Service Delivery Ecosystems
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I3P125Keywords:
SAP Governance, Multi-Vendor Ecosystems, AIOps, SIAM, Topology-Aware Dependency Modeling, SLA Intelligence, Policy-As-Code, Explainable AI (SHAP), Servicenow Integration, Graph Databases, Enterprise IT Governance, Machine Learning, XGBoost, BERT, Isolation Forest, LSTAbstract
Enterprise SAP landscapes have evolved from centralized, single-provider environments to distributed multi-vendor ecosystems spanning four specialized domains: Service Integration and IT Service Management (SIAM/ITSM), Application Management Services (AMS), Integration and Data Operations, and Infrastructure/Network platforms. This fragmentation introduces systemic governance risks characterized by accountability ambiguity at vendor handoff points, integration drift, SLA breaches, and limited end-to-end visibility. Traditional governance approaches static KPIs, monthly reviews, and retrospective audits are structurally inadequate for dynamic, interdependent multi-vendor environments operating at enterprise scale. This paper presents an AI-Driven Governance Control Plane that unifies operational, contractual, and compliance intelligence through five integrated layers: (1) unified telemetry ingestion, (2) topology-aware dependency modeling via graph databases, (3) AI governance intelligence employing predictive SLA risk scoring, anomaly detection, and NLP-based contract extraction, (4) policy-as-code enforcement via Open Policy Agent (OPA), and (5) intelligent orchestration. A critical design innovation is the integration of SHAP-based explainable AI from the foundation phase, ensuring vendor trust and enabling evidence-based governance decision-making. The framework is purpose-built for organizations using ServiceNow as their ITSM system of record, with bidirectional REST API integration enabling real-time governance actions. A 30-day case study conducted within a global financial services organization managing 7 service vendors and 12 SAP domains demonstrates measurable operational improvements: 82% reduction in incident triage time (from 3–4 hours to 22 minutes), 30% reduction in SLA breach rate, 40% improvement in Mean Time to Resolution (MTTR from 6.4 to 3.8 hours), 57% reduction in cross-vendor ticket bouncing, 91% pre-deployment change collision detection accuracy, and 96% elimination of vendor attribution disputes. First-year cost avoidance totals $2.16M (ROI: 592%) against a $312K platform investment. An 18-month phased implementation roadmap with success criteria and risk mitigation strategies is presented. The framework enables enterprises to transition from reactive, dispute-driven governance to proactive, evidence-driven resilience engineering.
References
[1] Lundberg, S. M., & Lee, S. I. (2017). A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems (NeurIPS), Vol. 30.
[2] Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 785–794.
[3] Gartner. (2023). Market Guide for AIOps Platforms. Gartner Research, ID: G00776541.
[4] AXELOS. (2020). ITIL 4: Create, Deliver and Support. AXELOS Global Best Practice.
[5] Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT, pp. 4171–4186.
[6] Forrester. (2023). The State of IT Service Management. Forrester Research, February 2023.
[7] European Central Bank. (2023). Digital Operational Resilience Act (DORA): Regulatory Technical Standards. ECB/2023/18.
[8] Lipton, Z. C. (2016). The Mythos of Model Interpretability. arXiv:1606.03490.
[9] ibeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why Should I Trust You?": Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD Conference, pp. 1135–1144.
[10] Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining, pp. 413–422.
[11] Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
[12] Ke, G., et al. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems (NeurIPS), Vol. 30.
[13] SAP SE. (2023). SAP Signavio Process Intelligence Documentation. SAP Documentation.
[14] IT Service Management Forum. (2020). SIAM: Service Integration and Management Foundation Guide. Van Haren Publishing.
[15] Goldstein, M., & Uchida, S. (2016). A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. PLOS ONE, 11(4), e0152173.
