International Journal of Scientific Research and Engineering Development

Enterprises generate vast volumes of operational, financial, customer, workforce, supply-chain, and external information, yet business decisions often remain fragmented across reports, applications, meetings, spreadsheets, and individual experience. Decision Intelligence provides a structured discipline for improving how organizations define, model, support, execute, monitor, and learn from decisions. This paper proposes an Enterprise Decision Intelligence Framework that integrates enterprise architecture, business data products, semantic models, analytics, Artificial Intelligence, Knowledge Graphs, process intelligence, rules, optimization, human judgment, governance, and continuous learning. The framework defines decision domains, decision rights, information inputs, analytical methods, AI services, explainability, workflow integration, decision evidence, outcome monitoring, and feedback loops. It also introduces a layered reference architecture using SAP Business Technology Platform, SAP Business Data Cloud, SAP Datasphere, SAP HANA Cloud, SAP Analytics Cloud, SAP Signavio, SAP LeanIX, SAP Integration Suite, SAP AI services, and enterprise observability. The framework enables organizations to improve decision speed, consistency, transparency, accountability, resilience, and measurable business outcomes while retaining appropriate human control.

Sanjeeve Kumar Gajadi, "Enterprise Decision Intelligence Framework: An Enterprise Architecture Approach Integrating Business Data, Analytics, AI, Knowledge Graphs, Process Intelligence, Governance, and Human Decision-Making" International Journal of Scientific Research and Engineering Development, V9(5): Page(163-166) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.