The transition from conventional business intelligence (BI) toward semantic, AI-enabled decision intelligence requires infrastructure capable of integrating heterogeneous information, reasoning over complex relationships, and supporting decisions under uncertainty. Traditional BI architectures predominantly emphasize structured data integration, descriptive analytics, and predefined query logic, whereas next-generation semantic AI infrastructure seeks to connect data, business concepts, contextual relationships, and intelligent reasoning within a unified decision environment. This research and review paper develops a conceptual framework for such infrastructure by synthesizing the theoretical foundations of learning, reasoning, dependency management, and intelligent search represented in the provided literature. Particular attention is given to clause-learning mechanisms, quantified reasoning, dependency learning, heuristic decision-making, and practical satisfiability solving. These foundations are interpreted as computational principles for semantic constraint management and decision-oriented AI infrastructure. The proposed framework consists of semantic data representation, knowledge integration, dependency-aware reasoning, constraint-driven inference, adaptive decision orchestration, and sustainability-oriented intelligence layers. The analysis indicates that semantic infrastructure can improve explainability, contextual consistency, reasoning efficiency, and the alignment between organizational data and strategic decisions. However, computational complexity, semantic ambiguity, scalability, model governance, and integration overhead remain important limitations. The paper argues that sustainable BI should evolve from a reporting-centered architecture toward a reasoning-centered infrastructure in which semantic relationships and decision constraints are treated as first-class computational objects.