The increasing complexity of data-driven decision environments requires artificial intelligence systems that can integrate heterogeneous information, represent decision-relevant knowledge, and support decisions that remain interpretable, scalable, and aligned with sustainability objectives. This paper examines a conceptual architecture for scalable semantic AI systems designed to transform heterogeneous data into structured decision intelligence for sustainable organizational and policy-oriented decision-making. The study adopts a research-and-review methodology based exclusively on the supplied literature, particularly research on automated justification, judgment aggregation, preference aggregation, strategyproof decision mechanisms, computational social choice, and semantic AI infrastructure. The reviewed literature demonstrates that scalable decision intelligence requires more than computational efficiency: it also requires mechanisms for representing preferences, resolving conflicting judgments, explaining outcomes, and validating decision procedures. The proposed framework therefore integrates semantic representation, evidence transformation, preference-aware reasoning, constraint-based validation, collective judgment aggregation, and explainable decision outputs. The analysis indicates that automated reasoning and computational social choice provide useful theoretical foundations for handling conflicting stakeholder objectives, while semantic infrastructure can provide the connective layer required to operationalize these mechanisms over heterogeneous data environments. The paper further identifies transparency, preference conflicts, computational scalability, semantic consistency, and governance as central design challenges. The resulting framework positions sustainable decision intelligence as an integrated socio-technical capability rather than merely an AI prediction function.

Semantic AI, Decision Intelligence, Sustainable Decision-Making, Knowledge Representation

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K. K. Goyal, "Semantic AI Infrastructure for Sustainable Decision Intelligence," 2025 8th International Conference on Informatics and Computational Sciences (ICICoS), Semarang, Indonesia, 2025, pp. 434-439, doi: 10.1109/ICICoS68590.2025.11329920.