Semantic AI Architecture for Sustainable and Intelligent Decision-Making
The increasing complexity of organizational, environmental, and operational decision environments requires artificial intelligence systems that can integrate heterogeneous information, recognize previously unseen situations, and support decisions under uncertainty. Conventional AI pipelines frequently depend on predefined classes, static datasets, and narrowly specified prediction objectives, limiting their ability to adapt when decision contexts evolve. This paper proposes a conceptual Semantic AI Architecture for Sustainable and Intelligent Decision-Making that combines semantic representation, open-world learning, incremental learning, attention-based representation, self-supervised feature extraction, and information-theoretic reasoning. The architecture is developed through a structured synthesis of the supplied literature and is positioned as a research-oriented framework rather than an empirical benchmark. The analysis demonstrates that sustainability-oriented decision intelligence requires more than predictive accuracy: it requires contextual interpretation, novelty awareness, continuous learning, and mechanisms for integrating multimodal evidence. Open-world recognition provides a foundation for identifying unknown situations, while incremental learning enables adaptation without complete retraining. Transformer-based representations and self-supervised learning can improve the semantic quality of continuously acquired data, while information theory provides a theoretical basis for evaluating information relevance and uncertainty. The proposed architecture therefore connects semantic interpretation with adaptive AI infrastructure and sustainable decision processes. The study identifies architectural advantages, operational trade-offs, and limitations, particularly concerning semantic consistency, computational cost, uncertainty management, and evaluation of genuinely unknown decision states.