AI-Driven Semantic Frameworks for Sustainable Decision Intelligence

Abstract

The increasing complexity of organizational, environmental, and socio-technical decision environments has created a need for intelligent systems capable of integrating heterogeneous information, identifying contextual relationships, and supporting decisions that balance operational objectives with long-term sustainability. This paper examines AI-drven semantic frameworks as an architectural approach for sustainable decision intelligence, with particular emphasis on semantic representation, contextual learning, multi-task learning, domain adaptation, and lightweight natural language processing. The study adopts a conceptual research-and-review methodology based exclusively on the supplied literature. The reviewed studies provide complementary theoretical foundations: parsing research demonstrates structured linguistic representation, multi-task learning explains how related tasks can share useful information, large language models illustrate scalable semantic inference, and domain-oriented tuning demonstrates mechanisms for adapting AI systems to heterogeneous contexts. These foundations are synthesized into a conceptual semantic decision framework comprising data interpretation, semantic representation, contextual reasoning, adaptive learning, decision synthesis, and sustainability-oriented evaluation layers. The analysis indicates that semantic intelligence can improve decision consistency by connecting heterogeneous information according to contextual meaning rather than treating individual observations independently. However, challenges remain regarding domain transfer, interpretability, computational efficiency, task interference, and the absence of explicit sustainability objectives within many existing AI learning paradigms. The paper therefore positions semantic AI as an enabling infrastructure rather than an autonomous decision-maker and proposes a research direction in which semantic representations, adaptive learning, and sustainability constraints are integrated into a unified decision architecture.