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. 

Artificial Intelligence, Semantic Intelligence, Sustainable Decision-Making, Decision Intelligence

Amini, A., & Cotterell, R. (2022). On parsing as tagging. In Proceedings of the 2022Conference on Empirical Methods in Natural Language Processing.

Bingel, J., & Søgaard, A. (2017). Identifying beneficial task relations for multi-task learn-ing in deep neural networks. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics.

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan,A., Shyam, P., Sastry, G., Askell, A., et al. (2020). Language models are few-shotlearners. In Advances in neural information n processing systems.

Chen, X., Li, L., Deng, S., Tan, C., Xu, C., Huang, F., Si, L., Chen, H., & Zhang, N.(2022). Lightner: A lightweight tuning paradigm for low-resource ner via pluggable prompting. In Proceedings of the 29th International Conference on Computational Linguistics.

Chen, X., Li, L., Qiao, S., Zhang, N., Tan, C., Jiang, Y., Huang, F., & Chen, H. (2023).One model for all domains: Collaborative domain-prefix tuning for cross-domain ner.In Proceedings of the Thirty-Second International Joint Conference on Artificial In-telligence, IJCAI-23.

Cocke, J. (1969).Programming languages and their compilers: Preliminary notes. New York University.

Collins, M. (1997). Three generative, lexicalised models for statistical parsing. In35thAnnual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics.

Crawshaw, M. (2020). Multi-task learning with deep neural networks: A survey. InarXiv.

Cui, L., Yang, S., & Zhang, Y. (2022). Investigating non-local features for neural con-stituency parsing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics.

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.