The Evolution of Decision Support Systems (DSS) to Strategic AI: A Systematic Review of Architectural Shifts and Business Value

Farhan Alif Budianto; Muharman Lubis; Iqbal Yulizar Mukti; Setyo Budianto

doi:10.31004/riggs.v5i1.6990

Authors

Farhan Alif Budianto Telkom University
Muharman Lubis Telkom University
Iqbal Yulizar Mukti Telkom University
Setyo Budianto Telkom University

DOI:

https://doi.org/10.31004/riggs.v5i1.6990

Keywords:

Decision Support Systems, Strategic Artificial Intelligence, Architectural Autonomy, Business Value, Generative AI, Systematic Literature Review

Abstract

This study examines the evolution of Decision Support Systems (DSS) toward Strategic Artificial Intelligence (SAI) by systematically analyzing architectural shifts and their implications for business value creation. Using a Systematic Literature Review (SLR) approach based on the PRISMA protocol, data were collected from Scopus, Web of Science, and IEEE Xplore databases covering publications from 2000 to 2025. A total of 85 peer-reviewed articles were selected after a rigorous screening and eligibility process. The findings reveal a progressive transition from model-driven, on-premise DSS architectures to cloud-native, agent-based, and LLM-integrated systems characterized by architectural autonomy and decentralized AI mesh structures. This transformation reshapes organizational decision-making from reactive data support to proactive and generative strategic insight. The study proposes a DSS–SAI Convergence Framework that explains how architectural autonomy reduces strategic latency and enhances agility, competitive advantage, and innovation capability. The results highlight that Strategic AI is not merely a technological upgrade but a fundamental shift in organizational intelligence and value logic, requiring new managerial competencies in decision engineering and explainable AI governance. Furthermore, the review identifies emerging risks—including algorithmic drift, governance latency, and configuration complexity—that may undermine strategic alignment if not properly managed. The study contributes to the information systems literature by integrating architectural, organizational, and governance perspectives into a unified analytical lens and offers practical guidance for firms seeking to operationalize AI-driven strategic decision infrastructures.

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