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All Journal Journal of Artificial Intelligence and Digital Business
Setyo Budianto
Telkom University
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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AI-Enabled Information Systems and Strategic Alignment: A Systematic Literature Review on Digital Orchestration Farhan Alif Budianto; Muharman Lubis; Iqbal Yulizar Mukti; Setyo Budianto
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 1 (2026): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.6989

Abstract

This paper aims to synthesize the fragmented body of literature on how Artificial Intelligence (AI) transforms the traditional Strategic Alignment Model (SAM). Specifically, the study examines the role of Digital Orchestration as a mediating mechanism between AI capabilities and organizational performance. Using a Systematic Literature Review (SLR) approach guided by PRISMA protocols, this research analyzes 84 peer-reviewed articles published between 2018 and 2026 and indexed in the Scopus and Web of Science databases. The study identifies three main thematic pillars: Cognitive Alignment, Algorithmic Governance, and Human–AI Collaborative Synergy. Overall, these themes indicate that AI is no longer merely an operational support tool but has evolved into an agentic strategic capability that enables continuous sensing, predictive decision-making, and real-time synchronization between business and IT domains. The findings demonstrate a paradigm shift from “Static Fit” toward “Fluid Orchestration.” Theoretically, this study extends the Resource-Based View by positioning agentic AI capability as a higher-order dynamic capability and proposes an AI-Enabled Digital Orchestration Framework to integrate previously fragmented insights. Managerially, the research emphasizes the importance of Dynamic KPIs and Agentic Governance to prevent algorithmic misalignment. Overall, the study advances strategic alignment theory by framing AI-driven strategy as a continuously adaptive orchestration capability in volatile digital ecosystems.  
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
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 1 (2026): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.6990

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.
Data Governance and AI Strategy: A Systematic Synthesis of Information Systems Frameworks for Competitive Advantage Farhan Alif Budianto; Muharman Lubis; Iqbal Yulizar Mukti; Setyo Budianto
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 1 (2026): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.6991

Abstract

The rapid integration of Artificial Intelligence (AI) into organizational strategy has intensified the need for robust data governance mechanisms that ensure data quality, accountability, and strategic alignment. While prior studies have examined data governance and AI strategy separately, a comprehensive synthesis explaining how Information Systems (IS) frameworks bridge technical data management and competitive advantage remains limited. This study addresses this gap by conducting a Systematic Literature Review (SLR) to synthesize key IS frameworks that govern data for AI-driven strategic outcomes. Following the PRISMA 2020 protocol, relevant peer-reviewed articles published between 2018 and 2026 were systematically collected from Scopus, Web of Science, and the AIS eLibrary. A total of 65 high-quality studies were selected for thematic and theoretical analysis. The findings reveal three dominant thematic clusters: algorithmic accountability and ethics, data pedigree and provenance, and the evolving strategic role of the Chief Data Officer (CDO). The synthesis further demonstrates a theoretical shift from static, compliance-oriented governance toward dynamic capabilities grounded in the Resource-Based View. This study contributes to IS and AI strategy literature by re-conceptualizing data governance as a second-order organizational capability that enables sustainable competitive advantage. Practical implications highlight the importance of governance agility, strategic alignment, and trust-building mechanisms in scaling AI initiatives.
Co-Authors Farhan Alif Budianto Iqbal Yulizar Mukti Muharman Lubis