AI-Enabled Information Systems and Strategic Alignment: A Systematic Literature Review on Digital Orchestration

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.v5i1.6989

Keywords:

Artificial Intelligence, Strategic Alignment, Digital Orchestration, Information Systems, Systematic Literature Review

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.

 

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References

H. Benbya, S. Pachidi, and S. Jarvenpaa, “Special issue editorial: Artificial intelligence in organizations: Implications for information systems research,” J. Assoc. Inf. Syst., vol. 22, no. 2, p. 10, 2021.

N. Venkatraman, J. C. Henderson, and S. Oldach, “Continuous strategic alignment: Exploiting information technology capabilities for competitive success,” Eur. Manag. J., vol. 11, no. 2, pp. 139–149, 1993.

I. M. Enholm, E. Papagiannidis, P. Mikalef, and J. Krogstie, “Artificial intelligence and business value: A literature review,” Inf. Syst. Front., vol. 24, no. 5, pp. 1709–1734, 2022.

N. Sambamurthy and M. Kamaraju, “Reconfigurable AI-enabled vectored median filter for real-time image denoising and edge preservation in FPGA-based smart imaging systems,” Multimed. Tools Appl., vol. 84, no. 30, pp. 37311–37325, 2025.

P. Mikalef and M. Gupta, “Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance,” Inf. Manag., vol. 58, no. 3, p. 103434, 2021.

P. Grover, A. K. Kar, and Y. K. Dwivedi, “Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions,” Ann. Oper. Res., vol. 308, no. 1, pp. 177–213, 2022.

D. J. Teece, “Business models and dynamic capabilities,” Long Range Plann., vol. 51, no. 1, pp. 40–49, 2018.

P. C. Verhoef et al., “Digital transformation: A multidisciplinary reflection and research agenda,” J. Bus. Res., vol. 122, pp. 889–901, 2021, doi: https://doi.org/10.1016/j.jbusres.2019.09.022.

D. J. Teece, “The foundations of enterprise performance: Dynamic and ordinary capabilities in an (economic) theory of firms,” Acad. Manag. Perspect., vol. 28, no. 4, pp. 328–352, 2014.

S. Chatterjee, N. P. Rana, and Y. K. Dwivedi, “How does business analytics contribute to organisational performance and business value? A resource-based view,” Inf. Technol. People, vol. 37, no. 2, pp. 874–894, 2024.

R. Ulfsnes et al., “Responsible AI in Agile Software Engineering-An Industry Perspective,” in International Conference on Agile Software Development, 2024, pp. 33–41.

A. F. S. Borges, F. J. B. Laurindo, M. M. Spínola, R. F. Gonçalves, and C. A. Mattos, “The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions,” Int. J. Inf. Manage., vol. 57, p. 102225, 2021.

S. Akter, Y. K. Dwivedi, K. Biswas, K. Michael, R. J. Bandara, and S. Sajib, “Addressing algorithmic bias in AI-driven customer management,” J. Glob. Inf. Manag., vol. 29, no. 6, pp. 1–27, 2021.

M. M. Mariani, R. Perez‐Vega, and J. Wirtz, “AI in marketing, consumer research and psychology: A systematic literature review and research agenda,” Psychol. Mark., vol. 39, no. 4, pp. 755–776, 2022.

D. Guan, Z. Wang, W. Han, and Y. Pei, “Artificial intelligence usage, breakthrough innovation, and innovation performance in high-tech enterprises: the nonlinear moderating role of Not-Invented-Here Syndrome,” Front. Artif. Intell., vol. 8, p. 1699860, 2026.

M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” bmj, vol. 372, 2021.

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Published

07-03-2026

How to Cite

[1]
F. A. Budianto, M. Lubis, I. Y. Mukti, and S. Budianto, “AI-Enabled Information Systems and Strategic Alignment: A Systematic Literature Review on Digital Orchestration”, RIGGS, vol. 5, no. 1, pp. 7216–7224, Mar. 2026.