Transactions on Machine Intelligence

Transactions on Machine Intelligence

AI-Enabled Anticipatory Capacity Planning for Smart Government: Integrating Artificial Intelligence, Strategic Foresight, and Queueing Theory under Future Demand Uncertainty

Authors
K. Yeganegi 1
M. Mohebbi 2
1 Assistant Professor, Department of Industrial Engineering, Islamic Azad University, Za.C, Zanjan, Iran
2 Ph.D. in Futures Studies, Director of Administrative Reform and Performance Evaluation, Management and Planning Organization of Zanjan Province, Zanjan, Iran
10.22034/tmi.2026.252085
Abstract
The future operating environment of smart government will be increasingly influenced by artificial intelligence (AI), rapidly changing citizen expectations, demographic shifts, digital service adoption, regulatory changes, cyber disruptions, public-health emergencies, and other sources of demand uncertainty. In such an environment, conventional reactive capacity planning may be insufficient to maintain service quality and operational resilience. This study develops an AI-enabled anticipatory capacity-planning framework that integrates artificial intelligence, strategic foresight, scenario construction, and queueing theory to support resilient and adaptive smart government services. The proposed framework uses AI as an analytical and decision-support layer for identifying emerging demand patterns, supporting future-demand estimation, and translating alternative future conditions into quantitative arrival-rate assumptions. These assumptions are subsequently evaluated through an M/M/c queueing model to examine their implications for utilization, queue length, and waiting time. Three alternative capacity configurations and four future-demand scenarios are analyzed to illustrate how AI-supported foresight can be connected to quantitative service-system design. The results demonstrate a strongly nonlinear relationship between system utilization and service performance. At an arrival rate of 40 requests per hour and a service rate of 15 requests per hour per channel, increasing capacity from three to four channels reduces average waiting time from approximately 9.57 minutes to 1.14 minutes. Adding a fifth channel further reduces waiting time but results in lower capacity utilization. Scenario stress testing indicates that a four-channel system performs effectively under stable and accelerated digital adoption; however, congestion increases rapidly as demand approaches total service capacity. The findings suggest that AI should not be viewed solely as a technology for automating public services. When integrated with strategic foresight and queueing analysis, AI can contribute to anticipatory governance by supporting early identification of emerging demand pressures, detection of critical capacity thresholds, scenario-based stress testing, and adaptive resource planning. In this framework, queueing theory provides the quantitative mechanism for translating AI-supported future-demand assumptions into measurable operational consequences, while strategic foresight provides the structure for dealing with uncertainty and alternative futures. The study contributes to smart government, futures studies, artificial intelligence, and operations research by proposing a transparent analytical bridge between AI-supported demand intelligence, qualitative future scenarios, and quantitative capacity planning. The framework can assist public-sector decision-makers in moving from reactive resource allocation toward anticipatory, data-informed, and adaptive capacity management.
Keywords
Artificial Intelligence
AI-Enabled Capacity Planning
Strategic Foresight
Smart Government
Queueing Theory
Anticipatory Governance
Digital Resilience
Future Demand
Adaptive Capacity
[1]      Pedrosa, G. V., Kosloski, R. A. D., de Menezes, V. G., Iwama, G. Y., da Silva, W. C. M. P., & de Figueiredo, R. M. da C. (2020). A systematic review of indicators for evaluating the effectiveness of digital public services. Information, 11(10), 472. https://doi.org/10.3390/info11100472
[2]      Latupeirissa, J. J. P., Dewi, N. L. Y., Prayana, I. K. R., Srikandi, M. B., Ramadiansyah, S. A., & Pramana, I. B. G. A. Y. (2024). Transforming public service delivery: A comprehensive review of digitization initiatives. Sustainability, 16(7), 2818. https://doi.org/10.3390/su16072818
[3]      Escobar, F., Almeida, W. H. C., & Varajão, J. (2023). Digital transformation success in the public sector: A systematic literature review of cases, processes, and success factors. Information Polity, 28(1), 61–81. https://doi.org/10.3233/IP-211518
[4]      Haug, N., Dan, S., & Mergel, I. (2024). Digitally-induced change in the public sector: A systematic review and research agenda. Public Management Review, 26(7), 1963–1987. https://doi.org/10.1080/14719037.2023.2234917
[5]      Slaughter, R. A. (1995). The foresight principle: Cultural recovery in the 21st century. Adamantine Press.
[6]      Voros, J. (2003). A generic foresight process framework. Foresight, 5(3), 10–21. https://doi.org/10.1108/14636680310698379
[7]      Wilkinson, A., & Kupers, R. (2013). Living in the futures. Harvard Business Review, 91(5), 118–127.
[8]      Organisation for Economic Co-operation and Development. (2019). Strategic foresight for better policies: Building effective governance in the face of uncertain futures. OECD Publishing.
[9]      Erlang, A. K. (1909). The theory of probabilities and telephone conversations. Nyt Tidsskrift for Matematik B, 20, 33–39.
[10]   Kendall, D. G. (1953). Stochastic processes occurring in the theory of queues and their analysis by the method of the embedded Markov chain. The Annals of Mathematical Statistics, 24(3), 338–354. https://doi.org/10.1214/aoms/1177728975
[11]   Kingman, J. F. C. (1961). The single server queue in heavy traffic. Mathematical Proceedings of the Cambridge Philosophical Society, 57(4), 902–904. https://doi.org/10.1017/S0305004100036094
[12]   Cooper, R. B. (1981). Introduction to queueing theory (2nd ed.). North-Holland.
[13]   Kleinrock, L. (1975). Queueing systems, volume I: Theory. Wiley-Interscience.
[14]   Kleinrock, L. (1976). Queueing systems, volume II: Computer applications. Wiley-Interscience.
[15]   Gross, D., Shortle, J. F., Thompson, J. M., & Harris, C. M. (2008). Fundamentals of queueing theory (4th ed.). Wiley. https://doi.org/10.1002/9781118625651
[16]   Medhi, J. (2003). Stochastic models in queueing theory (2nd ed.). Academic Press.
[17]   Taha, H. A. (2017). Operations research: An introduction (10th ed.). Pearson.
[18]   Ciancarini, P., Giancarlo, R., & Grimaudo, G. (2024). Digital transformation in the public administrations: A guided tour for computer scientists. IEEE Access, 12, 22841–22865. https://doi.org/10.1109/ACCESS.2024.3363075
[19]   Ciancarini, P., Giancarlo, R., & Grimaudo, G. (2024). Scrum@PA: Tailoring an agile methodology to the digital transformation in the public sector. Information, 15(2), 110. https://doi.org/10.3390/info15020110
[20]   Trottier, J., Van Woensel, W., Wang, X., Mallur, K., El-Gharib, N., & Amyot, D. (2025). Using process mining with pre- and post-intervention analysis to improve digital service delivery: A governmental case study. In Process mining workshops (ICPM 2024) (Lecture Notes in Business Information Processing, Vol. 533, pp. 572–585). Springer. https://doi.org/10.1007/978-3-031-82225-4_42
[21]   Alenezi, M. (2022). Understanding digital government transformation. arXiv. https://arxiv.org/abs/2202.01797
[22]   Shibambu, A., & Ngoepe, M. (2025). Enhancing service delivery through digital transformation in the public sector in South Africa. Global Knowledge, Memory and Communication, 74(11), 63–76. https://doi.org/10.1108/GKMC-12-2023-0476
[23]   D'Auria, B., Adan, I. J. B. F., Bekker, R., & Kulkarni, V. G. (2022). An M/M/c queue with queueing-time dependent service rates. European Journal of Operational Research, 299(2), 566–579. https://doi.org/10.1016/j.ejor.2021.12.023
[24]   Mergel, I., Edelmann, N., & Haug, N. (2019). Defining digital transformation: Results from expert interviews. Government Information Quarterly, 36(4), 101385. https://doi.org/10.1016/j.giq.2019.06.002
[25]   Moore, J. F. (1993). Predators and prey: A new ecology of competition. Harvard Business Review, 71(3), 75–86.
[26]   Walker, W. E., Lempert, R. J., & Kwakkel, J. H. (2013). Deep uncertainty. In S. I. Gass & M. C. Fu (Eds.), Encyclopedia of operations research and management science (pp. 395–402). Springer. https://doi.org/10.1007/978-1-4419-1153-7_1140
[27]   Lempert, R. J., Popper, S. W., & Bankes, S. C. (2003). Shaping the next one hundred years: New methods for quantitative, long-term policy analysis. RAND Corporation. https://doi.org/10.7249/MR1626
[28]   van der Heijden, K. (2005). Scenarios: The art of strategic conversation (2nd ed.). John Wiley & Sons.
Transactions on Machine Intelligence
Volume 9, Issue 2
Spring 2026
Pages 142-154

  • Receive Date 09 February 2026
  • Revise Date 17 April 2026
  • Accept Date 07 May 2026