Transactions on Machine Intelligence 2821-1693 9 2 2026 06 01 AI-Enabled Anticipatory Capacity Planning for Smart Government: Integrating Artificial Intelligence, Strategic Foresight, and Queueing Theory under Future Demand Uncertainty 142 154 252085 10.22034/tmi.2026.252085 EN K. Yeganegi Assistant Professor, Department of Industrial Engineering, Islamic Azad University, Za.C, Zanjan, Iran 0000-0003-0421-6258 M. Mohebbi Ph.D. in Futures Studies, Director of Administrative Reform and Performance Evaluation, Management and Planning Organization of Zanjan Province, Zanjan, Iran 0000-0001-6697-2302 Journal Article 2026 02 09 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. https://www.tmachineintelligence.ir/article_252085_b46dc4a276f8a619426bbbbba9e990c7.pdf