Urban technology has advanced rapidly over the past decade, with cities investing heavily in sensors, connectivity, and digital platforms to improve services. These initiatives have delivered meaningful progress, but they have also revealed structural limits: as cities become more complex, they are increasingly focused on transforming existing data into coordinated, real-time action.
Smart cities taught us how to connect urban systems and make city operations more visible through sensors and integrated data. Cities are now looking for ways to strengthen these foundations with more intelligent tools that enhance decision-making and service delivery. Rather than adding more devices, the focus is shifting toward building the capability to act at speed, and toward open, cross-domain platforms that support co-creation across industry, government, and academia. In this context, AI becomes a natural extension of the Smart City framework — enriching and empowering existing strategies rather than replacing them.
What we refer to as AI City describes this AI enabled operational capability within Smart City development, sometimes framed as Smart City 4.0, or AI City 1.0, to reflect the role of AI as an empowering layer. An AI enhanced Smart City uses real-time decisioning and orchestration, with models continuously predicting demand, optimizing resources, and coordinating services.
Smart cities are largely about sensing and integration, helping them see what is happening. AI builds on these capabilities by adding deeper contextual understanding, predictive insight, and coordinated response across services. Together, these capabilities help cities transform data into consistent, real-time action.
A five-layer framework for reliable, scalable AI
Within Smart City 4.0, AI City decisioning models sit at the core of how cities turn operational data into timely, consistent actions—reliably and at scale.
This occurs across three steps. First, the city’s decisioning system uses signals from sensors, systems, and frontline operations to predict what is likely to happen next. This can include demand peaks in public transit, emerging congestion patterns, rising equipment‑failure risk in utilities, hospital service bottlenecks, or incident probability during severe weather.
Second, the decisioning system determines and optimizes the best intervention under real-world constraints—service priorities, budgets, regulations, safety requirements, and fairness—by combining optimization methods with policy and rule frameworks as well as machine learning. Third, the city implements these decisions through workflows and control systems (sometimes automated, sometimes human-approved), then measures outcomes to continuously improve models and policies.
For this loop to operate across an entire city as part of a Smart City framework, decisioning must be supported by a coherent foundation spanning sovereign compute, secure platforms, governed models, real-world applications, and continuous innovation. This foundation allows cities to run diverse, real-time services—from dynamically adjusting traffic signals to reduce congestion, to reallocating EV charging loads during peak demand, to triggering flood‑risk alerts and coordinating emergency response teams across agencies.
To move from pilots to dependable citywide services, these elements must operate as one continuous loop. Trusted city signals flow through secure platforms into locally governed models, which drive coordinated decisions and workflows across mobility, energy, safety, and public services. This is an iterative process that involves learning from outcomes and improving over time.
This is why embedding AI City capabilities within Smart City 4.0 is not just a technology upgrade—it establishes a new operating model.
When this framework works as one, cities gain the capability to anticipate demand, orchestrate resources across agencies, and continuously improve service quality through trust and transparency.
What does this look like in the real world?
Mobility is often the first domain where cities experience the gap between visibility and real outcomes. Turning data into smoother traffic flow, easier parking, and more convenient EV charging requires systems that can sense demand, make real-time decisions, and execute them reliably. At ASUS, work in areas such as AIoT‑enabled mobility services, smart parking, and integrated payment and charging systems has helped shape a practical understanding of what it takes to run these services end to end.
Across Taiwan, several deployments already show how AI City concepts translate into daily operations. In Tainan, the “Demonstration AI City” initiative connects city data with the computing foundation needed for sovereign AI. This includes smart transportation applications such as Navya’s autonomous shuttle solutions, ASUS-developed smart parking pillars with 5G integration, and a citywide smart parking data platform that unifies operational and financial transaction data. Tainan has also introduced an AI‑enabled Pharmacist Agent Service, which analyzes prescriptions in real time to flag potential drug interactions, dosage issues, or contraindications—improving medication safety and reducing risks associated with polypharmacy.
AI City applications extend beyond mobility and healthcare. In Hualien, an AI Intersection ASUS Accident Detection System identifies collisions the moment they occur and triggers roadside equipment to respond, enabling faster intervention. In Keelung, a Low‑Carbon Transportation Decision Platform integrates energy management with electric bus fleet planning to support the city’s net‑zero strategy. In metropolitan transit environments, ASUS AISDetector voiceprint monitoring enables early anomaly detection inside tunnels and stations, reducing the likelihood of train suspensions and lowering maintenance costs.
AI is also enhancing safety and environmental resilience. At the Xinzhuang Pumping Station, ASUS AISSENS wireless sensors provide high‑frequency vibration and temperature monitoring to support predictive maintenance—critical for preventing equipment failures during heavy rainfall.
International deployments further demonstrate the versatility of ASUS solutions, with projects in Portugal, Italy, and Japan covering scenarios such as forest‑fire smoke detection, ATM security monitoring, and wildlife‑related risk detection. And at Tainan’s Water Resources Center, a Digital Twin transforms the city’s physical water infrastructure into an interactive 3D environment, improving transparency, education, and operational management.
Across all these examples, the focus remains operational: the signals cities can use, the decisions they can make in real time, how actions flow into control rooms and field operations, and how outcomes are measured and governed with appropriate human oversight. Building AI City operating capabilities within Smart City programs depends on close collaboration between cities, industry, and research partners—grounded in real deployments, measurable results, and shared accountability.
At ASUS, we are committed to working with city leaders and ecosystem partners to turn AI capabilities into dependable public services. Together, we are building resilient infrastructure that can scale, earning trust through security and governance, and ensuring operational excellence that turns insights into reliable execution.
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Autor(en)/Author(s): Samson Hu
Dieser Artikel ist neu veröffentlicht von / This article is republished from: Asus Press, 18.03.2026
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