Inside South Korea’s Plan to Turn a Smart City Into an AI City

The smart city era was built on sensors, cameras, and dashboards that told urban managers what had already happened. A new study argues that this reactive model is now obsolete, and it maps out in unusual detail how one of the world’s most wired districts could leap into something genuinely different: a city where artificial intelligence does not merely report on urban life but anticipates, simulates, and increasingly acts within it. The setting is South Korea’s Incheon Free Economic Zone, or IFEZ, a district-scale zone established in 2003 that encompasses the purpose-built cities of Songdo, Yeongjong, and Cheongna, and the blueprint comes from a framework published in the journal Discover Cities.

The study, authored by Sangho Lee of the Graduate School of Logistics at Incheon National University, formalizes what it calls the IFEZ AI City Framework, a four-pillar architecture designed to carry the zone beyond conventional smart city practice. The timing is not accidental. In December 2025, IFEZ formally declared its AI CITY INITIATIVEs at the IFEZ Smart City Summit, and the announcement was deliberately aligned with the January 2026 enforcement of South Korea’s Framework Act on Artificial Intelligence, widely known as the AI Basic Act. The study treats that coincidence of infrastructure maturity and new national law as a defining institutional moment, one that could make IFEZ a testbed for city-scale AI deployment and validation.

The intellectual core of the paper is a distinction that urban scholars have been sharpening for several years: the difference between a smart city and an AI city. Smart cities, as they evolved through the 2000s and 2010s, were fundamentally about connectivity and efficiency, using IoT sensor networks, cloud computing, and centralized data platforms to optimize existing processes such as traffic management, energy distribution, and public safety. AI cities, by contrast, are conceived as systems that learn continuously, predict future conditions, and derive solutions autonomously. The paper draws on recent theoretical work on urban artificial intelligence and AI urbanism, including concepts such as City AI and Agentic Urban AI, in which AI systems move from recommending actions to reasoning, planning, and executing multi-step decisions on their own.

What makes IFEZ a plausible candidate for this transition is the sheer density of its existing digital nervous system. The zone manages approximately 9,000 on-site urban facilities, CCTV units, traffic devices, and emergency response terminals, all feeding a centralized Integrated Operations Center supported by high-performance GPUs. That infrastructure generates continuous, high-volume operational data suitable for AI model training and real-time inference. Yet the study is candid about the gaps: its SWOT analysis identifies fragmented data infrastructure and insufficient GPU and high-performance computing capacity for large-scale model training as current constraints, meaning that further integration and computing expansion are prerequisites rather than afterthoughts.

The first pillar of the framework is the AI-based urban model architecture, the technical backbone of the proposed AI city. Its centerpiece is a proposed AI City Brain platform, a centralized AI engine that would integrate urban data, simulation models, and decision processes through an ontology-based architecture, acting as a semantic layer that connects heterogeneous data streams from traffic sensors, environmental monitors, and administrative databases. This sits alongside a proposed unified data lakehouse combining the flexibility of data lakes with the performance of data warehouses, and digital twin models that would dynamically represent physical urban assets and allow scenario simulation before any real-world policy or operational change is made. Predictive and AI-assisted operations, from traffic flow and energy consumption forecasting to predictive maintenance, would run on top of this foundation, with selected functions progressing toward higher automation only where technically reliable, legally permissible, and subject to defined human oversight.

The second and third pillars are economic rather than infrastructural, and they reveal the framework’s most distinctive bet: that an AI city must be an AI economy. Pillar two, the AI-bio industry enablement system, builds on Songdo’s biopharmaceutical cluster, home to Samsung Biologics and Celltrion, and proposes an AI bio data hub integrating biomedical, clinical, and genomic data for precision medicine and drug discovery, federated data sharing that preserves data sovereignty, AI-driven laboratory automation, and interdisciplinary talent programs drawing on the five international universities at the Incheon Global Campus, including SUNY Stony Brook, George Mason, Ghent University, the University of Utah, and FIT. Pillar three proposes an AI startup ecosystem built around a real-city sandbox, an expanded Incheon Startup Park, and a Startup-City Co-Growth Model in which startup solutions that pass technical, legal, ethical, and operational validation could be progressively integrated into city services.

The fourth pillar translates all of this into citizen-facing services: AI-assisted analysis of predefined safety events such as crowd congestion, falls, and fire indicators; multilingual AI chatbots for administrative services suited to IFEZ’s internationally diverse population; and an AI city operation center that would integrate all IoT and sensor data streams to automate maintenance scheduling and monitor KPIs aligned with ESG and UN Sustainable Development Goal indicators. Crucially, the framework insists that AI-generated alerts in public safety be treated as decision-support inputs rather than as evidence of unlawful or dangerous intent, with consequential actions remaining subject to human review.

Implementation is structured as a three-phase roadmap running from 2026 to 2030. Phase one, in 2026, focuses on what the study calls data assetization, the deliberate transformation of raw smart city data into AI-ready assets that are preprocessed, labeled, and standardized so that AI companies can use them immediately; its flagship initiative proposes de-identification and event labeling of CCTV footage to create safety-oriented training datasets, subject to privacy and data-protection compliance. Phase two, spanning 2027 and 2028, would expand the AI City Sandbox across the three districts and deploy edge AI infrastructure to process analytics closer to camera and sensor endpoints, reducing latency and privacy exposure. Phase three, from 2029 to 2030, aims to institutionalize validated AI services within the Integrated Operations Center and to standardize and internationally disseminate a K-AI City model, though the study is careful to frame export as a future objective rather than an accomplished fact.

Governance receives as much attention as technology, reflecting the constraints of the AI Basic Act, which defines High-Impact AI systems that materially affect citizens’ lives, safety, and fundamental rights and imposes obligations for prior notification, risk management, and algorithmic explainability. The framework’s Responsible AI by Design approach recommends explainable AI for consequential administrative decisions, human-in-the-loop oversight, de-identification, Zero-Trust security principles, and a sovereign AI infrastructure strategy to reduce dependence on external platforms for sensitive public-sector applications. It also confronts the darker risks of AI-enabled surveillance head-on, including algorithmic bias, false positives, function creep, and disproportionate monitoring, and prescribes purpose limitation, data minimization, periodic bias audits, and public channels for review and redress.

The study is equally explicit about its own limits. The framework is conceptual, derived from qualitative synthesis of academic literature, policy documents, and IFEZ materials rather than from empirical validation, and the author acknowledges that his affiliation with the IFEZ administration may introduce institutional bias. Many components, including the AI City Brain platform and the full-scale sandbox, are proposed or planned rather than operational. Yet the paper’s transferable design principles, that data assetization should precede AI deployment, that vertical sector-specific AI and horizontal urban AI should develop together, that regulation can function as an innovation-enabling capability rather than merely a compliance burden, and that modularity and interoperability should be embedded from the outset, offer a genuinely replicable architecture. If the roadmap holds, the world’s first district-scale AI city may be less a technological moonshot than a disciplined exercise in institutional design.

Subject of Research: A strategic framework for transitioning the Incheon Free Economic Zone from a smart city to an AI city model

Article Title: A strategic framework for transitioning from smart city to AI city in the Incheon free economic zone (IFEZ)

Article References: Lee, S. (2026). A strategic framework for transitioning from smart city to AI city in the Incheon free economic zone (IFEZ). Discover Cities, 3(1), Article 198. https://doi.org/10.1007/s44327-026-00379-w

Image Credits: AI Generated

DOI: 10.1007/s44327-026-00379-w

Keywords: AI city, smart city, IFEZ, urban artificial intelligence, digital twin, AI governance, free economic zone, Songdo, AI Basic Act, data assetization, agentic AI, urban policy