The World Cup's lesson for smart cities: Making AI work as one

Asia Sheikh

Sep 28

As Singapore links more smart systems, FIFA World Cup 2026 offers a high-pressure lesson: shared operational data can help teams coordinate when disruptions cross boundaries.Singapore has no shortage ...

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The harder question is what happens when several of them need to work together all at once.\nSince launching its Smart Nation initiative in 2014, the country has applied data and digital technology across transport, urban planning and public services. Singapore's update to its National AI Strategy sets the direction for the next phase: deepening AI use across sectors and government agencies while strengthening the foundations for ecosystem integration, data and trust.\nThat shift matters because a city can have intelligent systems in every corner, but the big question is how these systems can respond as one.\nA transport operator may see congestion building, an infrastructure team may detect a technical fault, and emergency services may be responding to a related incident. If those signals remain in separate systems, decision-makers are left to piece together the situation while it is still unfolding.\nA global football tournament may seem an unlikely place to look for answers. Yet FIFA World Cup 2026 presented a concentrated version of the same challenge. Across 104 matches in 16 host cities and three countries, tens of thousands of people and systems had to work in concert.\nVenue operations, ticketing, security, logistics, communications and broadcast delivery involved different teams and priorities, but a disruption in one area could quickly spill into another. Lenovo deployed and managed more than 35,000 technology assets across over 600 official FIFA sites, with little tolerance for downtime or fragmented decision-making.\nTechnology mattered. Its value, however, lay in turning information from across the tournament into a shared basis for action.\nSeeing the same event at the same time\nAcross Asia Pacific, governments are developing common digital foundations to connect urban data and support better planning and operations. In Singapore, Punggol Digital District's Open Digital Platform uses a 3D digital twin and data from more than 20,000 sensors to integrate systems spanning building performance, occupancy, environmental conditions and energy use. Japan's Project PLATEAU has extended the coverage of its open 3D city models to approximately 250 municipalities, supporting applications such as mobility planning and disaster resilience. In Australia, Digital Twin Victoria brings 2D, 3D and live data into one platform for government, industry, researchers and the public.\nThese efforts differ in scale and purpose, but they reflect the same principle: urban information becomes more useful when it can be accessed and understood beyond the system or organisation that produced it.\nYet connecting data does not automatically produce coordinated action. Agencies may still rely on different processes, alerts and lines of authority. The gaps become most visible when an event cuts across several services and decisions must be made quickly.\nThis is where FIFA's experience becomes instructive. The tournament brought information from more than 40 functional areas into a single, real-time operational view through its Intelligent Command Center, delivered by Lenovo. The platform monitored 1.3 million events every second and was used by 550 FIFA personnel, giving teams a shared view of activity across individual venues and the wider tournament.\nRather than replacing the expertise of security specialists, venue managers or technology operators, it helped them identify how developments in one area might affect another and coordinate their response based on the same information.\nApplying that principle to a city requires more than linking databases. Computing and data must connect across devices, edge locations, data centres and cloud environments. Information must remain protected while reaching authorized users with enough context to be useful. AI can then identify patterns, flag emerging risks and suggest responses that teams can assess within the wider operational picture.\nIntelligence at the edge can improve individual systems. Without a common operational picture, however, it may remain a collection of isolated signals.\n Scaling intelligence through replication\nConnecting systems is only half the challenge. Cities also need a better way to scale the intelligence running through them.\nMost smart-city programmes begin with individual pilots. One agency might build a model to forecast demand, another might introduce an anomaly-detection tool, and a third might use AI to summarise operational reports. Each can solve a legitimate problem.\nThe difficulty begins when every team must separately develop, test, and govern capabilities that perform similar tasks. Costs and complexity rise, systems multiply, and useful ideas become difficult to extend beyond the environments in which they were created.\nAs governments move from experimentation towards wider adoption, the test is no longer whether an AI tool can work once. It is whether a proven model, agent or use case can be adapted for another service with the appropriate data, permissions, performance thresholds and safeguards.\nThis need for reuse also shaped the FIFA World Cup technology environment. Venues and functions had different requirements, but the underlying infrastructure could not be rebuilt for every location. A connected environment spanning devices, edge computing, data-center infrastructure, AI-enabled solutions and operational support allowed capabilities to be applied across different settings.\nDoing this at scale requires a consistent way to deploy, monitor and govern AI across agents, workplace systems and hybrid infrastructure. Lenovo applies this principle through xIQ, within its broader Hybrid AI Advantage approach. The objective is to make useful AI repeatable, manageable and adaptable rather than rebuilding it each time.\nFor cities, reuse does not mean forcing every agency onto a uniform system or copying a capability into a new environment without review. Transport, healthcare and public safety have different operational and regulatory requirements. Any model, agent or use case must therefore be adapted to the relevant data, risks and decisions while preserving the controls each service requires.\nAnd that changes how AI scale should be measured. The number of pilots launched reveals little on its own. A more meaningful test is how safely and consistently proven capabilities can move into wider use-and whether they continue to deliver value once they get there.\nCoordination does not remove accountability\nA shared view can improve coordination, but it also raises the stakes for accountability. When an AI-generated alert influences an essential service, officials must be able to trace its source, understand how it was assessed and know who had the authority to act.\nThose safeguards must be designed before deployment. Clear ownership, access controls, escalation paths and human review are essential, as is continuous monitoring when data changes or model performance drifts.\nFIFA's experience makes this human layer visible. More than 350 engineers and operational specialists supported a tournament ecosystem that achieved 99.9% uptime across its technology solutions. That reliability was not simply a property of the technology. It depended on people continuously integrating, securing, monitoring and maintaining the systems behind it.\nThe same standard should apply to AI. Meaningful human oversight requires people with enough context to question a recommendation, override it when necessary, and explain the eventual decision. When several organisations contribute data or act through a shared system, audit trails and clearly defined hand-offs become even more important.\nFIFA's command centre helped people coordinate, but responsibility for the outcome remained with them. That distinction is critical in public infrastructure, where AI-supported decisions can affect safety, access to services and the daily movement of people.\nA city that can act as one\nSingapore and other cities across Asia Pacific have many of the building blocks: connected infrastructure, real-time data platforms, digital twins and increasingly ambitious AI strategies.\nThe next challenge is making those elements work together when it matters.\nWhile the FIFA World Cup does not offer cities a ready-made blueprint, it offers an operating principle and proven AI-led solutions: technology works at scale when infrastructure, data, AI and people are designed as a coherent whole. The measure of a smart city is not how many AI tools it deploys but whether its systems can work together reliably when multiple services come under pressure. That means keeping them resilient, observable and governable-and ensuring people can turn shared intelligence into coordinated action while remaining accountable for what happens next.\nAsia Sheikh, Global CTO, Sports & Entertainment, Lenovo","kind":"highlight","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":false,"editions":null,"column":"","sentiment":"0","news_tag":"","news_rank":0,"isVideo":false,"video":null,"symbols":[],"gpt_button":0,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"}}}