Local authorities around the world are recognizing that the key to unlocking value in cities lies not in isolated technology pilots but in the deliberate integration of data, artificial intelligence, and secure digital foundations. That is the central theme of a forthcoming virtual panel discussion scheduled as part of the 2026 global city summit series, where urban technology specialists and government leaders will examine how city workforces can move toward more strategic, risk-based infrastructure resilience.
The session, which will be streamed online and supplemented with on-demand material, comes amid a noticeable shift in how cities approach innovation. For several years, many municipalities have experimented with AI in areas such as traffic management, waste collection, and citizen services. Yet these efforts have often been fragmented, with individual departments purchasing their own tools, developing separate data pipelines, and failing to share insights across city agencies. The result has been a mix of proof-of-concepts that rarely produce the broad efficiency gains that smart-city proponents originally promised.
Bringing data, AI, and secure foundations together
The new discussion is expected to center on a more integrated model, one in which unified data platforms sit at the heart of urban digital transformation. Agentic AI, a term used to describe AI systems that can reason, plan, and take actions autonomously, will also feature prominently. These systems have become increasingly common in the private sector, where they help companies automate complex workflows. But cities are only beginning to understand how they can be used to support staff, rather than replace them.
For local governments, one of the key challenges is ensuring that AI tools are built on accurate, consistent, and well-governed data. A chatbot that answers questions about building permits, for example, is only as reliable as the records it receives. Similarly, a predictive model designed to identify roads that are at risk of deteriorating requires up-to-date condition data from multiple departments, as well as a clear understanding of budget constraints and maintenance schedules. Without secure digital foundations, those models are unlikely to be trusted by either city workers or the public.
The panelists are expected to argue that cities can achieve more by adopting a portfolio-based approach. Rather than funding hundreds of small pilots, they can focus on a handful of strategic platforms, built once and used across agencies. These platforms can then support a wide range of applications, from optimizing public transit to targeting energy efficiency improvements in municipal buildings. The most important element, however, is not the technology itself but the human and organizational capacity to make the most of it.
What Dublin can teach about pragmatic AI adoption
Ahead of the AI CityXchange workshop in Dublin, Alan Murphy, Regional Manager at Smart Dublin, has been discussing where AI can genuinely add value and where caution is required. Murphy, who has spent years working on urban digital projects in the Irish capital, emphasized that public institutions do not need to be the first to adopt every new AI development. Instead, they should focus on concrete problems, such as the time staff spend sitting through administrative tasks, or the difficulty of identifying underused city spaces. By tackling such issues with carefully scoped, data-rich use cases, city workers can build confidence in AI and gradually expand its use.
Murphy is one of many local government leaders who have called for a more measured approach to AI. While vendors are quick to highlight the possibilities of autonomous agents and machine learning, cities are accountable for the quality of the decisions made, the fairness of outcomes, and the privacy of citizens. This makes robust oversight a necessity, not an optional add-on.
The Dublin workshop is part of a wider peer-learning network in which city officials from Europe and beyond share lessons and jointly explore new technologies. The topic is particularly timely because the Irish government has made digital innovation a pillar of its national development strategy, and Dublin has already hosted experiments with smart lighting, autonomous drones, and data-informed public service delivery. The challenge now is to ensure that these experiments do not remain isolated.
Cayala: A private city drives integrated growth
Another perspective will come from Central America, where the city of Cayala is expanding in a context that differs dramatically from typical European cities. Juan Carlos Lopez, Chief Technology Officer and Head of the Value Management Office at Cayala, describes the project as an agile transformation laboratory. Cayala is one of Central America’s largest private cities, essentially a financially self-sustaining urban development that combines residential, retail, commercial, and cultural spaces. Lopez argues that because Cayala operates under a unified management structure, it can make decisions much faster than traditional public administrations. That allows the team to test digital infrastructure, shared data platforms, and community-led services in a real-world environment, while keeping a clear focus on return on investment and longer-term value.
Lopez’s involvement brings attention to a broader trend around agile transformation in cities. In an urban setting, agile is not merely a software development methodology. It means bringing together specialists from a wide range of backgrounds to tackle problems iteratively, with user feedback informing every stage. Digital infrastructure is essential to this, because it supplies both the connectivity and the data that make responsive services possible. But community-led services also matter. Cayala has learned that residents and businesses are more likely to embrace digital services when they participate in shaping them, and when those services make everyday tasks more convenient, such as booking facilities, paying utility fees, or reporting safety concerns.
Singapore and Sunderland show different paths to the same goal
The conversation around data and AI also draws on city profiles published in recent months. Singapore, for example, continues to build its reputation as one of the world’s leading smart nations. Known for its national digital identity system, integrated transport network, and widespread sensor infrastructure, Singapore now wants to move further by using AI to predict maintenance needs, plan urban development, and customize services for residents. The city-state’s commitment to data interoperability across government agencies has given it a considerable advantage over cities that are still working to connect legacy systems.
Sunderland, a city in the north-east of England, offers a different model. Once heavily dependent on traditional industries, Sunderland has repositioned itself as a smart city that combines digital infrastructure with low-carbon innovation. The local council has invested in high-speed connectivity, installed sensors in streetlights and utility networks, and supported projects that range from offshore-linked energy development to telemedicine centers. These investments are intended to build a resilient, future-focused economy and to address some of the inequalities that have persisted since the decline of the region’s industrial base. What Sunderland and Singapore share is the understanding that a city profile is only a snapshot. The real work lies in embedding data and AI into the daily routines of planning, finance, and operations.
Closing the ‘AI super gap’ between cities
Professor Jung Hoon Lee, who has emerged as one of the most cited scholars on smart-city indices and urban AI strategy, has repeatedly argued that AI is moving from pilots to real-world impact across cities worldwide. His research and interviews look beyond glitzy announcements to the practical ways that local governments are using AI to improve disaster response, allocate emergency services, and plan transportation networks. At the same time, he warns that a global “AI super gap” is beginning to appear. Some cities have invested heavily in data platforms, AI-ready infrastructure, and governance frameworks, while others still lack basic broadband access or fail to protect the personal data of citizens. That gap, if left unchecked, could deepen existing inequalities both within countries and between them.
Professor Lee stresses that building an AI-ready city is not just about buying expensive technologies. It requires a systematic approach to data stewardship, enabling safe and ethical reuse of data across public bodies. It also demands procurement processes that give small and medium-sized enterprises a fair chance to compete, rather than facilitating long-term dependency on a handful of global technology giants. Finally, governance structures must be flexible enough to keep up with emerging developments, while clear enough to reassure residents about how their data will be used.
Cybersecurity in the smallest urban devices
The urgency of cybersecurity in urban infrastructure will also be explored in the new materials, with a particular focus on smart lighting. Fabio Mauri, Head of Technology Operations and Cybersecurity at Paradox Engineering, explains that connected lighting systems are often treated as a low-risk grid asset. Yet light poles are now capable of hosting sensors, cameras, edge computers, and wireless transmitters. They sit at street level, often far away from secure data centers, and are therefore exposed to physical attacks, signal jamming, and network intrusions. Mauri believes cybersecurity must be designed into smart lighting from the start, rather than patched on after an incident occurs. His advice echoes a wider lesson for cities: security should be treated as a first-class consideration in every connected service, from building management systems to parking applications.
Transport and the responsible AI agenda
Transportation is one of the most promising areas for AI adoption, and Katherine Flesh, a technology strategist at Microsoft, argues that the greatest opportunities depend on strong data foundations, workforce readiness, and responsible governance. In her view, AI is already capable of helping transport agencies find better ways to schedule buses, anticipate failures in rail signaling, and guide drivers to available parking. But these capabilities will be wasted if agency staff do not trust the outputs, or if the underlying data is fragmented and messy. Flesh points to the need for dedicated data teams, clear policies for algorithm auditing, and partnerships with universities and broader communities to ensure that an AI-supported transport network serves everyone.
The broader conversation around AI in city operations is now turning to a question that is as much institutional as it is technical: how can a city make digital transformation stick? The answer, according to many of the experts gathered for the virtual discussion, is by combining the right infrastructure with workforce training, executive sponsorship, and realistic expectations. Some cities have appointed chief AI officers, created ethics boards, and introduced standard assessment frameworks. Others are creating innovation labs where staff can test tools in a sandbox with synthetic data, reducing the risk of exposing personal information.
These efforts are increasingly supported by a growing library of on-demand panel discussions and trend reports. One such session looks at “AI in city operations – from pilots to everyday practice,” while another focuses on operating smarter with digital twins and AI in urban infrastructure management. Cities that are slower to consolidate their data and establish governance frameworks may find themselves outpaced by peers that have already begun to move from experimentation to enterprise-wide deployment. For all the complexity of the technologies involved, the underlying message is reassuringly simple: becoming a smart city is less about adopting a single breakthrough tool, and more about creating the conditions under which data, AI, and secure infrastructure can be used safely and effectively for the benefit of every resident. From Dublin’s pragmatic workshops to Singapore’s national platforms, from private urban developers to post-industrial traders in Sunderland, the race is not for the smartest algorithm, but for the smartest system of values, capabilities, and trust.
Source:Smart Cities World News