Designing Services Around Citizens, Not Institutions 

This principle also shaped the city’s approach to employment services. 

Miguel Ángel Alemany, Deputy Director for Active Employment Policies, Employment Agency, Madrid City Council, presented SALTA, Madrid’s Employment Agency platform, which reorganizes services around the job seeker’s journey rather than the agency’s internal structure. 

AI helps automate administrative tasks, reduce repetitive data entry, and better match individuals with employment and training opportunities. However, decision-making remains subject to human oversight. 

The project’s broader significance lies in its design philosophy. 

Instead of asking citizens to navigate government structures, the service adapts to citizens’ needs. 

It is a subtle shift, but one that increasingly defines successful digital government. 

Building the Digital Foundations of the City 

Behind many of Madrid’s initiatives is a growing digital infrastructure that helps the government understand and manage the city in real time. 

Jesús Cerezo, Head of Process Integration Service, Deputy Directorate General for Urban Innovation and Information, highlighted Madrid’s expanding geospatial ecosystem, which has evolved from historic aerial photography archives into a sophisticated platform comprising approximately 1,000 datasets and serving thousands of users every day. 

The city’s Geoportal supports a wide range of use cases, from analyzing urban greenery to modelling underground infrastructure and supporting long-term planning decisions. 

At the same time, Javier Morales Puerta, Technical Adviser, Sueña Project, showcased how Madrid is reimagining urban planning itself. 

Traditional planning processes can take years to develop and approve. Madrid’s new approach introduces the concept of “4D urbanism,” using real-time data and predictive models to make planning more adaptive and responsive. 

A Strategic City Simulator, built using dozens of public data sources and indicators, enables planners to evaluate different scenarios and explore how policy decisions may affect the city over time. 

The result is a more dynamic approach to governing a rapidly changing urban environment. 

From Managing Traffic to Managing Mobility 

Transportation offered another glimpse into the future of public services. 

José Javier Rodríguez, Deputy Director for Traffic Regulation and Taxi Services, Madrid City Council, described Madrid’s efforts to use AI and computer vision to improve how traffic signals respond to real-world conditions. 

But the project begins with a change in perspective. 

The objective is no longer simply to manage vehicles. It is to manage mobility. 

Using video analytics, the city can detect pedestrians, understand movement patterns, and adapt crossings when conditions change. For example, traffic-light timing can be adjusted when large crowds leave a football stadium or when pedestrians require additional time to cross safely. 

Rodríguez was refreshingly realistic about the challenges involved. 

AI systems are imperfect. Performance varies across environments, lighting conditions, and contexts. In safety-critical situations, human oversight and fallback systems remain essential. 

That honesty reflected a recurring theme throughout the session: successful AI implementation requires discipline, governance, and a clear understanding of where technology adds value. 

The Agentic City Starts with Citizens 

Closing the session, Carlos Acha Ledesma, Director of Technology and Innovation of EMT Madrid, offered a broader perspective on the future of urban innovation. 

He described a progression from data collection to predictive analytics and ultimately to AI-powered operational decision-making across transport and mobility systems. 

Yet his strongest message was not about technology. 

It was about purpose. 

Digital transformation should create public value. It should improve accessibility, sustainability, equity, safety, and quality of life. 

In other words, innovation should be measured by outcomes, not by novelty. 

That philosophy aligns closely with Madrid’s vision of the “agentic city” — a city where technology helps institutions act more effectively on behalf of citizens. 

Key Takeaways 

• Start with the problem, not the technology – Across every project, Madrid focused first on solving real challenges such as housing delays, social isolation, employment access, and mobility. Technology was the tool, not the objective. 

• Clean data is the foundation of transformation – As Marta Parro Pérez demonstrated with the digital permits platform, meaningful automation requires structured data, clear processes, and strong information management before AI can deliver results. 

• Participation creates value when it drives action – María Pía Junquera’s Decide Madrid initiative showed that citizen engagement is most effective when it directly influences policies, investments, and decision-making. 

• AI works best when paired with human expertise – From Paloma’s loneliness programme to traffic management systems, Madrid’s projects consistently used AI to support human decision-making rather than replace it. 

• The ultimate test is the citizen experience – Whether improving housing delivery, helping older adults, supporting job seekers, or making streets safer, every initiative was evaluated against the same standard: does it make life better for residents?