The Fraunhofer Institute for Industrial Engineering IAO has analyzed on behalf of the Friedrich Naumann Foundation how Artificial Intelligence (AI) can efficiently, sustainably, and citizen-oriented solve urban challenges. The new publication "City and AI" shows:
How artificial intelligence is already creating tangible added value for cities today
Which organizational stumbling blocks slow down the implementation
Why the topic should now be on the municipal agenda
Practical examples show: AI supports almost all urban areas.
Whether it's intelligent traffic control, energy-efficient buildings, or automated administrative processes - Fraunhofer IAO has examined over 30 real use cases. The study shows: AI can sensibly complement almost every urban domain.
"However, technology alone is not enough. Furthermore, not everything that is technically possible is also meaningful," emphasizes study leader Patrick Ruess from Fraunhofer IAO. "Only in conjunction with data governance, clear process integration, and citizen participation does AI unfold its full potential."
A central example is the so-called Digital Twin. This can virtually replicate urban spaces and link them with real-time data from IoT devices as well as sensors in buildings and neighborhoods. This digital representation, in combination with AI, enables precise analysis, monitoring, and control of operations. Thus, a Digital Twin can efficiently support neighborhood management and at the same time serve as a data basis for planning and investment decisions of the city administration or private actors.
Organization beats technology: Interactive workshops deliver key insights.
A accompanying workshop with experts from administration, urban development, and smart city projects showed: The biggest hurdles in the use of AI are of an organizational nature:
Missing data strategies
Unclear responsibilities
Limited budgets
Even though technically simple solutions such as chatbots or smart waste systems can be implemented, they often lack relevance for structural challenges. More complex, data-intensive applications - such as in building management or emergency assistance - are considered particularly effective, but are more difficult to realize.
Recommendations: Strategic Implementation of AI in the City
The publication provides clear recommendations:
Establishment of municipal data platforms
Formation of interdisciplinary teams from specialist and IT areas.
Early citizen participation to ensure acceptance
Targeted use of AI where concrete improvements are possible
"Cities affect citizens in all areas of life - from registration offices to mobility. AI should therefore be used specifically and appropriately where it provides real benefits. For example, rule-based AI approaches are sufficient to support many repetitive administrative tasks, without the need for more complex machine learning methods," says Patrick Ruess.
Looking ahead: Areas of action for municipalities
Key areas for the sustainable implementation of AI for cities:
Alignment with municipal goals such as CO2 reduction, improved traffic flow, efficient administrative services.
Development of a governance framework for data protection, ethics, and financing.
Utilizing synergies for social and ecological urban development