AI for municipalities
Municipalities are under increasing pressure. Society is changing rapidly: residents expect transparent decision-making, the demand for care is rising, and issues such as sustainability and housing require smart choices.
The challenge for municipalities
We see daily that many municipalities struggle with:
- Increasing financial pressures (leading up to canyon year 2026)
- Budget cuts and need for greater efficiency
- Outdated IT systems and fragmented data.
- Spatial challenges: housing shortage, vacancy, redevelopment
- Pressure on social domain and healthcare budgets
- Participation issues: involving residents better in policies
At Data Science Lab, we help municipalities do more with fewer resources without losing the human touch.
How we help municipalities
We help municipalities deploy data and AI responsibly, explainably and practically. Not as an experiment, but as a proven tool for social impact.
Together with policy makers, information managers and domain specialists, among others, we map:
- Where data now goes unused
- Where AI can help save capacity
- And how to innovate responsibly within legal frameworks (AI Act, AVG, algorithm registry)
In this way, we help municipalities make faster decisions, better policies and understand what works.
Our approach
We work on the principle: start small, learn fast and scale up sustainably.... We often start with a workshop to map the current and desired situation. Read more about the workshop and examples of cases from municipalities below.
Collaboration is key
We work with all the different organizational units (OOs), each with their own dynamics and challenges, including:
- Space: predictive models for housing construction and flow
- Social domain: AI signals for early care intervention
- Permits & enforcement: smart document analysis and pattern recognition
- Energy & infrastructure: data-driven planning for energy capacity
- Governance & communication: understanding societal trends and citizen participation
This is how we make data and AI tangible within the daily practice of municipalities.
Sample cases
We help municipalities concretely apply data and AI to their daily challenges. And that delivers tangible results:
1. Decision making in objection cases
Municipalities use our AI models to recognize patterns in previous rulings. This allows similar cases to be handled faster and more consistently, on average we see a 30% faster turnaround time and less administrative pressure on legal teams.
2. Energy planning
Using geographic data and predictive models, we are helping municipalities better plan their energy capacity by district.
This prevents bottlenecks and enables better alignment of investments by municipality, developers and grid operators, good for a 20% lower chance of exceeding capacity.
3. Licensing
We support municipalities with smart document analysis that automatically recognizes relevant information in applications.
This saves a lot of manual work and ensures faster, better substantiated decisions. In pilots, this led to 40% less administrative burden and higher satisfaction among both employees and applicants.
4. Care & social domain
In collaboration with municipal care and policy teams, we are deploying predictive models to identify risks early.
This allows interventions to take place earlier, leading to better services, lower costs and more direction for residents.
Why municipalities choose DSL?
- 9 years of experience with data & AI within public and semi-public organizations
- Combination of technical expertise and social responsibility
- Strong focus on Responsible AI: explainable, ethical and applicable
- We speak the public language and translate complex data into managerial insights.
Cases AI in healthcare
View some of our healthcare case studies here:
- Instant access to contract information and policy conditions at the pharmacy counter by our virtual assistant
- Improved flow in the emergency room through our AI solution that predicts whether a patient needs to be admitted
- Safe and efficient implementation from Machine Learning models at the care
- Influence from classic risk factors at cardio metabolic diseases investigated
- Understanding data maturity as a foundation for future-proof healthcare innovation
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