Name in original language
DIMO, Gobernando los Datos y la Inteligencia Artificial en los gobiernos locales. Gobernanza del dato previa a la IA
Initiative overview
Local governments increasingly rely on data to design and implement public policies, yet administrative information is often fragmented across organisational silos and managed under heterogeneous standards. This limits institutional capacity, increases legal risk and constrains the responsible use of advanced analytics and artificial intelligence. At the subnational level, these challenges are particularly acute for small and medium-sized municipalities with limited technical and organisational resources. Without clear data governance, the introduction of AI risks reinforcing inconsistencies rather than improving public decision-making. This context highlighted the need for a co-ordinated governance approach before expanding the use of artificial intelligence in public administration.
Results, outcomes and impacts
The initiative has strengthened data quality, interoperability and analytical capacity across local administrations. It has enabled more consistent and evidence-based public decisions, reduced legal and operational risks and improved co-ordination between institutions and municipalities. DIMO has also supported fairer policy design, increased transparency and contributed to higher levels of citizen trust. Beyond operational results, the initiative has fostered a cultural shift towards evidence-based governance within public administration.
Challenges and lessons learned
Key lessons from the initiative include the importance of establishing data governance before deploying artificial intelligence, and recognising that trustworthy AI is primarily an organisational challenge rather than a technical one. The initiative also demonstrated the value of shared governance models in reducing territorial inequalities and supporting smaller municipalities. Institutional trust increased when public servants clearly understood how data and analytical insights were generated. These lessons highlight that governing with AI requires long-term institutional commitment and continuous learning.





























