Public-private collaboration for AI projects: a practical guide for municipalities

Why consider public-private partnerships for AI

Municipalities often need technical capabilities and data that aren’t always available in-house. Working with companies can speed up deployments, provide access to expertise, and share financial risk. But without a clear framework problems can arise: GDPR breaches, security failures under the ENS (Royal Decree 311/2022), contractual risks or technological dependency.

This guide offers a practical —legal and operational— approach to designing AI partnerships that comply with Law 9/2017 on Public Sector Contracts, the GDPR, the ENS and the emerging obligations under the EU AI Act.

Legal and compliance framework: essential checklist

• Public procurement: use procedures compatible with innovation (e.g. competitive dialogue or innovation procedures contemplated by Law 9/2017) and define award criteria that value interoperability, knowledge transfer and data control.
• Data protection: document legal bases (Articles 6 and 9 of the GDPR as applicable), perform a data protection impact assessment (DPIA) when processing involves risks to rights and freedoms, and ensure data processing/processing-by-contract clauses with subcontractors.
• Security: require compliance with the ENS (Royal Decree 311/2022) for services and throughout the supply chain; include minimum cybersecurity and audit requirements.
• AI regulation: determine whether the system is “high-risk” under the EU AI Act; if so, include specific safeguards (conformity assessments, technical documentation, data logging).
• Transparency and accessibility: include transparency obligations toward citizens (explainability, complaint channels), especially when decisions affect administrative rights.

Contract clauses that really matter

• Data rights: clearly define who is the controller and who is the processor; permissions for use, anonymization, reuse and derived data. Avoid broad transfers of personal or aggregated data without time limits.
• Intellectual property and models: distinguish between the provider’s pre-existing tools and deliverables developed ad hoc. Include usage licenses that allow operational continuity if the provider changes.
• Portability and interoperability: require open formats and documented APIs; clauses for exporting data and code under specified conditions and timeframes.
• SLAs and performance metrics: measure accuracy, fairness (bias), response times and availability; define penalties and improvement plans.
• Audit and trace rights: permit independent technical audits and access to logs to verify behavior and compliance.
• Exit and continuity plan: protocols to migrate services, preserve data and keep the administration operational in case of termination or breach.

Operational model: pilot, validation and scaling (stage-gate)

1. Scoped PoC (3 months): synthetic or minimized data, clear success criteria and objective metrics.
2. Controlled pilot (6–12 months): real users in a limited environment, full DPIA if personal data are involved, monitoring of performance and bias.
3. Independent evaluation: technical and legal audit before scaling; review by an internal committee (Legal Services, IT, affected department).
4. Phased rollout: stages by service or territory, with post-implementation review and continuous improvement clauses.

Internal governance and roles

• Decision committee: representatives from Legal Services, IT, the user department, transparency and internal control.
• Data Protection Officer (DPO): oversight of GDPR compliance.
• Security Officer: ENS compliance checks and incident management.
• Technical review team: validates integrity, explains models and coordinates audits.
• User coordinator: collects operational feedback and manages citizen channels.

Risks and practical mitigations

• Vendor lock-in: mitigate with open licenses, interoperability and transfer clauses.
• Bias and discrimination: continuous validations with representative data; include fairness metrics in SLAs.
• Security incidents: response plan, backups and recovery tests; require rapid notification by contract.
• Lack of transparency: contractual obligations for technical documentation and user manuals aimed at the administration.

Recommended KPIs to evaluate the partnership

• Mean time to resolve operational incidents.
• Accuracy and error rate by use case; fairness metrics by cohorts.
• SLA compliance percentage per month.
• Number of findings in security and compliance audits.
• Level of reuse/portability of data and artifacts.

Immediate actions for municipalities (checklist)

• Define a priority use case and a measurable impact objective.
• Inventory data and perform a preliminary DPIA.
• Choose a procurement process that favors innovation and knowledge transfer.
• Include clauses in the tender about ENS, GDPR, auditing and portability.
• Plan a PoC with exit criteria and independent evaluation.

Conclusion: actionable takeaway

For a public-private partnership to add value without increasing risk, make compliance and knowledge transfer design requirements: define data, audit and portability clauses from the start; manage adoption in stages with independent validation; and maintain multidisciplinary governance. This approach lets municipalities leverage external AI capabilities, retain operational control and protect citizens’ rights.

If you need practical support to design tender documents and technical clauses adapted to your municipality, sector platforms and solutions like OptimGov can be integrated into procurement and governance processes to facilitate secure, compliant deployment.