AI incident tabletop exercises in municipalities: how to plan them and what to measure
Practical guide to designing and running tabletop exercises for AI incidents in local authorities.
Articles, analysis and trends on artificial intelligence applied to the public sector
Practical guide to designing and running tabletop exercises for AI incidents in local authorities.
A practical guide to validate, govern, and monitor the quality of data used in AI within municipal entities.
A practical guide to applying differential privacy in municipal data: use cases, tools, integration into DPIA and regulatory requirements.
Practical checklist for evaluating AI vendors: legal compliance, technical evidence, and operational controls required when contracting.
How to apply AI to voting, vote counting and local electoral logistics with practical legal and operational controls.
Risks and practical controls for adapting AI models with administrative data in public entities.
Practical guide to automating the processing and evaluation of public contracts with AI while complying with ENS, GDPR and Law 9/2017.
Practical list of tests and criteria to accept AI models into production, aligned with ENS, GDPR and the AI Act.
Practical guide to integrating AI into the municipal budget cycle: prioritization, scenarios, risks and compliance requirements.
Practical guide to assess licenses, IP risks and contractual obligations when deploying open AI models in public entities.
Practical guide to creating an AI sandbox for municipal governments: infrastructure, data, compliance (ENS, GDPR, AI Act) and success criteria.
Practical guide to implementing hybrid evaluation panels for bids that comply with Law 9/2017, the GDPR, ENS and the EU AI Act.
Practical guide to designing auditable and compliant decision logs (ENS, GDPR, AI Act) in public AI systems.
Practical 30-day plan for municipalities to classify their AI systems and meet the AI Act's initial obligations.
How to leverage low-code/no-code platforms with AI in city governments securely, meeting ENS, GDPR and AI Act requirements, plus a project template.
How to design multilingual AI services for municipalities: technical options, risk assessment, data protection, and operational governance.
How to use AI to generate understandable summaries and texts while preserving legal validity, compliance and traceability in public administration.
A practical guide to classifying municipal AI systems and assigning proportionate operational and regulatory controls.
A practical guide to designing data-sharing agreements between public entities that comply with the GDPR and ENS and enable AI projects.
How to design citizen participation platforms with AI that are secure, transparent and resistant to manipulation for local governments.
Practical guide to using AI for prioritizing and planning municipal inspections with a focus on data, compliance and secure deployments.
Practical guide to designing and running adversarial tests (red teaming) in municipal AI projects with legal and operational requirements.
Practical guide to designing a modular AI training program for municipal teams, with modules, metrics, and a 90-day roadmap.
A practical guide to prioritizing interventions and meeting ENS, GDPR and public procurement requirements when implementing predictive maintenance.
Practical guide for creating an operational catalog of AI services in municipalities: structure, metadata, governance and prioritization steps.
How to roll out AI models and features in public services with progressive deployments, risk control and compliance.
How to apply AI to prevention, response, and recovery for local emergencies, with practical security and compliance requirements.
Practical guide to integrating AI with legacy systems: data models, APIs, ENS and GDPR compliance.
Practical guide to managing language model prompts in the public sector: logging, auditing, risk and compliance.
How to use AI to reduce barriers and improve SME participation in public procurement processes, while ensuring regulatory compliance.
Reduce incidents in grant reporting with tailored checklists generated from the regulatory bases, institutional templates and prompts to anticipate errors.
How to automate security and data protection checks across the AI model lifecycle in the public administration.
How to design useful explanations for citizens and officials: templates, patterns and operational steps.
Practical checklist to ensure sovereignty and security requirements in AI deployments for local government (ENS, GDPR, EU AI Act)
A practical guide for municipalities to share models, not data: technical, legal and operational requirements.
Seven common bottlenecks in grant management and how to diagnose them to simplify procedures, cut costs and speed up processing.
Practical guide to creating an AI Governance Committee in municipalities: roles, responsibilities, meeting cadence, and a 90-day checklist.
Step-by-step guide for municipalities with limited resources to begin safe, useful, and compliant AI projects.
Practical guide to cut energy use and emissions from AI projects in public-sector organizations.
How to reduce errors in grant applications to avoid correction requests, speed up processing, and cut administrative delays.
Practical guide to using synthetic data for AI testing and audits while complying with GDPR, ENS and the EU AI Act.
How to apply AI to identify and reduce fraud in municipal procurement while complying with Law 9/2017, the GDPR, ENS and the EU AI Act.
Practical risks and controls for using models that learn in production while complying with ENS, GDPR and the EU AI Act.
How an AI chat streamlines operational tasks in grants management teams: summaries, drafts, verification and responses to save time.
How to turn award criteria into auditable automatic scores while complying with Law 9/2017, the GDPR, the ENS and the EU AI Act.
How to design and deploy a hybrid AI+rules system to detect conflicts of interest in public procurement processes.
How to integrate AI solutions with legacy systems: patterns, concrete steps, and security requirements for municipal teams.
How to design kiosks and hybrid AI-assisted help while meeting GDPR, ENS and operational best practices.
Simplify grant processing before implementing AI: streamline processes, remove friction, and prepare the ground so technology can deliver real value.
Practical guide to ensuring ENS and GDPR compliance in modular AI SaaS platforms used by public entities.
Contractual and technical keys to avoid vendor lock-in in public AI projects: portability, escrow, testing and exit plans.
Practical methodology to identify and mitigate biases in administrative datasets before and during the deployment of AI systems.
How to design and implement effective human-in-the-loop (HITL) oversight for AI-assisted decisions in public entities, with concrete steps and metrics.
Hidden cost of processing grants: hours, errors and delays that consume internal resources and require measurement and optimization.
Practical guide to designing responsible training data flows: compliance, anonymization, traceability and operational controls.
Best practices to maintain continuity of citizen services during AI model updates in public entities.
Practical guide for city councils: how to respond to access, rectification and appeal requests regarding AI-supported decisions.
How to require and evaluate model cards and data sheets in tender documents: technical and legal requirements for municipalities.
How to turn inquiries, social platforms, and surveys into safe, compliant operational decisions.
How to document and communicate automated decisions: concrete templates and steps to comply with the GDPR, the EU AI Act and the ENS.
Practical guide to evaluate, deploy and maintain open-source LLMs in municipalities while meeting ENS, GDPR and data sovereignty requirements.
What to measure in municipal AI services: operational, quality, equity, compliance and cost KPIs, and how to implement them.
What to include in contracts with AI providers: ENS security, GDPR, EU AI Act, metrics, audit and exit plans.
Practical steps to prepare for, detect, and respond to incidents involving AI systems in municipalities, while complying with ENS, GDPR and the EU AI Act.
A practical guide to detecting model drift, setting metrics, governance and incident response for public AI models.
Practical guide to designing AI pilots in municipal governments: scope, legality, metrics and a 90-day scaling plan.
Practical checklist to ensure AI projects in government are accessible, inclusive, and compliant.
AI and GovTech trends that city councils should prioritize in 2026 and concrete steps to adopt them securely and in compliance.
How to translate AI ethical principles into concrete operational controls for municipalities and public entities.
Practical guide to using AI to simplify administrative processes: procedures, legal limits, and concrete steps for municipalities.
A practical guide to redesigning positions and processes with AI while complying with ENS, GDPR and public procurement rules.
Practical guide to designing citizen-centered AI public services: accessibility, transparency, and concrete metrics.
Practical guide to identifying, piloting and scaling automation of repetitive tasks in public organizations.
Practical guide to creating an AI business case for municipalities: metrics, regulatory compliance, and a cost/benefit template.
How to structure partnerships with the private sector to deploy public AI while meeting regulations and minimizing risk.
How to prepare and publish municipal open data for AI projects with security and compliance.
How to apply NLP techniques to review decrees, permits and grants with legal and security assurances.
How to decide between building, buying, or combining AI solutions in local government, with legal requirements and actionable steps.
How to adopt generative models in municipalities: use cases, operational controls, regulatory requirements and an implementation checklist.
How to assess whether your local authority is ready for AI projects and the concrete steps to take over 12 months.
Practical checklist to prepare municipalities and public entities for AI audits and transparency obligations.
How to design and implement predictive models to prioritize urban maintenance and social services using operational and governance criteria.
Practical framework for evaluating the return on investment of AI projects in municipalities and public entities.
Practical guide for municipalities to comply with ENS, GDPR and ensure sovereignty and security when deploying AI in the cloud.
Practical guide to deploying AI chatbots in municipalities: use cases, compliance (GDPR, ENS, EU AI Act), metrics and a deployment checklist.
How to integrate AI models with municipal legacy systems without disrupting services or compromising legal compliance.
A practical guide for municipalities on the obligations of the EU AI Act, system classification, risk management and concrete steps to achieve compliance.
How to implement OCR + NLP to automate registration, classification and routing of documents in municipal governments with legal and security guarantees.
Practical strategies for managing AI adoption in public administrations: pilots, training, compliance and impact measurement.
Practical guide to complying with the GDPR when deploying AI in public entities: DPIA, legal bases, contracts and technical measures.
Practical checklist to comply with the National Security Framework (RD 311/2022) when deploying AI in public administrations.
How artificial intelligence helps public administrations streamline tender preparation, verify documentation, and evaluate bids with greater rigor and transparency.
Before implementing artificial intelligence, public administrations need a clear governance framework. Key steps to move forward with legal certainty and organizational criteria.
Public grants management involves drafting award decrees, reviewing justifications, and document control. AI streamlines every phase without sacrificing rigor.
Municipal urban planning departments handle a growing volume of citizen queries. AI enables automatic urban planning reports from cadastral and regulatory data.