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# Competences and governance practices for artificial intelligence in the public sector
- URL: https://www.aigl.blog/competences-and-governance-practices-for-artificial-intelligence-in-the-public-sector/
- Published: 2025-04-03T18:05:56.000Z
- Updated: 2026-09-12T20:05:21.000Z
- Description: his JRC report outlines the competences and governance practices public organizations need to adopt AI effectively.
- Author: Jakub Szarmach
- Tags: Guide, Policy, EU AI Act, #aigl-library

[Competences and governance practices for artificial intelligence in the public sectorCompetences and governance practices for artificial intelligence in the public sector.pdf2 MBdownload-circle](https://www.aigl.blog/content/files/2025/04/Competences-and-governance-practices-for-artificial-intelligence-in-the-public-sector.pdf "Download")

## **What’s Covered?**

This *Science for Policy* report by the Joint Research Centre focuses on how public administrations across the EU can build the right **skills** and **governance practices** to adopt and manage AI in a way that generates public value. The authors draw on 48 policy and academic sources, an expert workshop with 40 participants, and case studies from seven European public bodies (e.g. Amsterdam, Trondheim, Czech Interior Ministry). Their goal is to answer two key questions:

- What *individual competences* do public managers need?
- What *governance practices* should public organizations adopt?

The **competence framework** has three main domains:

- **Technical competences** (25): data science, AI development, evaluation, etc.
- **Managerial competences** (16): project management, stakeholder engagement, etc.
- **Policy, legal, and ethical competences** (15): impact assessment, rights protections, etc.

These are crossed with three competence “clusters”:

- **Attitudinal** (know-why): values, motives, ethics, openness.
- **Operational** (know-how): practical skills like testing or auditing AI.
- **Literacy** (know-what): understanding AI concepts, limitations, and risks.

The **governance framework** includes 34 organizational practices, grouped into:

- **Procedural practices** (14): guidelines, review protocols, audits.
- **Structural practices** (12): committees, task forces, coordination bodies.
- **Relational practices** (8): partnerships, stakeholder dialogue, collaboration.

These practices are applied at:

- **Strategic level** (11): alignment with missions, leadership, resourcing.
- **Tactical level** (13): implementation and change management.
- **Operational level** (10): daily oversight and frontline execution.

The report also delivers **6 key recommendations**, broken into **18 actions**. These address:

1. Identifying and developing critical competences.
2. Tailoring governance structures to AI maturity levels.
3. Promoting learning-by-doing via pilot projects.
4. Creating shared repositories of best practices.
5. Building internal and cross-sector partnerships.
6. Supporting sustained knowledge exchange through EU-wide initiatives.

Case studies provide real-world examples of AI projects (e.g. chatbots, predictive analytics) and their institutional, legal, and ethical hurdles. These illustrate both the promise of AI for public value creation and the risks of poorly managed implementation—bias, opacity, or mission misalignment.

## 💡 **Why it matters?**

If AI is to benefit public services without undermining trust or rights, public institutions need more than legal compliance—they need the skills to design, test, evaluate, and govern AI effectively. This report translates that ambition into actionable steps grounded in real European contexts. It bridges policy, competence building, and organizational reform.

## **What’s Missing?**

While the report is practical and grounded in public sector realities, it leans heavily on **internal readiness** and **institutional alignment**. Some blind spots include:

- **Citizen co-governance or participatory mechanisms** are barely mentioned. Public trust is treated more as an outcome than a dynamic input.
- **Evaluation metrics** for effectiveness, fairness, or social value of AI systems are not deeply addressed.
- The report could better integrate the **AI Act’s risk-tier framework** into practical governance actions—particularly for distinguishing high-risk use cases.
- It underplays **procurement competences** or the governance of outsourced/third-party systems, which are critical in many public sector deployments.
- The **role of unions, civil society, or whistleblowing channels** in AI governance is largely absent.

## **Best For:**

Policy leads, digital transformation officers, HR managers, and chief data/AI officers in public organizations. Also highly useful for EU institutions or national digital agencies seeking to align AI governance with staff capabilities and operational structures.

## **Source Details:**

**Title**: *Competences and Governance Practices for Artificial Intelligence in the Public Sector*

**Authors**:

- **Rony Medaglia** – Professor at Copenhagen Business School; expert in digital government and public sector transformation.
- **Patrick Mikalef** – Professor at NTNU; researcher on AI capabilities, governance, and digital innovation.
- **Luca Tangi** – Policy analyst at the European Commission’s Joint Research Centre; focuses on digital public governance and public sector innovation.

**Institution**: European Commission, Joint Research Centre (JRC)

**Publication**: EUR 40032, 2024

**Part of**: Public Sector Tech Watch, supporting EU digital transformation initiatives under the AI Act and Interoperable Europe Act.