Strengthening AI capabilities in civil society organisations

The use of artificial intelligence is becoming increasingly important in civil society. At the same time, many organisations face the challenge of building up the necessary skills. The position paper ‘From Know-How to Competence’ by the Civic Coding initiative was produced with the involvement of more than 20 civil society organisations and examines which AI and data skills are needed today, how existing support programmes are working, and what needs remain. The analysis is based on 22 guided interviews with representatives from sectors such as education, nature conservation, inclusion and welfare.

Thesis 1: Competence-building is fragmented

The interviews show that the development of AI competences has already begun in many organisations. Training courses, pilot projects, working groups and initial guidelines are widespread. At the same time, this competence-building often takes place in isolation – both within individual organisations and across sectors. Experiences and insights are linked only to a limited extent. Furthermore, not all organisations have sufficient human or financial resources to establish their own learning processes. Alongside these isolated approaches, however, there are also formats such as learning groups, working groups or open workshops that focus on collaborative learning.

Thesis 2: Experience is rarely shared

According to the study, particularly relevant learning experiences arise in connection with specific practical issues, such as the introduction of AI tools or dealing with data protection requirements. However, this knowledge often remains tied to individual people and is rarely documented or made accessible across organisations. As a result, similar issues are frequently tackled in parallel. Formats that make experiences and learning processes visible are as yet not very widespread.

Thesis 3: Large organizations can pool knowledge

Larger organisations are more likely to have the resources for pilot projects, specialised teams and external expertise. This enables them to test new applications and gain experience. The paper sees the potential for them to act as centres of knowledge and expertise. However, this role has so far been neither systematically defined nor coordinated. According to the analysis, shared themes and challenges are crucial for successful collaboration.

Thesis 4: Strategic AI competence remains underdeveloped

Whilst technical foundations and initial application skills are increasingly being developed, the strategic engagement with AI often remains in the background. Responsibilities are frequently unclear, long-term visions are lacking, and pilot projects are rarely translated into lasting change processes. The interviews show that issues relating to work processes, organisational development and leadership must be given greater consideration. Strategic expertise arises particularly where managers are actively involved and AI is understood as part of organisational transformation.

Thesis 5: Shared structures promote learning

Many organisations are grappling with similar issues, such as selecting suitable tools, meeting legal requirements or developing meaningful use cases. Nevertheless, these topics are predominantly addressed in isolation from one another. The paper therefore highlights the importance of shared structures for the exchange of experience. It mentions ongoing exchange formats, central knowledge hubs, modular learning programmes and cross-organisational networks. Existing examples already demonstrate possible approaches, but these are currently only available in isolated instances.

Based on the findings, the authors conclude that there is a need to promote collaborative learning and sustainable competence development more strongly. In this context, AI competence is not understood solely as a technical skill, but also encompasses strategic, organisational and ethical aspects.

About Civic Coding

The ‘Civic Coding – AI Innovation Network for the Common Good’ initiative aims to shape AI in a socially responsible, sustainable and participatory manner, thereby strengthening data and AI competences within civil society. It is supported by three federal ministries: the Federal Ministry of Labour and Social Affairs (BMAS), the Federal Ministry of Education, Family Affairs, Senior Citizens, Women and Youth (BMBFSFJ) and the Federal Ministry for the Environment, Climate Protection, Nature Conservation and Nuclear Safety (BMUKN).