Focus Areas
To ensure broad and targeted upskilling across society, the AI Competence Pact has defined nine focus areas. Each area targets specific actors, functions, and needs – and aims to promote collaboration, knowledge development, and concrete actions.
The initiatives include:
- Development of guides, learning materials, and recommendations
- Testing and scaling of pilot projects
- Knowledge sharing through case studies and networks
- Studies of barriers, potentials, and competence needs
AI Competencies in Public Sector Denmark
Examines which structural frameworks promote or hinder the development of AI competencies in the public sector. The focus is on responsibility, trust, and professionalism.
Future AI Competencies
Promotes knowledge about which AI competencies will be central in the future education system and job market – from primary school to higher education and lifelong learning.
AI Competencies and Competitiveness
Focuses on how leadership, incentives, and organizational maturity affect companies’ ability to translate AI competencies into real competitive advantage.
AI Competencies in
Citizen and Customer Interaction
Shares knowledge about the competencies necessary to use AI with empathy, responsibility, and judgment in interactions with citizens and customers.
AI Competencies in Operations and Production
Stimulates learning about AI in technical and production-related functions and develops perspectives on how professional groups in operations and production can gain a fundamental understanding of AI.
AI Competencies in Administrative Functions
Examines competence needs among administrative staff and promotes reflection on ethics, automation, and digital responsibility in everyday functions.
AI Leadership Competencies
Develops perspectives on the competencies leaders need when facing AI-driven changes – focusing on strategy, ethics, digital imagination, and organizational readiness.
Methods and Academy
Promotes insight into how AI competencies are best developed and anchored – through knowledge sharing about learning forms, quality, and effect in upskilling.
Diversity in AI
Shares knowledge about how AI upskilling can promote inclusion and responsibility – and examines which competencies are required to counteract bias and strengthen diversity.