clear governance model (e.g. data) with stakeholders involved.
Alignment with relevant policies and regulations.
across entities, as well as with private sector partners, academia, and others.
- Efficient collection, storage, organisation, integration and use of data (e.g.,quality, privacy & security, accessibility, interoperability) - Data gaps management (e.g., external third parties, proxies, synthetic data development).
Modern infrastructure and tools required to ingest and process data (e.g., cloud computing coupled with robust cybersecurity).
Framework to track projects and refine them as needed.
- Strong leadership with top- down sponsorship to drive adoption and maturity. - Clear vision and strategy, aligned with the broader government’s strategy, coupled with an action plan (e.g., real-automation roadmap) that includes goals and milestones dependent on the budget. - Change management strategy (e.g., support to reskill and upskill employees).
- Cross-entity alignment and collaboration (e.g., data sharing culture) - Culture that believes in failing fast, putting fear aside and experimenting.
- Task force to drive the strategy and roadmap under leadership - Specific roles, responsibilities and skills development at different levels (e..g, trainings, pod team per use case combining functional/ industry and technical competencies).
Governments around the world are taking action to increase the skills of their public workforce
Institute for Public Management and Economic Development (IGPDE), offers training courses (E.g., Artificial intelligence, data science: New economic challenges) to equip public servants with basic knowledge about AI and its opportunities and challenges.
AI workshops open to public officers and, in particular, middle and senior managers, to increase digital literacy and provide foundational knowledge about the potential of AI for public work and public organisations.