Summary of recommendations
We have world-class assets that need to be nurtured and developed. And with the right level of ambition and thoughtful implementation, we can change our society for the better. We structure our recommendations in five chapters. We start with skills, putting people first, and with a responsible way of sharing data. Technology should be at our service, not the other way around. The next three chapters focus on technology adoption, innovation and better public service. In conclusion, we set out a few implementation principles, such as the need for overall ambition.
Set up a new learning deal – Technology and AI are transforming society and our job market. We currently lack both the capacity and tools to support this transition and our schools are not preparing the next generations for the 21st century. This is the reason why we propose a new learning deal; a universal skills building program for adults and more digital – as well as human – skills for our youth.
Develop a responsible data strategy – Trust is the cornerstone of any transformation. We believe in the need for a robust and up-to-date legal framework, ethical principles and more transparency. Moreover, data is the energy that will fuel the fourth industrial revolution. But data often remains inaccessible. We need to build a data ecosystem that facilitates more responsible data-sharing with reinforced open data policies, more collaborations and a platform with well-structured tools and approaches.
Support private sector AI adoption – It can be hard for companies, particularly SMEs, to start working with AI. It can be perceived as complex; companies might lack the internal resources and the iterative approach can be too costly. Hence, we propose to demystify AI through a lighthouse approach (training programs, large-scale events and social-impact projects). Secondly, we believe in more collaboration and accessibility to AI through a national AI hub. Lastly, we need to facilitate experimentation.
Innovate and radiate – We have world-class researchers, but our research is not at scale. Also, we have yet to develop, attract and retain enough AI talent. Lastly, it is hard for innovative start-up companies to grow beyond the early stages. Hence, we propose to position Belgium as Europe’s AI lab through sandboxes and large-scale collaboration within academia, leveraging Belgian transposition of the GDPR. Next, we recommend creating more AI-related training programs, more focus on practical applications and more selective migration. Lastly, we suggest supporting the growth of our AI companies through an investment fund and by differentiating our expertise.
Improve public service and boost the ecosystem – Too few public organisations are currently experimenting with AI. Firstly, we propose that public institutions rethink their own roles and evolve towards a platform approach. Secondly, we need to give public institutions the tools to experiment; such as a rolling fund and more innovation-friendly procurement. Lastly, we recommend creating a Chief Digital Officer role to organise internal transformations and launch large-scale transversal projects. A few principles to ensure a sustainable implementation: ensuring continued trust from the public, a European approach, collaboration between all stakeholders, a grass-roots/community-led approach, focus on specific areas (such as healthcare/life sciences) and, lastly, daring to be ambitious and audacious. This will require an investment of at least EUR 1 billion by 2030.