Generating value ultimately depends on the successful deployment of data and AI solutions in operational environments. Europe should therefore promote the conditions required to accelerate adoption across industries and public sectors, reducing the gap between research outcomes and practical implementation. Priority areas of this direction include:
- From experimentation to real-world deployment. Europe should prioritise AI and Data for real-world environments, where they can deliver transformative impact across industry and society. This implies accelerating the transition of data and AI solutions from pilots and demonstrators into operational environments. While substantial investments are currently being made in technology development and research, less attention is often given to the final stages of implementation. To generate real societal and economic value, more focus should be placed on deploying solutions in industrial environments, integrating AI and data services into operational processes, and ensuring adoption in real-world settings, such as factories or research infrastructures.
- Industrial and cross-sector applications, fostering deployment across sectors and value chains and enabling the transfer and reuse of successful data and AI solutions across domains
- End-to-end value creation, connecting data generation, management and sharing with AI development, services, operational processes and decision-making
- Adoption by SMEs and smaller actors, addressing their specific barriers to AI and data adoption and leveraging instruments such as BDVA i-Spaces to provide access to expertise, experimentation and validation. SME AI operational adoption and organisational readiness. This point should focus on the practical and organisational challenges that companies (especially SMEs) face when adopting AI in real business environments. Beyond skills and education, successful AI deployment also requires organisational readiness, leadership alignment, digital culture, operational integration, change management, and the ability to incorporate AI into existing processes and legacy systems.