Designing national policies for AI.

 

National competitiveness increasingly relies on science, technology and innovation(STI) and knowledge-intensive services. Developing countries therefore need to design strategies and industrial policies, taking into account the role of knowledge-intensive services and the uncertainties around research and development (R&D). They should also consider the diffusion, direction and impact of frontier technologies in the economy to adapt catch-up strategies accordingly. To date, most AI policies have come from developed countries. By the end of 2023, about two thirds of developed countries had a national AI strategy, while only six out of the 89 national AI strategies were from least developed countries (LDCs). AI policies implemented by major economies can have significant spillovers, influencing the policy options of other countries. Developing countries should quickly set and implement AI strategies that align with their national development goals and agendas. While it may be more immediately feasible to support AI adoption for particular sectoral needs, developing countries should also make long-term strategic plans to steer their own AI development; otherwise, as latecomers, they may be left with few options. This section focuses on a new wave of industrial policies for AI and frontier technologies to strengthen national capacities and drive inclusive, innovation-led growth. It highlights good practices and lessons learned, with an emphasis on infrastructure, data and skills.

Key policy takeaways

New industrial policies – Accelerated digitalization and the rise of AI call for new industrial policies. As value in the global economy shifts toward knowledge-intensive activities, decision makers need to support the adoption and development of new technologies, as well as the creation, dissemination and absorption of productive knowledge. 

Coordination – National strategies should coordinate across domains, including STI, industry, education, infrastructure and trade. Moreover, AI policies should go beyond incentives such as tax deductions and include regulations, such as on consumer protection, digital platforms and data protection, along with governance and enforcement to orient the direction of technological change. 

Policies should address the three leverage points: 

Infrastructure – It is vital to ensure equitable access to enablers such as electricity and the Internet that facilitate AI adoption and reduce inequalities. This can be achieved by fostering a conducive business environment with incentives for private-sector investment. Distributed networks and computing power can also enable AI development, but it is important to ensure interoperability and harmonization between infrastructures and systems. 

Data – Open data and data-sharing enhance data integration, storage, access and collaboration. AI adoption and development rely on good practices in data collection, with interoperability and accessibility across the innovation ecosystem. Privacy, accountability and intellectual property aspects should also be addressed, to foster innovation while safeguarding human rights. 

Skills – Population-wide AI literacy promotes widespread AI adoption and can be achieved by integrating science, technology, engineering and mathematics (STEM) and AI subjects, from early education to continuous learning. Partnerships between academia and the private sector can help build AI talent to meet particular industry needs and drive AI development.