Artificial Intelligence (AI) is no longer a distant pursuit; it is a foundational technology with significant implications for national development. Its transformative potential across sectors such as healthcare, agriculture, education, and public service delivery positions it as a powerful catalyst for socio-economic progress. For countries in the Global South, AI offers a unique opportunity to leapfrog traditional development constraints, provided adoption is strategic, inclusive, and systematic. Drawing on our extensive experience across nations, this playbook offers practical guidance for designing and implementing effective national AI strategies.
We propose a five-pillar framework for building national AI ecosystems:
- AI in Policy
- AI Competitiveness
- AI Enablers
- AI Infrastructure
- AI Safety and Governance: Responsible AI
The report focuses on readiness across these pillars, outlining design principles, implementation pathways, and global best practices.
- AI in Policy is the foundation. National strategies should be anchored in development priorities, with clear timelines, targets, and governance mechanisms. Case studies demonstrate the importance of aligning AI initiatives with industrial policy, sectoral priorities, and strong institutional leadership.
- AI Competitiveness covers both foundational technology and high-impact applications. Governments must choose whether to build indigenous AI capabilities or adapt global models, supported by public-private collaboration. Successful deployment requires prioritisation, sustainable institutional mechanisms, and ecosystem alignment. Examples from Egypt and India’s Telangana state illustrate how challenge-based pilots, funding, and institutional engagement can enable scale.
- AI Enablers, skilling and financing, are critical for long-term success. Effective skilling strategies span education levels and geographies, leveraging industry partnerships and employment-linked curricula, as seen in Vietnam’s AI Talent Development Programme. On financing, ecosystem gap assessments, targeted instruments such as funds-of-funds, and blended public-private capital models are essential, with Singapore and Vietnam offering useful templates.
- AI Infrastructure focuses on data and compute. Governments must choose among centralised, federated, or hybrid data architectures while enabling regulatory readiness and inter-agency data sharing. Compute infrastructure should democratise access and ensure financial sustainability. India’s hybrid data model and Telangana’s Data Exchange-cum-AI Sandbox (TGDeX) highlight inclusive infrastructure approaches.
- AI Safety and Governance: Responsible AI is a core pillar, encompassing ethical frameworks aligned with global norms, institutional governance structures, and safety tools. Countries must balance hard law, soft guidance, and co-regulation while aligning local needs with international standards. Initiatives in India and Indonesia show how safeguards can be operationalised.
To translate strategy into action, the report proposes a Preparate-Execute-Sustain framework. Preparation secures leadership sponsorship and dedicated AI units; execution emphasises agile delivery and partnerships; sustenance relies on feedback loops and transparency.
Ultimately, AI represents an opportunity for inclusive and sustainable development in the Global South, but only through coordinated system-building, robust infrastructure, skilled talent, and shared execution. This playbook offers a practical roadmap to turn national AI visions into scalable impact.