Foreword

AI is expected to accelerate economic growth yet may widen inequality where access to compute, data and skills is limited. The AI Governance Alliance launched the AI Competitiveness through Regional Collaboration Initiative to promote equitable, responsible and secure intelligence-driven economies, offering guidance for countries at all AI maturity levels.

A blueprint for intelligent economies

The blueprint provides a strategic architecture for building resilient national and regional AI ecosystems. It emphasises coordinated development of infrastructure, data, responsible models and investment channels to support inclusive AI outcomes. It also identifies the need for cross-sector collaboration to drive innovation and minimise emerging risks.

Unpacking the blueprint

The framework comprises three layers—building foundations, growing intelligent economies and putting people at the heart. Nine strategic objectives guide the creation of AI infrastructure, inclusive datasets, responsible models and channels of investment. Each objective includes defined capabilities to support planning, implementation and collaboration.

Key stakeholders collaborating to deliver intelligent economies

Governments shape AI ecosystems through regulation, incentives, strategy and public-private partnerships. Enterprises lead technical innovation but must work with government and academia to support inclusive use cases. SMEs, while critical to economic growth, often lack early access to AI capabilities; in contrast, start-ups drive early adoption. Academia contributes R&D, ethics & skills; civic society provides accountability and community insight.

Blueprint layers and strategic objectives

The foundational layer focuses on energy, connectivity, compute, data governance and AI model responsibility. The second layer targets embedded intelligence, sectoral adoption and entrepreneurship ecosystems. The third layer prioritises skills, education and ethical guardrails. These layers collectively aim to support responsible, human-centred intelligent economies.

Spotlight on three strategic objectives

Build sustainable AI infrastructure

Key challenges include high energy demand, significant capital requirements, supply-chain vulnerabilities, limited connectivity and device affordability. Examples include Microsoft’s nuclear-powered data centre agreement and regional sharing of AI infrastructure.

Capabilities required include sustainable green energy, secure and diversified supply chains, expansion of high-speed connectivity for the 2.6 billion people currently offline, scalable compute (with the global AI infrastructure market expected to reach USD 223.45 billion by 2030 at a 30.4% CAGR), and access to affordable AI-ready devices. Recommended actions include incentives, PPPs, trade corridors, cloud partnerships, digital public infrastructure and device subsidy schemes.

Curate diverse, high-quality datasets

Capabilities include ensuring available data (e.g., Fugaku LLM using 60% Japan-origin data), building inclusive datasets (e.g., Aya’s 114-language dataset), strengthening ownership and residency requirements, improving privacy to address deepfakes and breaches, and adopting consistent governance standards. Related actions include disclosure rules, opt-in/opt-out consent options and cross-border data frameworks.

Establish guardrails for ethics, safety and security

Challenges involve addressing bias, adapting to evolving regulation, mitigating emerging risks and managing legal uncertainty. International initiatives such as UNESCO’s Ethics of AI and the OECD framework aim to support consensus.

Capabilities include culturally sensitive ethical guardrails, responsible use standards combining self-regulation and government oversight, safety mechanisms such as “red lines” in the EU AI Act, and development of regional and global IP frameworks to address ownership of AI-generated content. Actions include risk-based regulation, enhanced transparency, strengthened accountability mechanisms and international cooperation.

Conclusion

National and regional AI strategies should combine top-down direction with bottom-up engagement. Countries should tailor approaches to local needs, adapt proven models, strengthen access to devices and infrastructure, invest in skills and support SMEs. Future work will focus on regional implementation involving multistakeholder collaboration.