Earlier this year at the United Nations AI for Developing Countries Forum (AIFOD) in Vienna, I spoke on a theme that is shaping the global development agenda — how emerging economies can build an equitable AI future. The forum brought together policymakers, financiers, technologists and innovators, united by a shared belief that AI must deliver inclusive and sustainable value.
I delivered a keynote on AI for the Global South. I later moderated a panel on blended finance mechanisms, exploring how financial innovation can unlock investment in AI research, infrastructure and local talent. These were not abstract discussions — they were practical conversations about how to translate vision into scalable action.
Across the forum, one idea gained strong momentum: finance and fairness must advance together. The Global South’s AI journey will depend on two interdependent levers — blended finance and the FAIR standards framework (Future AI Investment Responsibility). When combined, they create a pragmatic pathway for responsible innovation and long-term scale.
One of the persistent challenges in the Global South is access to capital for AI innovation. Investors often perceive early-stage projects as too risky due to data scarcity, limited infrastructure and regulatory uncertainty. As a result, many promising initiatives struggle to secure funding at the point when they need it most.
Blended finance offers a practical solution. By combining public, philanthropic and private capital, this approach helps distribute risk and attract investment into frontier markets. Concessional support from development agencies allows early-stage AI initiatives to refine their models, develop local talent and strengthen data ecosystems.
At AIFOD, the discussion highlighted examples where blended finance is already creating impact — from AI-enabled agricultural forecasting and climate-resilience tools to digital health diagnostics and education access. In each case, concessional funding served as a bridge between innovation and scale.
Blended finance is not a short-term subsidy; it is a structural enabler of sustainable ecosystems. It signals confidence, attracts private capital and aligns commercial incentives with social imperatives. In short, it turns developmental vision into an investment opportunity.
Capital alone does not create sustainable innovation. A framework of accountability, transparency and inclusivity must guide it. This is where FAIR standards come in — a structured, measurable approach to responsible AI deployment.
At AIFOD, I proposed that FAIR should function as a market passport — a clear set of criteria that defines what “responsible AI” looks like in practice. FAIR benchmarks include safety, explainability, inclusivity and sustainability. Meeting these benchmarks builds trust with investors, regulators and end-users alike.
FAIR also encourages global investors to respect the local context. Fairness is not universal; it must adapt to cultural and economic realities. For AI to be inclusive, it must reflect the languages, values and lived experiences of the communities it serves. That requires local voices in governance — not as consultees, but as co-architects.
Without this cultural grounding, even the most advanced AI risks reinforcing inequality or dependency. With it, AI becomes a force for empowerment. FAIR ensures that innovation respects sovereignty while remaining globally interoperable.
Another central takeaway from the UN dialogues — including the Global Dialogue on AI Governance and the UN Independent International Scientific Panel on AI — is the importance of local stewardship. Communities across the Global South must be active participants in defining the purpose, boundaries and benefits of AI.
AI should align with national priorities such as food security, healthcare, education and climate adaptation. Models must also uphold data sovereignty — ensuring that local data is governed responsibly and that value creation remains equitable. Extractive data models risk repeating the economic patterns of the past, where the benefits of innovation flow outward rather than inward.
Strong governance and community participation ensure that data becomes a developmental asset, not a traded commodity. This balance between openness and sovereignty is essential for trust — and for the long-term legitimacy of AI ecosystems in emerging markets.
When countries embed these principles into financing and standard-setting, they move from dependency to partnership. The Global South then becomes a co-author of the AI narrative, shaping technology in alignment with its own values and development goals.
Every discussion on AI governance eventually returns to one word — transparency. Without it, good intentions stall. With it, collaboration and capital flow more freely.
Investors, regulators and citizens alike must have visibility into how AI systems are designed, funded and evaluated. Transparent reporting frameworks — built on open data, auditable metrics and participatory review — help reduce perceived risk and strengthen confidence in responsible innovation.
Transparency is not bureaucracy; it is infrastructure for trust. It assures that AI solutions are built ethically and deliver measurable outcomes. It gives investors clarity, policymakers accountability and communities confidence that technology serves their interests.
Trust, ultimately, is the true currency of the AI economy. It determines whether innovation can scale sustainably and whether public and private sectors can work together to accelerate meaningful progress.
Reflecting on these UN engagements, one insight stands out: finance unlocks innovation, but fairness sustains it. Both are indispensable to building inclusive AI ecosystems across the Global South.
Blended finance offers the capital and confidence to get ideas off the ground. FAIR standards provide the guardrails that keep innovation ethical and transparent. Together, they offer a blueprint for growth that is responsible, scalable and human-centred.
As AI governance continues to evolve globally, the goal should not simply be regulation — it should be re-imagining development through AI. For developing countries, this is a generational opportunity to define their digital futures on their own terms.
By combining investment with integrity and capital with conscience, the Global South can transform AI from a symbol of inequality into a driver of shared prosperity. The next chapter of AI will not be written solely in the labs, but in the communities, classrooms and enterprises of the South — where innovation meets inclusion, and finance meets fairness.
The views and opinions expressed in this publication are entirely those of the author and do not necessarily represent the views of any company, board, or institution with which the author is associated.
