Africa & Latin America To Lead in AI?

Africa and Latin America (LatAm) are at an inflection point for artificial intelligence. Both regions remain “early-stage” compared with North America, Europe and China in absolute funding, infrastructure and research output, yet they possess the ingredients to leapfrog like large mobile-first populations, pressing development challenges where AI can deliver outsized impact (health, agriculture, finance), growing startup pipelines, and accelerating government interest.

Strategic investments in talent, data infrastructure and locally relevant models could unlock rapid, inclusive AI adoption across industries and public services. Major multilateral reports and market trackers identify both regions as priority growth frontiers for AI-driven development.

The Landscape Today

Funding & startups

• Africa: Investment slowed after the 2022–24 global VC correction, but activity is recovering with AI and data-oriented startups increasingly visible in funding rounds. African-focused ecosystem reports document renewed funding and a rebound in 2025, underscoring a maturing pipeline of early-stage companies. Local venture funds, regional accelerators, and international corporate partners are beginning to back AI-native plays in fintech, logistics, health and agritech.
• Latin America: A broader, deeper VC market than Africa, LatAm shows steady early-stage deployment with a rising share of AI-native startups. Regional investors and international players are funding AI-driven firms across Brazil, Mexico, Colombia and Argentina; institutional interest is growing in talent hubs beyond São Paulo and Mexico City. LAVCA and ecosystem reports indicate a healthy pipeline of seed and Series A rounds that can feed AI growth.

Policy and public programs

• Brazil & LatAm: Brazil’s national AI plan (2024–2028) is a high-profile example of country-level commitment, with multi-billion-reais allocations aimed at infrastructure, training, and regulation. Such national plans in LatAm signal that governments see AI as strategic for economic development and sovereignty.
• Africa: Several African governments and regional organizations are advancing digital strategies, though plans vary widely by country. International development institutions and multilaterals (e.g., World Bank Group) emphasize AI as a tool to accelerate progress across food security, health, and public services when deployed responsibly.

Talent & research

Both regions face brain-drain pressures (talent moving to U.S./Europe) but are also producing high-quality engineers, data scientists and applied researchers. Growing university programs, bootcamps and industry partnerships are expanding the talent base; hubs such as Nairobi, Lagos, Cape Town, São Paulo and Mexico City concentrate much of the activity.

The increasing availability of open-source models and cloud credits lowers the barrier for early-stage experimentation.

Why These Regions Can Leap Ahead

Financial services & fintech

High unbanked/underbanked populations in both regions create demand for AI-driven credit scoring alternatives, fraud detection, and personalized micro-insurance. AI models that leverage transactional and mobile data can rapidly expand financial inclusion.

Agriculture & food systems

Smallholder farms dominate in many countries. Satellite imagery, cheap IoT sensors, and AI-driven advisory apps can boost yields, optimize inputs and shorten response times during climate shocks — delivering both productivity and climate resilience.

Health & diagnostics

AI can extend scarce clinical capacity through triage chatbots, radiology-assisted diagnostics and predictive outbreak models. When paired with strong data governance and clinical validation, these tools can improve outcomes in low-resource settings.

Logistics & urban services

AI optimizes first-/last-mile logistics in congested cities and informal transport networks, improving delivery, reducing emissions and supporting e-commerce growth.

Language & local content

Regional languages (Portuguese, Spanish and hundreds of African languages) require tailored NLP models, a major opportunity for locally trained models that outperform generic global models on context and cultural nuance.

Each of these sectors benefits from mobile penetration, local data sources and concrete ROI pathways, making them ideal targets for early AI adoption.

Key Challenges to Address

• Infrastructure & compute: Data centers, reliable broadband and affordable GPUs remain limited, raising costs for training and deploying large models.
• Data availability & quality: Fragmented records, limited digitization, and privacy/legal uncertainties constrain high-quality labeled datasets for supervised learning.
• Funding depth & follow-on capital: Seed rounds are growing, but there’s still a shortage of later-stage capital to scale AI companies regionally or globally.
• Talent retention & skills mismatch: Competitive salaries abroad and in big tech can drain startups of senior ML talent.
• Regulation & data governance: Emerging rules (e.g., Brazil’s dataprotection actions) create uncertainty but also an opening to build trustworthy systems aligned with local norms.

Case Examples

• Brazil’s industrial push: The country’s multi-billion-reais plan explicitly budgets for AI infrastructure and training, which is a rare, material state investment signaling political will to build domestic capacity. This can catalyze both startups and public-sector AI pilots.
• Ecosystem rebounds: Regional trackers show renewed funding flows in 2025 for African startups and a rising number of AI-native companies in LatAm, indicating market confidence and growing deal activity.

Practical recommendations

For investors:

• Prioritize sector-specific AI plays (fintech, health, agritech) with clear monetization. Back founder teams that combine domain expertise with applied ML. Support talent retention via remote work, competitive packages and technical leadership programs.

For startups & founders:

• Focus on data moat and local product-market fit (language support, offline modes, low bandwidth). Build partnerships with governments, NGOs and telecoms to scale distribution. Use open-source models and cloud credits to reduce early compute costs.

For policymakers & development partners:

• Invest in digital infrastructure and national compute capacity, subsidize compute access for startups, and establish clear, proportionate data governance frameworks that protect citizens while enabling innovation.

For multilateral organizations:

• Support data trusts, technical training programs, and catalytic blended finance that de-risks later-stage investment in regional champions.

Quick Verdict

Africa and Latin America are not behind by default, they’re early. With targeted investment, sensible regulation and an emphasis on local problems and data, both regions can deliver disproportionate AI value.

The path won’t be identical in every country, but the ingredients for fast, meaningful leaps are present: use cases with high social and economic ROI, growing startup talent, and a new wave of public and private capital ready to invest.

Fast Facts

Are African and Latin American AI startups attracting meaningful VC?
Yes. After a pause in 2023–24, funding activity rebounded in 2025 across both regions, with AI startups receiving an increasing share of seed and early-stage rounds — though later-stage capital remains comparatively scarce.

Will local language support slow AI adoption?
Language is both a barrier and an opportunity. Generic models often underperform on local languages and dialects; investing in localized NLP and labeled datasets will accelerate adoption and deliver higher user retention and trust.

What’s the fastest way to accelerate AI impact in these regions?
Three levers move the needle fastest: (1) subsidize access to compute and cloud tools for startups; (2) build shared, privacy-preserving data infrastructure (data trusts); and (3) fund use-case pilots in high-impact sectors (health, agriculture, finance) that can be scaled once validated.