In emerging markets, the real AI opportunity is product leadership

Digital illustration of Earth surrounded by glowing blue circuitry patterns, symbolizing global connectivity and technology networks.

Artificial intelligence connects global digital networks, data systems, and economic activity across emerging markets. Photo credit: geralt via Pixabay.

As artificial intelligence has evolved from a scientific milestone into an economic and geopolitical force, it is reshaping emerging markets in everything from energy development to national competitiveness. Recent analysis highlights how AI investment is influencing economies such as Brazil, Kenya, Kazakhstan and Saudi Arabia and offers a vivid picture of how developing regions are positioning themselves for the future.

There is a growing opportunity for product leaders to evolve how they think and operate to translate AI investment into lasting economic value. Emerging markets across Africa, Asia, and Latin America are adopting AI technologies at scale and attracting investment. Converting these investments into sustainable economic value requires not only engineering expertise, but also strong commercial judgment, cultural and regional insight, and a deep understanding of the geopolitical forces that influence business. These capabilities together constitute a more global form of product management that is becoming essential for the AI era.

This topic is especially important now because AI business models are still evolving. Many AI products generate cost savings, but few sustain commercial value on their own. Emerging markets play a critical role in the future of AI not because they are untapped, but because they are shaping how technology becomes sustainable over time. As AI struggles with durable business models, identifying monetizable use cases is less about extraction and more about ensuring that innovation can persist, scale responsibly and deliver long-term value. An important question is how AI products will be designed, governed and led so that they create value that is economically viable, locally relevant and broadly shared.

AI acceleration in emerging markets

The pace of AI adoption in emerging markets reveals both the scale of the opportunity and the complexity of the landscape. Kenya has become one of Africa’s fastest-growing technology hubs and has launched a national AI strategy designed to attract global partnerships and scale digital infrastructure. International companies like Microsoft and Huawei are expanding their presence in Nairobi and supporting the development of cloud services, connectivity and data platforms that enable AI deployment. Kenya’s mobile-first economy, entrepreneurial energy, and position as a regional technology leader make it a promising environment for AI-enabled products.

Kazakhstan illustrates a variation of this trend. The country has secured more than $3 billion in investment from global firms such as Nvidia and Oracle and is leveraging its geographic position between the East and the West to negotiate new forms of economic collaboration. Kazakhstan’s strong STEM foundation, developed during the Soviet era, is helping it compete for AI-centered industries. The country also participated in a summit with leaders from the five Central Asian countries and the United States in November in Washington, D.C., and entered an agreement on the procurement of $2 billion in AI chips from Nvidia and OpenAI. These developments show how AI can reshape trade and partnership patterns and how emerging markets are using technological ambition to elevate their geopolitical relevance.

Kenya and Kazakhstan illustrate that AI is not merely a new tool for economic growth. It is a catalyst for new forms of global engagement and an entry point into high-value sectors.

The product leadership shift needed to capture AI’s economic potential

AI adoption across emerging markets is expanding quickly, which calls for a broader product leadership mindset that reflects the realities of these regions. Traditional frameworks often assume stable infrastructure, mature data systems and consistent user behavior, which does not match the diversity seen across markets such as Kenya, Indonesia and Brazil. Connectivity can vary widely; user patterns often mix formal and informal systems; and cultural and linguistic diversity shape how people interact with technology. Product leaders also operate within regulatory and geopolitical environments that influence investment decisions and technology partnerships. These factors create a need for product managers who combine commercial discipline with cultural insight and global awareness.

This shift is especially important because few AI business models generate sustainable commercial value on their own. Emerging markets offer opportunities to identify practical and scalable use cases such as agriculture, logistics, small-business tools and public service delivery. These sectors contain real-world problems that can translate into monetizable AI solutions. Companies that build global product teams with strong local understanding will be best positioned to discover these opportunities. The combination of regional insight and global collaboration can unlock innovation and help transform AI investment into long-term economic impact.

Case studies in practical innovation

Kenya demonstrates how AI can succeed when product teams are highly attuned to local behavior. The mobile-first culture that produced the mobile payment platform M-Pesa has required product leaders to design for low-bandwidth environments, hybrid payment systems and decentralized user patterns. This mindset translates directly to AI-enabled applications where accessibility and cost effectiveness matter.

Kazakhstan shows how emerging markets can use AI investment to expand geopolitical influence and develop higher-value industries. The country’s partnerships with global technology players highlight the need for product leaders who understand not only engineering constraints, but also international partnerships, data sovereignty negotiations and cross-border scalability.

Both cases reveal that successful AI products in emerging markets depend on product leadership that is globally aware, commercially grounded and deeply informed by regional contexts.

About the author

Ethan Davis

Ethan Davis, MBA ’26 is an Emerging Markets Fellow, a Johnson Leadership Fellow, and ROMBA Fellow at the Cornell SC Johnson College of Business, where he focuses on emerging markets, sustainable global enterprise, and the strategy and governance of advanced technologies. He is a product leader with experience across education, nonprofit and enterprise consulting. Grounded in his background in education and social impact, Davis approaches product development through a human-centered and system-driven lens with a focus on building inclusive platforms that translate technological innovation into scalable value creation.

All views expressed in articles published on the Cañizares Center for Emerging Markets webpage are those of the author(s) and should not be taken as reflecting the views of the Cañizares Center for Emerging Markets.

Ethan Davis MBA ’26