Artificial intelligence could unlock new investment opportunities across emerging markets by helping small and medium-sized businesses improve productivity, strengthen profit margins and compete more effectively, according to an executive at the International Finance Corporation.
Mohamed Eissa, the IFC’s chief investment officer responsible for global venture capital and direct technology investments, said AI applications tailored to specific industries could transform businesses whose low margins have historically limited their ability to grow.
Speaking on the World Bank Group development podcast, Eissa argued that the investment opportunity extends beyond companies developing large AI models to businesses applying the technology to practical commercial problems.
“I think of the world as what does tech-enabled businesses look like?” Eissa said, describing the potential for AI to improve operations in businesses that generate data but have not fully exploited it.
He gave the example of industry-specific applications that could help a business move from a 5% profit margin to 15% or 20%. The figures were presented as a potential scenario, not as measured results from a particular company.
The opportunity is significant for emerging-market economies where small businesses form an important part of commercial activity but often face constraints in financing, technology adoption and operational efficiency.
Eissa said AI could help improve the competitiveness of small and medium-sized enterprises by enabling them to automate selected tasks, analyse information and improve decision-making. Such changes could support business growth and create opportunities for technology providers serving specific industries.
Investment opportunities extend beyond software
Eissa identified healthcare, financial services and public education as three areas where he sees opportunities for vertically focused AI applications in emerging markets.
In healthcare, he described investments in companies using AI to digitise health systems and generate synthetic data to help address the lack of patient health records. He said some of these businesses were also connecting healthcare providers with insurers, pharmacies, consumers and governments.
Such applications could help address information gaps that complicate the delivery of healthcare services, while creating opportunities for companies developing technology around local needs.
Financial services and retail also offer potential applications, as businesses use data and automation to improve operations and reach customers. Eissa said entrepreneurs in emerging markets were exploring AI-driven businesses in areas including embedded finance, financial technology and healthcare.
The investment case does not depend on emerging markets building the computing infrastructure required to develop the world’s largest AI models. Eissa said gaps in computing capacity, connectivity and reliable power constrained that activity, but businesses could still adopt existing AI tools to solve local commercial problems.
That distinction separates investment in foundational AI infrastructure from investment in companies that use AI to deliver services or improve existing business models.
Small businesses face a technology adoption test
Raghavan said LinkedIn was observing faster growth in AI literacy and skills among small and medium-sized businesses than among larger enterprises. He argued that productivity improvements could help address the gap between very small firms and larger companies in economies where the middle tier of businesses is underdeveloped.
“If AI tools can make people more productive, can increase their viability so they can move into that middle chain, you’re actually going to have a lot more healthier economic structure,” Raghavan said.
For investors, the discussion points to a broad field of potential opportunities in companies that combine sector expertise with AI tools, rather than concentrating only on technology developers.
The commercial outcome will depend on whether businesses can deploy the technology effectively, solve genuine operational problems and turn productivity improvements into sustainable growth.
The broader economic question is whether the gains from AI adoption can strengthen companies enough to support expansion and employment, rather than delivering efficiency improvements primarily through cost reductions and workforce cuts.
