One of the world’s most exciting emerging markets, AI innovators are helping everyone from small farmers to a rising generation of young workers to participate in a sustainable and data-driven economy.
The First Industrial Revolution was fueled by steam and water power. The Second relied on electricity, the Third on electronics and information technology. The Fourth Industrial Revolution, says Klaus Schwab, founder of the World Economic Forum, is unfolding as “a fusion of technologies”—the Internet of Things, the cloud, and AI, among others.
Data is the engine of the Fourth Industrial Revolution. Every one of us continuously generates data—economic, social, political, and medical, among others. “Data is a seed for economic transformation,” says Kate Kallot, founder and CEO of Amini, a Nairobi-based environmental-data company. Data is at the center of international markets and shapes entire sectors.
But where data can’t be collected, read, or analyzed, people, regions, and economies—even weather—can be effectively “invisible,” says Kallot. By this definition, Africa is “the most data-scarce continent,” she says. Over the next decade, Africa’s working-age population will increase by 450 million—nearly 70%—and make its mark on the world. Yet broad swaths of the continent are not represented, economically or politically, in the tools we use more and more to understand the world, because most of the sub-Saharan region lacks broad-based access to the high-speed internet.
But as the sub-Saharan population is exploding, so too is excitement about AI. According to a recent study from Google and Ipsos, the Global South expresses more optimism about AI than any other world region—in fact, says Miishe Addy, founder and CEO of AI-driven logistics and financing start-up Jetstream Africa, “I’ve gotten to know more than a dozen diasporans who have been educated and built networks outside who have come back to Africa to build businesses.”
In countries where survey respondents were most likely to think AI will change most jobs and industries in the next 5 years, they also tended to be the most positive about that change. In South Africa, for example, 59 percent declared that changes in jobs and industries as a result of AI in the next five years are “probably a good thing.”
In the years to come, readiness for the AI revolution will be crucial, largely because emerging markets may have the most to gain. In a UN survey of AI readiness in nine global regions, sub-Saharan Africa ranked last. The necessary expansion of technological infrastructure, along with training and up-skilling, will bring people online, giving them access to information and opportunities, and empowering them to share with the world—using images, text, or videos—what they think, feel, and want. The proliferation of both infrastructure and new users will in turn up-level AI models and enable further growth and innovation.
In many ways, this process has already begun. As this new infrastructure blossoms in sub-Saharan Africa alongside an eager, youthful workforce, bold innovators are harnessing AI in surprising ways to address challenges and harness potential in three key areas: agribusiness, finance, and transportation.
As this new infrastructure blossoms in sub-Saharan Africa alongside an eager, youthful workforce, bold innovators are harnessing AI in surprising ways.
I. AGRIBUSINESS
Sub-Saharan nations are among those hardest hit by global warming, with populations that depend largely on agribusiness (as 52% of employed people do) experiencing worsening floods and droughts. Yet Africa—with only one-eighth of the minimum density the World Meteorological Association requires for weather stations—has a scarcity of both historical and real-time data that profoundly impairs meteorological predictions.
Using AI-powered weather forecasting, researchers are working to close this gap. Companies like Amini in Nairobi and projects like Google’s AI for Weather Forecasting in Accra, Ghana use real-time satellite data and historical data to develop highly accurate forecasts that can help the broader population, and farmers in particular, cope with increasingly erratic weather.
This data is critical for food security. “For a farmer,” says Emmanuel Asiedu Brempong, a research engineer on Google’s AI for Weather Forecasting team, “knowing the weather is like a magic wand.” Rainfall predictions can help determine which crops to plant (for instance, rice requires more rain; tomatoes less); and when to administer soil treatments (rainfall helps to dissolve and disperse grains of fertilizer, for instance, but washes away liquid pesticides).
But agribusiness demonstrates how data scarcity can lead to data bias. One bovine-health app found that virtually every cow it evaluated in Africa was malnourished, says Rob Floyd of the African Center for Economic Transformation (ACET). In fact, Floyd points out, the cows were perfectly healthy. The AI needed to be trained, using local data, that African cattle may be naturally skinnier. “All of these models are trained in the United States or Europe,” Floyd observes. “Therefore, they will get things wrong in Africa.”
“All of these models are trained in the United States or Europe. Therefore, they will get things wrong in Africa“. – Rob Floyd, Director of Innovation and Digital policy, ACET
II. FINANCE
People without bank accounts may struggle to prove their creditworthiness. But they do have payment and usage histories, along with networks of contacts. AI technologies can construct an identity from these patterns and connections to create a picture of a person’s finances. “That gives someone, frequently for the first time, a document where they exist as a consumer,” says Davide Strusani, an economist and consultant who has studied AI in emerging markets. “The moment you have a digital identity, you can ask for a digital bank account, then a microloan, and maybe a credit card.”
In Uganda, Yabx aggregates such variables as utility payments, network-usage patterns, and mobile wallet behaviours to assess creditworthiness. The automatic disbursement of payouts via non-bank payments providers, like M-Pesa—active in seven countries, including Kenya, Tanzania, and Lesotho—reduces the barriers farmers without bank accounts may encounter when they seek to purchase financial products related to the climate crisis, such as crop insurance.
Customers register for M-Pesa at authorized outlets—often mobile-phone stores or retailers like barbers, butchers, or bakers—where they exchange cash for electronic funds, which they can use to purchase insurance policies. In Kenya, for example, M-Pesa processes premiums and payouts for a micro-insurance provider, ACRE Africa. ACRE collects premiums prior to the start of the season and compensates farmers roughly three to 20 weeks following the harvest. The policy covers crop losses during four distinct chronological stages—germination, vegetative, flowering, and maturity—and the losses in each stage are calculated at the end of the season to determine the final payout. “If people start texting money,” Strusani says, “they start trusting the system.”
The Challenges
Data scarcity is one of several daunting obstacles to the growth of AI-driven technology in Africa. There are also technological, regulatory, and political challenges, all made more complex by the fact that Africa comprises 54 different nations. “The African Union is a very large, very slow bureaucracy,” Floyd says. For the continent to unify its various policies on artificial intelligence—which in the overwhelming majority of cases have yet to be written—is a complex undertaking.
1. Physical infrastructure
Of the challenges facing the development of AI on the African continent, the most pressing may be the lack of infrastructure—both technological and regulatory. “Internet connectivity in Africa happens mostly in urban areas, and often only on low-speed networks,” says Strusani.
But he notes that the recent installation of two new submarine cables will mean a massive increase in capacity. More broadly, AI itself can assist in the building of high-speed networks. Analysis of satellite imagery by AI-driven projects like WorldPop and Google’s Open Buildings offers invaluable information on population distribution and density that can guide the installation of both technological and electrical infrastructure.
2. Regulatory infrastructure
To date, only one African nation, Rwanda, has announced a national AI policy. Whereas companies or governments drive innovation in many countries, Kallot says, in Africa, “it’s actually a bottoms-up innovation driven out of grassroots communities. And the people who are driving it are not usually the people who have a seat at the table when it comes to discussing regulations.” There is a sense that young AI entrepreneurs are neither represented nor understood by the governing and policymaking generation. “The government too often regulates what it doesn’t understand,” says Addy.
