Why AI adoption may be lagging in Global South businesses

The geographical divide in AI adoption between what are often called the Global North and the Global South can feel like a chasm. While the Global North rushes to swallow up both compute capacity and resources to power further AI buildout, the Global South is only now beginning to host broader industry events that have become commonplace elsewhere.

This February, the United Nations hosted what they billed as the first AI summit in the Global South to, in their words, help “democratize AI”. The event included sessions with titles like Multilateralism and the Future of AI , The Role of Science in International AI Governance , and AI and Children’s Safety and Wellbeing.

But first, let’s slow down. Research on AI adoption in the Global South is evolving and some scholars argue that we shouldn’t be so quick to use terms Global South at all. One report from the Stanford Institute for Human-Centered AI found that those who study this area feel as if the term carries with it certain harmful connotations such as “harmful stereotypes of homogeneity, underdevelopment, and technological illiteracy”. In other words, just like Canadians can often bristle at being called American, it’s unfair to those building towards AI adoption to lump together large swathes of Asia or Africa for simplicity’s sake.

Data on AI adoption anywhere is a swiftly moving target, but some clear conclusions can be drawn from at least one industry giant. In one 2025 report, Microsoft highlighted Global South AI diffusion – defined as the percentage of people who have used a generative AI tool during the second half of the year – lagging 10 percentage points behind that in the Global North (14.1% versus 24.7%).

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This has profound implications for the businesses in these regions. If claims about AI productivity are taken at face value, the Global South could begin to notably lag behind AI ROI.

That’s not to say there aren’t outliers, or that the Global South isn’t leading in some cases. The United Arab Emirates and Singapore, for example, had the highest AI diffusion rates, more than twenty spots ahead of the US. This, in and and of itself, points to the dangers of seeing the Global South as a monolith. Researchers like Cornelia C. Walther, argue that part of the issue is that the world’s traditional powerhouses do not work for those in the Global South, as she wrote for Wharton:

“Three dominant AI paradigms have emerged: the United States’ market-driven model, China’s state-coordinated approach, and the European Union’s regulatory framework. Yet for the Global South – 85% of humanity – none of these models adequately addresses the interconnected challenges of development, dignity, and sustainable growth. They are being asked to choose a side, but none of the options truly serve them.”

The three large barriers

The tech world’s approach to the Global South has historically been extractive, taking high level talent from areas like India and bringing them to industry hubs like Silicon Valley. Arun Kumar, the global head of product and industry practice at Altimetrik, says that’s starting to change, but that challenges in the Global South when it comes to AI adoption boil down to three key categories: data, infrastructure, and people. That last one, he says, is particularly difficult.

“Brain drain for us is a true challenge. So AI talent and its ability to build AI models and so on, remains an issue in India.”

Kumar is a prime example himself, having taken that leap into the American workforce like so many have done in the past. The difference? He came back. However, he thinks that the data problem goes beyond the ability to get chips or to produce enough energy. Instead, it’s a question of nuance and what is lost when the default language of much of the world’s trade, English, doesn’t match local realities.

“A lot of the applications for the first 20 years were primarily based on English. So the data quality over there is great, but on the non English dimensions, the data quality may not be as complete.”

In a country like India, where 22 languages are officially recognized and many more exist, Kumar says this can be particularly important to consider when judging the effectiveness of AI tools like agents. He gives the example of a call center interaction where the staff member is summarizing the call for or with AI tools but the customer is communicating in a local language.

“The richness of the data is gone, right? The richness of the conversation is gone… There are people who speak extensively good English in this country, because our medium of instruction is English. But if the speech and the way of conversation is local, and my method of recording is in English. You're losing a lot of the nuance and the quality.”

Kumar points to large language models like the government funded Bhashini as an example of what can happen when local expertise meets this barrier.

What about companies in the Global North?

Jason Mann brings another perspective to why certain areas of the world lag behind when it comes to AI adoption. After working with an organization that focuses on international entrepreneurship, Mann says he’s found that working with people from the Philippines, where he has family connections. He runs STOCK, a marketing agency that hires talent remotely from the Philippines and teaches them how to use AI tools to benefit his business. He says that the biggest difference he sees between his local workforce and those he hires from the Philippines is how AI is discussed.

“I think, in the West and the global north and such, we are already so well aware of AI. Everyone's utilizing [it], It's a day to day conversation. You know, it's water cooler talk at this point. But within our team in the Global South, and again, specifically the Philippines, it's interesting to see how it hasn't been something that, I would say, is widely adopted, or something that's been utilized, or something that's been focused on for upskill and training.”

At Mann’s agency, staff are using tools like Claude and Perplexity to run reports, do competitive research, and trend analysis. He is also an advisor with Slice Global Equity. His argument is that, even as small businesses that are supported by entrepreneurship projects via micro grants in places like the Philippines often take the form of restaurants and stores, increased AI adoption can shift that dynamic towards jobs that better position workers for the ongoing technological revolution.

“Why were those the businesses that were getting created? The answer to that is because it's what they know. It's what they've been aware of, it's what they've been given access to. Now that I see how the world is changing, and within the axis of AI, there's the opportunity for people to expand their knowledge into other areas, to create different businesses and take advantage of this new landscape.”

What does that type of knowledge building take? Well, it turns out Mann thinks the answer is the same regardless of where you are in the world.

“We look for a work ethic and, most importantly, a curious mind. I think within AI, actually, having a curious mind is the most important skill that you can have, because we have so much access and ability to utilize these tools in a multitude of different ways. Different ways. And when we find people that have those work ethics, those curious minds and that desire to really push themselves forward and have the opportunities that past generations didn't, that leads me to believe that this is someone that has the ability to learn these skills because they have the desire to do so.”