When mobile networks first started rolling out at scale, the African market was considered unviable by many of the companies assessing the continent through the assumptions of more developed markets. Fixed-line penetration was low, household incomes were constrained, and the cost of devices and data appeared to cap the addressable market. Africa not only proved those detractors wrong, but those very constraints ended up producing some of the most inventive uses of the technology to date.
Mobile became one of Africa’s great multipliers because it allowed the continent to leapfrog infrastructure it did not initially have and solve real problems with the technology that it could now reach. It changed the point at which participation became viable, opening room for new services to emerge around needs that had always been there.
Artificial Intelligence is now being touted as the continent’s next great multiplier, and there is a good reason for that. For a continent with 1.5 billion people today, heading for 2.5 billion by 2050, with the youngest population on earth, whose customer needs differ enormously across geographies, income levels and digital maturity, no technology has offered more room to create opportunity. Used well, AI puts scarce expertise into more hands, changing the cost and practicality of delivery so that services that were too expensive or too difficult to provide conventionally can now be accessed in a more cost-effective and useful form.
But before getting to the use cases, there is a distinction worth making about what it means for Africa to really benefit from AI. It is tempting to ask whether Africa has the infrastructure to fully exploit AI as if value depends on owning the full stack. That instinct is understandable, but it is the wrong question to be asking about this moment.
The provision of the large models and the infrastructure behind them is going to require levels of investment, compute and power that few developing markets can realistically carry on their own, which means much of that capability will concentrate within more mature economies. That does not make it unusable for Africa, nor does it mean African innovation begins only once the underlying infrastructure is physically located on the continent.
Cloud resolves this, within the constraints of current data residency rules. It allows organisations to reach a capability that would otherwise be too expensive to build at the same scale for themselves, which matters enormously in markets where the need for advanced technology is often much larger than the balance sheet available to fund it. The more important question, then, is what African organisations do with that capability once they can reach it.
The point is not to look at AI only as a cost-reduction exercise. AI can certainly make services cheaper and more efficient to deliver, but that is the smaller prize. The larger proposition in Africa is reach: using the same capability to allow institutions to expand what they offer, to bring more people into the service rather than only improving the economics of serving those who are already inside it.
This is already happening at Absa. We are applying AI wherever friction keeps people out. In Business Banking, an AI-powered fulfilment agent now onboards SME customers 90% faster. Lowering that barrier is not simply an efficiency gain; it widens the door, letting more entrepreneurs in and giving them back time to run their businesses rather than wait on a bank. Similar AI capability shows the point more clearly in the Debt Review process, where since going live in late June 2026, AI-powered OCR (Optical Character Recognition) now automatically processes 80% of all inbound emails and attachments, enabling a one-day turnaround, helping customers already in financial distress get support at the moment they most need it.
Take Abby, our AI assistant. Abby is an agentic system that understands natural language, asks clarifying questions and can now support clients across all 11 spoken official South African languages on our Business Banking site, making the service more usable and more personal for clients who may not have been served as well by a conventional digital channel.
All these use cases are the same underlying technology, extended to very different human problems. We are now using AI to pilot numerous customer and product onboarding processes across our other markets in Africa – build it once, prove it, and bring more people into the service many times over.
That is why the multiplier argument needs a whole new formula.
If AI is going to change more than the efficiency of existing processes, then the technology has to be understood as part of a larger equation:
Cloud comes first because AI needs a different kind of foundation from the technologies before it. It depends on computing power and infrastructure that must keep advancing as the technology does, without asking every organisation to rebuild that foundation for itself. For Africa, that matters because the ambition is not to experiment with AI in isolated pockets, but to make it usable at the scale of real social and economic need.
Cloud turns advanced capability into something organisations can reach and use, rather than something they must first own.
Trusted data is the second part because AI is only as good as the information it works from. Data is just information until it is turned into intelligence, and for AI to use it, it must be trusted. It doesn’t have to be perfect, but it must be reliable, and sufficiently well understood for the technology to interpret the problem it is being asked to solve, and an understanding of local conditions in our markets adds vital richness. This matters acutely in Africa, where imported models will not automatically understand local languages or local behaviours. Trusted data and local context are what make global capability locally useful.
Responsible AI matters because the moment the technology touches services people depend on, trust becomes the whole game. People must have confidence that AI is being used fairly, that its limits are understood, and that an accountable institution still stands behind the outcome. Without that, AI may impress in controlled settings, but it will never earn the confidence needed to be adopted at scale.
The equation multiplies instead of adds for a reason. Addition would make each part add incremental value. Multiplication is more accurate because each part changes what the others are able to do. Cloud gives AI the scale, resilience and speed it needs, trusted data gives it the foundation for intelligence, and responsible use of AI gives it the trust needed for real adoption. If one part is weak, the whole effect weakens.
The result is multiplied human potential because AI may be the most powerful opportunity-creating technology Africa has had to work with.
Mobile phones and the internet became multipliers because they changed who could participate in the economy. AI reaches further still, into almost every industry, and into the decisions people make in their own lives.
In more developed markets, much of the AI race is about efficiency: doing existing things cheaper and faster. In Africa, the proposition should be about reach: doing things that were previously impossible at a reasonable price. That is a fundamentally different multiplier, which means success should not be measured by how many AI tools are built or how many use cases are announced. It should be measured by whether the technology creates an impact that people can actually feel in their lives.
Robert Benvenuti is the Chief Information Officer for Data & Applied AI at Absa
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