Across Africa, the most consequential uses of AI do not look like a chatbot. They look like a forecast that warns a nutrition team months before a crisis, a satellite map that shows a planner how land use is changing, a drought assessment that reaches a ministry in time to act. Most of this work runs on data that has nothing to do with language: imagery, health records, weather.
Yet whether any of it changes a person’s decision often comes down to language. One in six people worldwide has now used a generative AI product, according to Microsoft’s 2025 AI Diffusion Report, and Africa has about 1.5 billion people and more than 1,500 languages, yet most AI models were trained mainly on English and a handful of other global tongues.
A farmer looking for planting advice in Dholuo, or a mother seeking health guidance in Amharic, may find that today’s systems cannot speak to them. Language is not everything in Africa’s AI story, but without it, everything else struggles to arrive.
Encouraging progress is being made, and much is led from within the continent. For example, LINGUA Africa, an initiative of the Masakhane African Languages Hub with the Gates Foundation, the Microsoft AI for Good Lab and Google.org, funds open datasets, speech resources and practical language tools. Its recent call, designed to strengthen the language foundations needed for inclusive AI in Africa, drew more than 800 applications from 64 countries, 85 percent of them African.
Yet fluency is not the same as usefulness. A system can answer a farmer in fluent Kikuyu and still provide a recommendation that makes no sense for the soil, the season or the family budget. Language opens the door, but trust depends on whether the advice reflects agriculture’s complex, local realities.
Data scarcity in Africa is not only a shortage of examples. It also means missing communities, outdated maps, and records that capture only the people who managed to reach a clinic. Train a model on data like that and it quietly inherits the same blind spots.
Locally led data collection, documentation and long-term stewardship deserve as much investment as the models themselves. At the same time, scarcity is no reason to stand still: African innovation should be designed to work in today’s conditions, rather than waiting for perfect datasets (and compute capabilities) that may never arrive.
For AI solutions to deliver lasting impact, local institutions must own their development, deployment and long-term stewardship.
As I argued recently in Nature Africa, the hardest part of AI is not the algorithm. Around 600 million people in sub-Saharan Africa still lack electricity, and connectivity, devices, skills and maintenance decide whether an impressive demonstration becomes a service people rely on every day. For Africa, these are not a distraction from the AI agenda; they are a large part of it.
We should aim for more than AI that speaks Africa’s languages. We need AI that reflects its realities, strengthens its institutions and helps people make better decisions. Language belongs at the centre of that ambition, alongside good data, earned trust and the capacity to act.
The writer leads the Africa team of the Microsoft AI for Good Lab in Nairobi and is a member of the UN Secretary-General’s Independent International Scientific Panel on AI.
