AI in Africa must understand Africa
AI is becoming part of how Africa creates, communicates and does business. Yet access to these tools will only take the continent so far. For AI to become genuinely useful across African markets, it also needs to understand the people using it.
Artificial intelligence is moving quickly into everyday creative work. It can generate images, translate copy, produce voiceovers, analyse audiences and help businesses develop ideas faster. For African creators, brands and entrepreneurs, the opportunity is significant.
The conversation around Africa has therefore focused heavily on access. Who has the tools? Who has the skills to use them? Who has the infrastructure to participate?
While these questions matter, another is becoming just as important: how useful is AI if it does not understand the market it is being asked to serve?
Africa is home to more than 2,000 languages, most of which remain absent from global AI systems. The gap goes beyond language with culture, humour, images, knowledge and even the way people access technology being poorly reflected by systems largely trained on information produced elsewhere.
For Africa, the next stage of AI adoption therefore cannot only be about getting more people onto existing tools. It also has to be about building tools that understand them.
Speaking the language is only the beginning
Language is perhaps the clearest place to see the problem.
African languages remain severely underrepresented in the digital material used to develop today's AI systems. Even when a language technically exists within a model, understanding it is another matter. A 2024 study into Nigerian Pidgin, for example, found that generative AI models struggled to properly distinguish Nigerian Pidgin from broader West African Pidgin.
People do not communicate in standardised datasets, they code-switch. Dialects change between places, and expressions develop locally. Building useful African-language AI therefore means understanding how people actually communicate. Closing that gap could do more than make today's tools more inclusive and also expand who businesses are able to reach.
A customer-service system could allow someone to ask questions in Wolof rather than French. A financial service could explain its products through a local-language voice assistant. Media companies could translate and dub content across markets more easily, while brands could produce local-language campaigns and adapt content without limiting their communication to English or French.
The commercial opportunity is significant and GSMA, the global mobile industry association, has identified local-language AI as a way to broaden access to digital services and expand the addressable market for AI-powered products and a company that can communicate with consumers previously excluded by language can gain customers it could not effectively reach before.
This is why initiatives such as ATLAS Umoja AI matter beyond the technology sector. By developing datasets and models that better reflect African languages, cultures and knowledge, they could provide infrastructure on which other companies can build. For African startups in particular, that creates room for new customer-service tools, education products, financial services, media platforms and creative applications designed around local-language users.
The opportunity is not simply to translate the digital economy that already exists. It is to build products and services that become possible because technology can finally communicate with more of the people they are meant to serve.
Africa also needs to recognise itself
Language, however, is only one part of understanding.
Generative AI is becoming part of advertising, design, filmmaking, fashion and content production, and research has identified racial and gender biases in image-generation systems. A 2024 study examining DALL-E 2, Stable Diffusion and Midjourney,found systematic biases across images generated for different occupations.
This raises a practical question for African creatives: when we ask AI to visualise our markets, what does it see?
Recognising Black skin is only the starting point. A useful creative tool needs enough visual knowledge to represent environments, hairstyles, clothing, food, architecture and everyday life without reducing an entire continent to a handful of familiar references.
Dakar is not Lagos. Lagos is not Nairobi. Abidjan is not Johannesburg.
That distinction becomes commercially important as creative teams increasingly use generative tools. A brand trying to reach consumers in Senegal does not simply need an image containing African people. It needs work that feels credible to the people who actually live there.
Poor representation therefore creates more than an aesthetic problem. If African agencies, designers and brands spend their time correcting AI outputs because the technology cannot accurately interpret their markets, some of the productivity promised by these tools disappears.
Better visual understanding could have the opposite effect. It could give creative teams faster ways to prototype locally relevant ideas, help smaller businesses produce better visual material and give African creators more useful tools for bringing their own worlds to life.
Culture cannot simply be translated
Another interesting angle that cannot be treated separately is that of cultural nuance.
An AI model can become better at speaking Wolof, Yoruba or Kiswahili without necessarily understanding the cultural context behind those languages.
Humour is an obvious example. So are references, social norms, histories and the meaning attached to certain images or expressions. These are often precisely the ingredients that make creative work resonate. For a creative business, technically correct language is not enough if the idea behind it feels wrong.
A campaign generated for Côte d'Ivoire might use perfect French and still miss the humour that would make it connect. A piece of content might depict the right people while misunderstanding what a gesture, outfit or phrase communicates locally. The more brands use AI to generate ideas and content at scale, the more these distinctions matter.
Research is beginning to expose this gap more systematically. Afri-MCQA, a benchmark covering 15 African languages and 12 countries, found significant performance gaps when AI models were tested on culturally grounded questions in African languages. Another project, AfriStereo, was developed after researchers found that existing stereotype benchmarks largely reflected Western perspectives, making them poorly suited to evaluating how models represent African societies.
This is also why talking about AI that “understands Africa” requires some caution. There is no single African context for a model to learn.
The challenge is almost the opposite. Useful AI needs to become better at recognising differences between countries, languages, communities, generations and cultural references.
That is particularly important for Africa's creative economy, where cultural specificity is often the source of the value. The goal should not be an AI that produces something vaguely African. It should be technology capable of helping people create something specifically Senegalese, Kenyan, Nigerian, Togolese or Ivorian when that is what the work requires.
The smartest AI may be the one people can actually use
Understanding a market also means understanding how people interact with technology.
MAGGI MAMI offers an interesting example. Developed with Viamo, the initiative provides AI-powered business guidance to women working in informal food commerce. Launched first in Côte d'Ivoire, mammies can call a toll-free number from a basic mobile phone, ask questions in their local language and receive practical advice without needing internet access.
MAGGI could have built an AI-powered platform that assumed its users had smartphones, data and the confidence to navigate another digital interface. Instead, the service is designed around the conditions in which its audience already operates. Its knowledge is also rooted in the experiences of established mammies. Their experience with pricing, stock, cash flow and customer relationships becomes part of the knowledge the tool can pass to newer traders.
That makes MAGGI MAMI useful as more than another example of a brand experimenting with AI. It demonstrates what changes when technology starts with a local problem rather than looking for somewhere to apply a global solution.
For brands exploring AI across African markets, that principle is worth remembering. The most sophisticated technology is not necessarily the most useful one. Sometimes innovation means designing around the phone already in someone's hand, the language they already speak and the way their business already works.
From access to relevance
Africa still needs greater access to AI tools, training and infrastructure. AI can lower production costs, help creators work faster and make it easier for African businesses to reach new audiences. Those opportunities, however, depend on skills, systems and access being in place.
Access should not become the finish line.
The bigger opportunity is for African markets to help shape what these technologies become. That means stronger African-language datasets, more locally grounded research, better representation in visual systems and more African creators, researchers and businesses participating in how AI products are designed and evaluated.
AI that understands local languages can help companies reach consumers they previously struggled to serve. Better visual understanding can make generative tools more useful to African creative teams. Greater cultural understanding can help brands localise without flattening every market into the same idea of Africa. Technology designed around local behaviour can reach people that internet-first products leave behind.
For entrepreneurs, those gaps are opportunities too. There is room to build applications, interfaces, creative tools and services on top of African-language and culturally aware AI infrastructure. Some of the businesses created around AI in Africa may succeed precisely because they understand something about their users that a global product does not.
The question, then, is no longer simply whether Africa will adopt AI. It is whether the AI Africa adopts will understand enough about Africa to be genuinely useful.
And perhaps the bigger opportunity is not simply making global AI work better for African markets. It is seeing what better products, businesses and creative possibilities can emerge when African realities help shape the technology from the beginning.
Sources :
GSMA, “GSMA Welcomes Abuja Declaration on Meaningful Connectivity for Africa and Joins Partners to Launch ATLAS Umoja” gsma.com
ATLAS Umoja AI, “African Tongues and Languages at Scale” atlasumoja.ai
ACL Anthology, “Does Generative AI speak Nigerian-Pidgin?: Issues about Representativeness and Bias for Multilingualism in LLMs” ACL Anthology
arXiv, “Bias in Generative AI” arxiv.org
ACL Anthology, “Afri-MCQA: Multimodal Cultural Question Answering for African Languages” ACL Anthology
arXiv, “AfriStereo: A Culturally Grounded Dataset for Evaluating Stereotypical Bias in Large Language Models” arxiv.org
