LANGUAGE MODELS CAN NOW PROVIDE HEALTH GUIDANCE VIA TEXT MESSAGE IN DOZENS OF AFRICAN LANGUAGES. THESE INNOVATIONS WORK BECAUSE THEY ARE DESIGNED WITH LOCAL CONTEXTS IN MIND. THEY ACCOUNT FOR INTERMITTENT ELECTRICITY AND LIMITED INTERNET CONNECTIVITY. THEY RESPECT LOCAL HEALTH PRACTICES WHILE COMPLEMENTING THEM WITH EVIDENCE- BASED MEDICINE. THEY ARE BUILT IN PARTNERSHIP WITH THE COMMUNITIES THEY SERVE, RATHER THAN BEING IMPOSED FROM OUTSIDE. Or consider small-scale farmers, who represent the majority of the world’s food producers. These farmers lack access to agronomists, soil testing laboratories, market information, and weather forecasting services. They farm with knowledge passed down through generations, which is increasingly insufficient as climate change disrupts rainfall patterns and introduces new pests. The consequences are devastating and risk threatening food security for entire nations. AI can analyse soil conditions and weather patterns while providing personalised recommendations in local languages through simple voice interfaces. These systems can alert a farmer in a rural area that their maize crop shows early signs of fall armyworm infestation
warnings of job displacement and by utopian promises of effortless abundance. We hear too often about AI replacing doctors, eliminating teachers or surpassing human creativity, thus making us wholly irrelevant. Perhaps our error lies in viewing these structures and systems solely through their technical capabilities. When we shift our lens from technical capability to societal impact, an entirely different landscape emerges. We begin to see AI as a tool for democratising access to knowledge rather than a replacement for human intelligence. Herein lies an opportunity to liberate people and create space for meaningful contribution. Let me be concrete about what this means in practice. Consider education. In many parts of the world, quality education remains inaccessible. In South Africa, for example, millions of young people are excluded not because they are unwilling but because structural barriers prevent them from participating meaningfully in these opportunities. These barriers include underfunded schools, limited digital infrastructure, socio-economic exclusion and even the legacy of apartheid spatial planning. For those of us at the helm of higher education institutes, it has been apparent that AI offers tangible ways for us to close this access gap. For instance, AI can serve as a tireless tutor available 24/7 that adapts to each student’s pace and learning style and could even offer explanations in their native language. Critically, of course, this AI tutor should not replace the human teacher. The goal is not automated education but rather amplified human teaching that reaches more students more effectively. AI could also be used to reach a wider audience. For example, we know that developing more massive open online courses (MOOCs) could be one solution to access challenges, funding constraints and skills gaps. It is by removing geographical, financial and physical barriers that online education provides opportunities to those who might otherwise be excluded from traditional academic pathways. Or consider healthcare. So many die from preventable diseases because they lack access to medical expertise. AI diagnostic tools, which can be run on basic smartphones, can analyse symptoms and suggest treatments. This brings specialist-level knowledge to remote clinics staffed by community health workers. AI-powered ophthalmology tools can detect diabetic retinopathy from a simple retinal scan, enabling early intervention in communities that may never have access to an ophthalmologist. Algorithms trained on chest X-rays can identify tuberculosis with accuracy comparable to expert radiologists, which is critical in regions where TB remains a leading killer, but diagnostic infrastructure is scarce. Voice-based symptom checkers can conduct preliminary health assessments in local languages, triaging patients and helping community health workers prioritise urgent cases. Much like my example of education, the goal is not to replace the doctor but to extend their reach. We must go further. For instance, we have seen promising initiatives where AI is being deployed to predict disease outbreaks by analysing patterns in social media alongside environmental factors. This gives health systems precious time to prepare and respond.
and recommend organic pest control methods before the damage becomes catastrophic.
They can advise farmers on optimal planting dates based on hyperlocal weather predictions while accounting for the specific microclimate of their particular plot. They can also inform farmers about fair market prices in real-time. This serves as a way to democratise knowledge that was once available only to large industrial farms. It must be noted that the role of AI is not to tell the farmer that their ancestral knowledge is wrong. But it is a tool to enhance that knowledge with information they couldn’t previously access. The result is a powerful synthesis of indigenous knowledge and technological innovation, which creates agricultural practices that are both productive and sustainable.
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