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When discussing the localisation of Artificial Intelligence (AI), many assume it simply means AI developed by Zimbabweans. However, the essence of a truly localised AI runs much deeper, it must understand Zimbabwe’s indigenous languages (idioms, slang, and culturally relevant expressions), cultures, and unique challenges while empowering communities from the ground up.
For Zimbabwe to harness AI in a way that truly serves its people, the very first step is not coding or algorithms, it’s building a National AI Knowledge Base rooted in Zimbabwean realities.
“Before any algorithms are written or models trained, Zimbabwe’s first crucial move should be to curate and digitise high-quality local data across key sectors—education, agriculture, governance, SMEs, healthcare, and culture. Why? Because localised AI can only reflect what it’s taught, and if it learns from foreign datasets alone, it won’t speak Zimbabwe’s language—literally or metaphorically,” founder and CEO of LADS Africa Pvt Ltd, Pr. Eng, Tererai Tinashe Maposa told Technomag.
AI is only as smart as the data it learns from.
By Gamuchirai Mapako
If Zimbabwean AI is trained solely on foreign datasets, it will struggle to grasp local languages, cultural nuances, or socio-economic challenges. Imagine an AI agricultural advisor that doesn’t understand Zimbabwe’s soil types or a healthcare chatbot that can’t interpret Shona or Ndebele medical terms, such tools would be ineffective at best, alienating at worst.
“Localized AI isn’t just smart—it’s homegrown smart, built with the soul and heartbeat of the place it serves,” added Eng Maposa.
The million dollar question is, What does building a national AI knowledge base look like?
To create AI that truly understands Zimbabwe, the country must first digitise Zimbabwe’s collective knowledge by converting public records that is school syllabi, agricultural reports, health protocols, and indigenous languages into structured digital datasets.
Universities must be allocated budgets like they do in developed countries to establish AI research hubs focused on curating local knowledge.
In order for the AI to understand the locals, there must be engagement with communities to document vernacular expressions, traditional practices, and behavioural patterns.
Zimbabwe also needs to foster multi-sector data collaboration where government ministries, private companies, and NGOs share data in secure, standardised formats. Platforms like Local Authorities Digital Systems (LADS) and Musiyamwa APP could integrate AI to improve service delivery through ERP systems. Speaking during the 2025 Smart Zimbabwe Conference and Expo, Eng Maposa touched on the EPR system topic, pointing out that one of Zimbabwe’s biggest enemy when it comes to technology is delaying as the government still sits on the company’s offer for a cheaper system citing policies and procedures as the cause.
Speaking to Technomag, Eng Maposa stated, “Government policies and regulations are the backbone of Zimbabwe’s AI journey—they shape not just what’s possible, but what’s ethical, inclusive, and sustainable”
Zimbabwe recently approved a strategy that prioritises sectors like manufacturing, agriculture, mining, and services, aiming to shift from a resource-based economy to one driven by technology and innovation, the National Artificial Intelligence Strategy Framework. The move aligns AI development with the Smart Zimbabwe 2030 Master Plan.
There should be policies that not only emphasise building digital infrastructure like fibre networks, 5G, and cloud platforms to support AI adoption across sectors. The government must then ensure that the people whom the technology is being built for are ready for it. Vandalism is Zimbabwe is still rampant.
Although there has been some improvements made toward embracing AI. Zimbabwe lacks specific legal frameworks for AI. The Cyber and Data Protection Act addresses data privacy, not necessarily AI ethics, accountability, or algorithmic transparency. A draft AI policy exists, but still hasn’t been publicly shared or implemented.
According to Eng Maposa, while officials emphasise the importance of adapting AI to Zimbabwe’s cultural values and languages, there’s no formal roadmap for integrating local languages, indigenous knowledge, or community data into AI systems.
“Government could fund projects that build Shona/Ndebele NLP models, vernacular chatbots, and community-driven datasets,” he said.
It is also of high importance that we prioritise grassroots data collection. Rural farmers, small business owners, and healthcare workers should contribute real-world insights to ensure AI solutions address actual needs.
Many Zimbabwean companies are already using foreign AI tools like ChatGPT and Gemini. While helpful, these systems lack local context, and when met with foreign concepts AI will and can hallucinate in most cases leading to misinterpretations in language, culture, or legal frameworks, exclusion of rural and low-income populations who need tailored solutions and dependency on foreign tech, undermining Zimbabwe’s digital sovereignty.
Once Zimbabwe establishes a robust local knowledge base, the possibilities are vast, we will have AI-powered soil analysis and smart irrigation for smallholder farmers, diagnostic tools that understand local disease patterns and language barriers, AI tutors that adapt to Zimbabwe’s curriculum and learning styles and chatbots assisting citizens with e-services in Shona, Ndebele, and English.
Localising AI isn’t just about technology, it’s about identity, relevance, and empowerment. By starting with a national AI knowledge base, Zimbabwe can ensure that its AI solutions are not just imported, but homegrown, culturally aware, and transformative.
The journey begins with data. The future belongs to those who own it.
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