AI transcription tools are struggling to accurately interpret South African dialects, code-switching and local languages, creating risks for businesses adopting these systems for customer service, compliance, healthcare administration and meeting intelligence.
The challenge stems from how most AI models are trained. Because the leading models are built in English-speaking countries, they are optimized for American or British English and frequently misinterpret other languages — especially when speakers shift between languages mid-sentence, as is common in South Africa.
“Transcription accuracy is becoming a key issue as businesses expand their use of AI across customer engagement, workflow automation, meeting intelligence and compliance-driven environments,” said Warren Hawkins, managing director at Euphoria Telecom. “It matters even more as transcription is increasingly used for meeting summaries, customer service platforms, legal documentation and healthcare administration.”
Hawkins said most transcription solutions struggle with South African dialects, producing outputs that make little sense. The result is unreliable AI summaries, transcripts that cannot easily be searched and slowdowns that AI is meant to eliminate rather than create. The problem deepens when speakers switch into isiZulu, isiXhosa or other African languages — and even when models claim to support these languages, the contextual meaning of a conversation is often lost. Afrikaans is frequently misinterpreted by AI models as Dutch, leading to inaccurate transcripts and translations.
“Businesses are moving quickly to adopt AI transcription, but that only delivers value if the system can understand how we actually speak,” Hawkins said. “If a model cannot handle South African English, Afrikaans, isiXhosa or isiZulu properly, it introduces risk and reduces usability.”
A structural challenge is that the bulk of AI investment is concentrated around major developers in the United States and a few other regions, meaning African markets are continually playing catch-up. Building a large language model capable of accurately transcribing African languages requires significant capital and time — a task that cannot be accomplished quickly. African-focused projects such as Lelapa AI are tackling that gap, but global players have shown limited interest in non-English speakers. That gap could prove costly given Africa’s population of more than one billion people.
“Language capability is part of the core infrastructure that determines whether these systems are accurate and usable in practice,” Hawkins said. “For businesses investing in AI, generic transcription is inadequate. If a model cannot understand how South Africans actually speak, the technology will struggle to deliver where it counts.”
