Position paper: In many African countries, the informal business sector represents the backbone of the economy, providing essential livelihoods and opportunities where formal employment is limited. Despite, however, the growing adoption of digital tools, entrepreneurs in this sector often face significant challenges due to lack of literacy and language barriers. These barriers not only limit accessibility but also increase the risk of fraud and financial insecurity. This position paper explores the potential of conversational agents (CAs) adapted to low-resource languages (LRLs), focusing specifically on Mooré, a language widely spoken in Burkina Faso. By enabling natural language interactions in local languages, AI-driven conversational agents offer a promising solution to enable informal traders to manage their financial transactions independently, thus promoting greater autonomy and security in business, while providing a step towards formalization of their business. Our study examines the main challenges in developing AI for African languages, including data scarcity and linguistic diversity, and reviews viable strategies for addressing them, such as cross-lingual transfer learning and data augmentation techniques.