The rise of chatbots in AI customer service has changed digital interactions, yet their effectiveness in emerging markets is underexplored. This study examines chatbot adoption, user satisfaction, and frustration in six African markets, merging the Unified Theory of Acceptance and Use of Technology (UTAUT) with Expectation Disconfirmation Theory (EDT). Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the chapter analyzes responses from 2,600 users, showing that perceived ease of use, performance expectancy, and social influence significantly drive chatbot adoption. Additionally, expectation disconfirmation moderates the link between chatbot adoption and satisfaction; negative expectation disconfirmation heightens user frustration. The findings indicate that while chatbots improve service efficiency and accessibility, misalignment between user expectations and performance leads to dissatisfaction. Social influence further encourages chatbot adoption and satisfaction, highlighting the role of peer validation in AI acceptance. Multi-group analysis reveals regional differences in chatbot experiences, pointing to the need for localized AI strategies. The study contributes to technology adoption literature in emerging markets, providing theoretical, managerial, and policy insights for optimizing chatbot services. It advocates for ongoing chatbot refinement, transparent expectation management, and appropriate regulations to enhance AI service ecosystems.