Research on Information Retrieval (IR) has historically prioritised high-resource languages such as English and Chinese, with less attention given to many low-resource languages. For example, Shona, a Bantu language spoken by approximately 12 million people in Zimbabwe and neighbouring countries, remains under-explored in IR research despite its widespread societal use in Southern Africa. In this work, we present a preliminary study of Shona IR using sparse and dense retrieval models, demonstrating significant performance limitations due to morphological complexity and data scarcity. Based on these findings, we propose to develop a framework to advance Shona IR by developing a large-scale benchmark dataset to support morphology-aware retrieval. We hypothesise that improving Shona IR supports equitable access to digital information and enables language-inclusive AI technologies aligned with global development priorities such as accessibility to education, dissemination of healthcare information, and digital inclusion.