This study investigates how Nigerian language research is moving from documentation toward the practical deployment of artificial intelligence (AI) systems. Although Nigeria is characterized by extensive linguistic diversity, many indigenous languages remain largely absent from digital and computational spaces, often classified as low-resource. To better understand this gap, the study draws on the Resource-Based View (RBV) and Diffusion of Innovation (DOI) theory, focusing on how the availability of linguistic resources and patterns of technological adoption shape the develop- -ment and uptake of AI applications.Using a qualitative approach, the study conducts a thematic analysis of existing literature in linguistics and natural language processing. Six recurring themes emerge from the analysis: the role of language documentation as foundational infrastructure, the persistence of low-resource conditions, the rise of AIdriven language technologies, the shift from research to real-world deployment, ongoing ethical and linguistic challenges, and the need for a more integrated development framework. The findings indicate that, while notable progress has been made in building datasets, speech corpora, and AI tools for Nigerian languages, these advances have not yet translated into widespread, deployable systems. Several constraints continue to limit this transition, including data scarcity, infrastructural gaps, bias in model develop- -ment, and uneven patterns of innovation diffusion.Addressing these limitations requires more than incremental technical improve- -ments. A coordinated strategy is needed—one that brings together resource development, technological innovation, and institutional support. In response, the study recommends the standardization of datasets, wider adoption of open-access tools, stronger interdisciplinary collaboration, and the development of structured frameworks to support sustainable, AI-driven language development in Nigeria