Generative AI is gradually being integrated into the public sector, with the potential to significantly enhance service delivery, policy development, and efficiency. However, widespread deployment remains limited due to structural barriers such as procurement inertia, talent shortages, and trust issues. These challenges are compounded by the dominance of a few established vendors and the difficulty in scaling pilots beyond the initial stages. Despite these hurdles, there is optimism about the future, as smaller, focused AI applications are beginning to demonstrate real value, improving efficiency and building trust. These applications often involve tasks like document redaction and policy aggregation, utilizing retrieval-augmented generation (RAG) to enhance accuracy and maintain data security. For sustainable adoption, public agencies require flexible procurement processes, secure and efficient AI models, and strategic public-private partnerships to navigate these challenges. The emphasis is on collaboration and incremental improvements rather than radical changes, ensuring that AI solutions are both practical and financially viable for the public sector.