Rather than investing in expensive GPU clusters or building large language models (LLMs) from the start, agencies can take advantage of pre-trained small language models (SLMs) and data processing techniques that take advantage of existing data centers. In less than a week, agencies could launch a simple chatbot that answers citizens’ questions, significantly improving citizen services without spending time building everything from scratch.
Leaders should also prioritize experimentation. ChatGPT and Anthropic offer a quick start, data privacy is a legitimate concern. Private genAI uses open-source LLMs or SLMs that provide qatar rcs data agencies with a safer space to try new approaches without leaking data into the public sphere. Depending on what architectures they use, agencies can skip training models but still foster an understanding of potential use cases and limitations. Every day, developers create new techniques to extend private genAI use cases to include things like summarizing documents, translation services, and even augment decision-making. The time for agency leaders to get their hands dirty planting seeds for a future harvest is now.
The path to AI adoption in the public sector isn’t a choice between innovation and sustainability. Thoughtful data center refreshes can deliver the performance and capacity for AI projects while keeping energy costs down. As the landscape of AI continues to evolve, public sector CIOs have the opportunity to lead the charge in responsible AI adoption, demonstrating that technological advancement and environmental stewardship can go hand in hand.