Frontier AI models have made it easier than ever to build intelligent applications, but when does it make sense for an enterprise to build and govern its own language models instead of relying solely on external APIs? This talk explores how to evaluate the build-versus-buy decision for AI models in a heavily regulated financial environment, where intellectual property, data sovereignty, governance, and long-term operational costs play a critical role. We will share practical lessons learned from developing, and deploying domain-specific language models at enterprise scale, including the tradeoffs between proprietary and frontier-model approaches. We will share a strategic perspective on model ownership, cost optimization, and the advantages of controlling the AI models. If you are responsible for bringing AI into enterprise ecosystems and making long-term platform decisions, our talk will provide a framework for evaluating when sovereign AI becomes a durable competitive advantage.
