Building GTM AI Agents: Lessons from Deploying to 6,000 Users — Sait Izmit, Snowflake

Building an effective internal AI go-to-market assistant requires prioritizing high-quality responses over broad coverage to establish initial user trust. Successful deployment hinges on rigorous change management, as adoption often fails due to lack of engagement rather than technical limitations. Once an MVP is established, teams must combat the "collapsing wow factor" by continuously iterating and integrating new capabilities to maintain user interest. Leveraging real-time logs creates a powerful feedback loop, allowing for the identification of knowledge gaps and the automation of complex workflows. By treating the AI system as an evolving platform rather than a static project, organizations can transition from simple data access to sophisticated, personalized orchestration. This iterative approach, supported by active leadership sponsorship and constant re-architecting, transforms AI from a novelty into a core driver of sales productivity and incremental revenue.