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

Before trying the agent at all, Sait Izmit wrote out 150 questions taken straight from Snowflake's sales process. The engineering team objected that the data behind most of them was not connected. That was the point. The first run scored 50 percent, and the rule that came out of it governs everything since: quality over coverage. Answer 50 questions at 95 percent rather than 100 at 70, because a free form chat box gets judged on its first five answers, and winning back a rep who bounced costs ten times more than earning them. Trust is slow to build and lost overnight. The assistant launched last September and has since answered over a million questions, roughly 40,000 a week, for about 6,000 go to market users. Around 60 percent of its data arrived after launch. It now spans 15 semantic views, 85 tables and 3,000 columns, with MCP connections and some 20 skills layered on. Rollout ran pilot, then a 10 percent beta of 600 people held to a retention bar above 70 percent, then general availability, where the real problem surfaced: only a fifth of the organization had tried it. Izmit reckons 60 to 70 percent of the job is sales meetings and demos, and argues these projects fail at activation, not technology. The second warning is the collapsing wow factor. Talking to your data stops feeling magic within months and becomes the baseline, so the roadmap has to keep moving into workflow automation and team built tooling. Expect to rearchitect rather than shop for the perfect architecture. Speaker info: - https://www.linkedin.com/in/saitizmit/ Timestamps: 0:00 - One million questions, 40,000 a week 1:31 - Half the company is sales, and the data is siloed 3:24 - Trust is earned slowly and lost overnight 4:16 - 150 questions written before touching the agent 5:08 - What the agent grew into 5:46 - Phased launch: pilot, 10% beta, GA 7:14 - Change management is where these fail 8:56 - The collapsing wow factor 9:44 - Talk to your data, then automate, then build 11:49 - Stop shopping for the perfect architecture 14:18 - Logs as a gold mine, and closing lessons