---
title: "Building GTM AI Agents: Lessons from Deploying to 6,000 Users"
category: "talks"
date: "2026-07-01"
time: "3:20pm-3:40pm"
track: "AI in GTM"
room: "Track 6"
speakers: ["Sait Izmit"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: "AI in GTM"
scheduleRoom: "Track 6"
scheduleLabels: ["AI in GTM", "Track 6", "session", "confirmed"]
---
# Building GTM AI Agents: Lessons from Deploying to 6,000 Users

## Conference Context
- Date/time: 2026-07-01 · 3:20pm-3:40pm
- Track/room: AI in GTM · Track 6
- Speaker(s): Sait Izmit
- Session type/status: session · confirmed

- Track: AI in GTM
- Room: Track 6
- Session type: session
- Status: confirmed

## Session Description
Building an enterprise AI agent for GTM teams isn't just an LLM problem—it's a product, engineering, and adoption challenge. In this session, I'll share how we built and scaled Snowflake's internal GTM AI Assistant from MVP to a production system serving more than 6,000 employees and answering over one million questions. We'll cover how we scoped the MVP, evolved the architecture over time, balanced quality versus coverage, adopted emerging technologies like MCP, and continuously adapted as the AI landscape rapidly changed. You'll leave with practical lessons for building enterprise AI products that users actually trust and use.

## Media Evidence
No related AI Engineer channel video found yet.

## Evidence Graph
This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

### Media Signals
No linked video, transcript, or slide source has been attached yet.

### Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

## Transcript Status
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

## People
- [[sait-izmit]]

## Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.

## Synthesis
### Synthesized Breakdown
# Building GTM AI Agents: Lessons from Deploying to 6,000 Users ## Conference Context - Date/time: 2026-07-01 · 3:20pm-3:40pm - Track/room: AI in GTM · Track 6 - Speaker(s): Sait Izmit - Session type/status: session · confirmed - Track: AI in GTM - Room: Track 6 - Session type: session - Status: confirmed ## Session Description Building an enterprise AI agent for GTM teams isn't just an LLM problem—it's a product, engineering, and adoption challenge. In this session, I'll share how we built and scaled Snowflake's internal GTM AI Assistant from MVP to a production system serving more than 6,000 employees and answering over one million questions. We'll cover how we scoped the MVP, evolved the architecture over time, balanced quality versus coverage, adopted emerging technologies like MCP, and continuously adapted as the AI landscape rapidly changed. You'll leave with practical lessons for building enterprise AI products that users actually trust and use.

### Speaker And Company Context
- [[sait-izmit|Sait Izmit]] — Principal Product Manager at [[snowflake|Snowflake]].

### Topics Covered
- [[agent-security]]
- [[mcp]]

### Derived Links And Source Material

### Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.

### Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.