GTM AI GTM

# AI Product GTM

Go-to-market strategy for AI products. These aren't generic AI principles — they're patterns from selling autonomous AI agents into enterprises where "autonomous" scared buyers and "teammate" converted them.

## When to Use

**Triggers:**
- "How do we position this AI product?"
- "Buyers say they're worried about AI breaking production"
- "Should we call it autonomous or copilot?"
- "How do we price AI when usage varies 10x by customer?"
- "Enterprise security passed but ops rejected us — why?"

**Context:**
- AI agent platforms (coding, support, ops)
- LLM-based applications
- Autonomous tools that *do* things (not just suggest)
- AI infrastructure
- Anything where the AI makes decisions

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## Core Frameworks

### 1. The Real Enterprise AI Objection (It's Not What You Think)

**What I Learned Selling Autonomous AI Agents:**

Three months in, enterprise security reviews were passing fast. Good sign, right? Then the pattern emerged: security approved, but **operations rejected us**.

The objection wasn't "will the AI break production?" — they *assumed* it would break production eventually. The real question was:

**"Who's responsible when the agent does something wrong?"**

Not "do we trust the agent?" — "do we trust our *team* to handle this?"

**Why This Matters:**

Autonomous agents create a new operational burden. You're not selling AI capability, you're selling organizational readiness. When your agent halts production at 2am, who gets paged? Who fixes it? Who explains it to the VP?

**Framework: The Accountability Cascade**

Before deploying AI agents, enterprises need clear answers:

1. **L1 Response**: Who monitors the agent? (24/7 ops team, or dev team on-call?)
2. **L2 Escalation**: When agent action fails, who debugs? (Agent team, or product team?)
3. **L3 Ownership**: When something breaks badly, who owns customer communication?

If you can't answer all three, **they won't buy**. Doesn't matter how good your AI is.