Gtm Ai Gtm

This is for anyone selling AI products into enterprises and hitting the "who's responsible when it breaks?" wall. It's not generic positioning advice. It's specific patterns from selling autonomous agents where security approved but ops teams kept saying no. The frameworks cover why "teammate" converts better than "autonomous," how to handle the accountability cascade question (who gets paged at 2am?), and pricing when usage varies 10x between customers. Most useful when you're stuck on enterprise deals that stall after security review, or when you're debating copilot versus agent framing. Written by someone who clearly lived through these specific sales problems.

skills.sh installs: 1.6K
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last updated: Aug 21, 2026

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Install

$ npx -y skills add github/awesome-copilot --skill gtm-ai-gtm --agent claude-code

Installs into .claude/skills of the current project.

Files

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

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:

SKILL.md on GitHub