gtm-ai-gtm

Provides go-to-market frameworks for positioning, pricing, and selling AI products to enterprises.

38.5k|4.9k|Updated Jun 11, 2025
One-click install
npx skills add https://github.com/github/awesome-copilot --skill gtm-ai-gtm
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: gtm-ai-gtm
Source: https://github.com/github/awesome-copilot/tree/main/skills/gtm-ai-gtm
Command: npx skills add https://github.com/github/awesome-copilot --skill gtm-ai-gtm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Selling AI products into enterprises fails when teams misread buyer objections, choose scary positioning like "autonomous", or misprice variable-usage AI. This Skill provides field-tested GTM frameworks for AI products, covering objection handling, positioning, pricing, demos, and buyer qualification.

Core Features & Use Cases

  • Positioning Frameworks: Choose between copilot, agent, and teammate framings with decision trees and specific language guidance for enterprise buyers.
  • Pricing Models: Compare seat-based, usage-based, outcome-based, and hybrid pricing with rules like pricing variable cost at 20-30% of the customer's alternative cost.
  • Objection Handling & Demos: Address the "who is responsible when AI breaks" objection with the Accountability Cascade, and structure demos that show AI failure and recovery to build trust.
  • Use Case: A startup selling an autonomous coding agent keeps stalling in enterprise deals. Use this Skill to reposition from "autonomous agent" to "AI teammate", restructure the demo to show failure recovery, and qualify buyers on incident response maturity.

Quick Start

Ask the AI to help position your AI product for enterprise buyers using the copilot vs agent vs teammate framework and draft objection-handling responses.

Frequently Asked Questions about gtm-ai-gtm

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I position an AI product for enterprise buyers?

Choose between copilot, agent, and teammate framings based on autonomy level and audience. Enterprises respond better to "teammate" language that emphasizes collaboration and control, while "autonomous" tends to trigger replacement fears and stall deals.

How should I price AI products with variable usage?

Use a hybrid model combining a base fee covering fixed costs with a variable fee scaled to value. Price the variable component at roughly 20-30% of the customer's alternative cost so high-usage customers are not punished.

What is the real enterprise objection to AI agents?

The real objection is not whether the AI will break production but who is responsible when it does. Buyers need clear answers on monitoring, escalation, and ownership before they will purchase autonomous tools.

Should AI demos show failures or only successes?

Demos should show the AI encountering an error or uncertainty and recovering, followed by human review and override. Showing failure modes builds more trust than cherry-picked perfect results, which buyers assume hide real-world messiness.

When is a buyer not ready for AI agents?

Buyers lacking incident response processes, on-call rotations, and blameless postmortem cultures are not ready for autonomous AI. If they demand 100% accuracy, pause the deal and help them build operational maturity first.