gtm-ai-gtm
Go-to-market strategy for AI products. Use when positioning AI products, handling "who is responsible when it breaks" objections, pricing variable-cost AI, choosing between copilot/agent/teammate framing, or selling autonomous tools into enterprises.
Profile →npx skills add github/awesome-copilot --skill gtm-ai-gtm --agent copilot
Same command for any agent — swap --agent for claude-code, codex, cursor.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
GTM-ai-gtm provides go-to-market guidance for AI products, emphasizing enterprise-oriented framing, handling ownership and failure scenarios, pricing models for variable usage, and positioning between copilot, agent, and teammate. It targets selling autonomous tools into enterprises and patterns for addressing operational readiness, incident handling, and trust-building.
How it works
The skill outlines concrete buyer objections and the corresponding sales and product framing:
- It shifts discussions from pure accuracy to accountability and incident handling (L1/L2/L3 ownership) so buyers know who fixes issues and communicates with customers.
- It prescribes three framings for GTM motions: Copilot (safe, developer-focused), Agent (autonomous, high value, enterprise), Teammate (collaborative, enterprise-friendly), with language and use-case guidance to steer conversations.
- It details pricing approaches for variable AI usage, recommending a hybrid model: Base fee plus a variable fee tied to value, plus guidance on evaluating per-call versus per-outcome economics.
- It describes a trust ladder strategy: transparency (model cards, security whitepaper, explainability), in-demo control (kill switches, confidence scores), performance proofs, and enterprise readiness (SOC 2, GDPR).
- It provides a structured demo approach that showcases failure and recovery, with emphasis on human review and owned outcomes to build credibility.
- It includes an objection handler for “Who owns this?” and a diagnostic approach to assess buyer readiness and incident response maturity before expansion.
When to use it
Use when positioning AI products, addressing concerns about production failures, choosing framing between Copilot/Agent/Teammate, pricing variable-cost AI, or selling autonomous tools into enterprises. Triggers include questions about positioning, ownership during failures, pricing, and enterprise security concerns.
What it can touch
The skill references tools and concepts such as:
- Framing terms: Copilot, Agent, Teammate
- Pricing models: seat-based, usage-based, outcome-based, and hybrid structures
- Operational processes: on-call rotations, incident response playbooks, blameless postmortems, escalation paths
- Documentation and demos: model cards, security whitepapers, explainability docs, safety mechanisms, rollbacks
Caveats
The material presents patterns and recommendations rather than guarantees. It emphasizes operational readiness and buyer maturity; if a buyer demands 100% accuracy or a fully guaranteed no-failure setup, it advises stepping back and focusing on incident handling and governance. It cautions against relying solely on accuracy improvements to close deals and against positioning solely on temporary feature advantages that competitors can copy.
# 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: **"Who's responsible when the agent does something wrong?"**
- When to Use
- Core Frameworks
- 1. The Real Enterprise AI Objection (It's Not What You Think)
- 2. Copilot vs Agent vs Teammate (Three Different GTM Motions)
- 3. The AI Pricing Problem (When Usage Varies 10x)
- 4. The AI Trust Ladder (From Someone Who Climbed It)
- 5. The Enterprise AI Demo (Show Failure, Not Just Success)
- 6. The "Who Owns This?" Objection Handler
- 7. The AI Positioning Trap (Fighting Asymmetric Wars)
- 8. Ceiling Moment Qualification (Finding High-Intent AI Buyers)
- Decision Trees
- Which Positioning Should I Use?
- Which Pricing Model Should I Use?
- Is This Buyer Ready for AI Agents?
What does the gtm-ai-gtm skill do?
Go-to-market strategy for AI products. Use when positioning AI products, handling "who is responsible when it breaks" objections, pricing variable-cost AI, choosing between copilot/agent/teammate framing, or selling autonomous tools into enterprises.
How do I install it?
Run `npx skills add github/awesome-copilot --skill gtm-ai-gtm --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From github/awesome-copilot, a repository with 37,432 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.