# GTM AI GTM Human Guide

## What This Is For
Go-to-market strategy for AI products. It gives the agent a clearer input/output frame for go-to-market work: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the GTM AI GTM agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for GTM AI GTM.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the GTM AI GTM guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- "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?"
- AI agent platforms (coding, support, ops)
- Autonomous tools that *do* things (not just suggest)
- Anything where the AI makes decisions
- **L1 Response**: Who monitors the agent? (24/7 ops team, or dev team on-call?)
- **L2 Escalation**: When agent action fails, who debugs? (Agent team, or product team?)
- **L3 Ownership**: When something breaks badly, who owns customer communication?
- Show the *failure modes* explicitly

## Decision Points And Nuance
The original skill emphasizes: When to Use, Core Frameworks, The Real Enterprise AI Objection (It's Not What You Think), Copilot vs Agent vs Teammate (Three Different GTM Motions), The AI Pricing Problem (When Usage Varies 10x), The AI Trust Ladder (From Someone Who Climbed It), The Enterprise AI Demo (Show Failure, Not Just Success), The "Who Owns This?" Objection Handler, The AI Positioning Trap (Fighting Asymmetric Wars), Ceiling Moment Qualification (Finding High-Intent AI Buyers).

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- Enterprises don't deploy MVPs. They need proof you won't fall over at 1000 users.
- Cherry-picking examples where AI is 100% accurate. Buyers know real-world data is messy. If you don't show messiness, they assume you're hiding it.
- Immature buyers say: "Can you make it never fail?"
- You're competing in the AI agent space. Every competitor's homepage says the same thing: "Automate [workflow] with AI." Your differentiation requires explaining complex technical benchmarks that buyers don't understand.
- Feature advantages that don't last:
- For every positioning claim, ask: Can a competitor copy this with a single product sprint? If yes, it's not defensible. Don't build your GTM on it.
- Acknowledge the incumbent has value (don't trash-talk)
- Buyers know real-world data is messy. If you don't show failures, they assume you're hiding them.

## Copy-And-Paste Prompt
```text
Use the GTM AI GTM human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the github/awesome-copilot skill entry for `gtm-ai-gtm`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# 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?"**

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.

**How This Changes Your Sales Process:**

**Old approach:**
- Demo the AI
- Show accuracy metrics
- Talk about ROI

**New approach:**
- Demo the AI
- Show the *failure modes* explicitly
- Ask: "Who on your team would handle this scenario?"
- Walk through their incident response process
- Map AI failures to their existing runbooks

**The Qualification Question:**

"Walk me through what happens when the agent takes an action that breaks a workflow. Who gets alerted? Who investigates? Who decides whether to roll back or fix forward?"

If they can't answer, they're not ready. Pause the deal and help them build the process first.

**Common Mistake:**

Treating this as a *product* objection ("we'll make the AI more accurate"). It's an *organizational* objection. More accuracy doesn't solve "who owns this at 2am?"

**Pattern I've Seen Work:**

Companies that succeed with AI agents already have:
- On-call rotations for production systems
- Incident response playbooks
- Blameless postmortem culture