ai-gtm

Name: ai-gtm
Author: beingsmit

39stars7forks

How to install ai-gtm?

beingsmit/technical-product-gtm/ai-gtm

$npx -y skills add beingsmit/technical-product-gtm --skill ai-gtm

Installs into the current project.

Summary

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.

Files · 1

View on GitHub

SKILL.md

1	# AI Product GTM
2
3	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.
4
5	## When to Use
6
7	Triggers:
8	- "How do we position this AI product?"
9	- "Buyers say they're worried about AI breaking production"
10	- "Should we call it autonomous or copilot?"
11	- "How do we price AI when usage varies 10x by customer?"
12	- "Enterprise security passed but ops rejected us — why?"
13
14	Context:
15	- AI agent platforms (coding, support, ops)
16	- LLM-based applications
17	- Autonomous tools that do things (not just suggest)
18	- AI infrastructure
19	- Anything where the AI makes decisions
20
21	---
22
23	## Core Frameworks
24
25	### 1. The Real Enterprise AI Objection (It's Not What You Think)
26
27	What I Learned Selling Autonomous AI Agents:
28
29	Three months in, enterprise security reviews were passing fast. Good sign, right? Then the pattern emerged: security approved, but operations rejected us.
30
31	The objection wasn't "will the AI break production?" — they assumed it would break production eventually. The real question was:
32
33	"Who's responsible when the agent does something wrong?"
34
35	Not "do we trust the agent?" — "do we trust our team to handle this?"
36
37	Why This Matters:
38
39	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?
40
41	Framework: The Accountability Cascade
42
43	Before deploying AI agents, enterprises need clear answers:
44
45	1. L1 Response: Who monitors the agent? (24/7 ops team, or dev team on-call?)
46	2. L2 Escalation: When agent action fails, who debugs? (Agent team, or product team?)
47	3. L3 Ownership: When something breaks badly, who owns customer communication?
48
49	If you can't answer all three, they won't buy. Doesn't matter how good your AI is.
50
51	How This Changes Your Sales Process:
52
53	Old approach:
54	- Demo the AI
55	- Show accuracy metrics
56	- Talk about ROI
57
58	New approach:
59	- Demo the AI
60	- Show the failure modes explicitly
61	- Ask: "Who on your team would handle this scenario?"
62	- Walk through their incident response process
63	- Map AI failures to their existing runbooks
64
65	The Qualification Question:
66
67	"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?"
68
69	If they can't answer, they're not ready. Pause the deal and help them build the process first.
70
71	Common Mistake:
72
73	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?"
74
75	Pattern I've Seen Work:
76
77	Companies that succeed with AI agents already have:
78	- On-call rotations for production systems
79	- Incident response playbooks
80	- Blameless postmortem culture
81	- Clear escalation paths
82
83	Companies that struggle:
84	- Manual deployment processes
85	- Hero culture ("Steve fixes everything")
86	- No formal incident response
87	- Blame-focused culture
88
89	Decision Criteria:
90
91	Before demoing autonomous AI to enterprises, ask yourself: "If this breaks their production, who on their team owns the fix?" If you can't answer, they can't buy.
92
93	---
94
95	### 2. Copilot vs Agent vs Teammate (Three Different GTM Motions)
96
97	The Positioning Trap:
98
99	Early enterprise conversations, we positioned as "autonomous AI agent." Buyers flinched. One word change — "autonomous" → "AI teammate" — and deal progression improved measurably.
100
101	Why? Word choice shapes buyer psychology.
102
103	The Three Framings:
104
105	1. Copilot (Safest, Lowest Value)
106	- What it means: AI suggests, human decides every time
107	- Buyer psychology: Feels safe, non-threatening
108	- GTM motion: Developer adoption, bottoms-up
109	- Use case: Code completion, writing assistance, search
110	- Objection: "Is this worth paying for?" (low perceived value)
111
112	2. Agent (Scariest, Highest Value)
113	- What it means: AI acts autonomously, human reviews periodically
114	- Buyer psychology: Scary, implies replacing humans
115	- GTM motion: Enterprise sales, top-down
116	- Use case: Batch processing, automated workflows, ops
117	- Objection: "What if it breaks production?" (accountability fear)
118
119	3. Teammate (Sweet Spot)
120	- What it means: AI and human collaborate, split the work
121	- Buyer psychology: Partnership, not replacement
122	- GTM motion: Hybrid (dev adoption + manager approval)
123	- Use case: Most AI

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Type

ai-gtm

39stars7forks

View on GitHub

How to install ai-gtm?

beingsmit/technical-product-gtm/ai-gtm

$npx -y skills add beingsmit/technical-product-gtm --skill ai-gtm

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# AI Product GTM
2
3	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.
4
5	## When to Use
6
7	Triggers:
8	- "How do we position this AI product?"
9	- "Buyers say they're worried about AI breaking production"
10	- "Should we call it autonomous or copilot?"
11	- "How do we price AI when usage varies 10x by customer?"
12	- "Enterprise security passed but ops rejected us — why?"
13
14	Context:
15	- AI agent platforms (coding, support, ops)
16	- LLM-based applications
17	- Autonomous tools that do things (not just suggest)
18	- AI infrastructure
19	- Anything where the AI makes decisions
20
21	---
22
23	## Core Frameworks
24
25	### 1. The Real Enterprise AI Objection (It's Not What You Think)
26
27	What I Learned Selling Autonomous AI Agents:
28
29	Three months in, enterprise security reviews were passing fast. Good sign, right? Then the pattern emerged: security approved, but operations rejected us.
30
31	The objection wasn't "will the AI break production?" — they assumed it would break production eventually. The real question was:
32
33	"Who's responsible when the agent does something wrong?"
34
35	Not "do we trust the agent?" — "do we trust our team to handle this?"
36
37	Why This Matters:
38
39	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?
40
41	Framework: The Accountability Cascade
42
43	Before deploying AI agents, enterprises need clear answers:
44
45	1. L1 Response: Who monitors the agent? (24/7 ops team, or dev team on-call?)
46	2. L2 Escalation: When agent action fails, who debugs? (Agent team, or product team?)
47	3. L3 Ownership: When something breaks badly, who owns customer communication?
48
49	If you can't answer all three, they won't buy. Doesn't matter how good your AI is.
50
51	How This Changes Your Sales Process:
52
53	Old approach:
54	- Demo the AI
55	- Show accuracy metrics
56	- Talk about ROI
57
58	New approach:
59	- Demo the AI
60	- Show the failure modes explicitly
61	- Ask: "Who on your team would handle this scenario?"
62	- Walk through their incident response process
63	- Map AI failures to their existing runbooks
64
65	The Qualification Question:
66
67	"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?"
68
69	If they can't answer, they're not ready. Pause the deal and help them build the process first.
70
71	Common Mistake:
72
73	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?"
74
75	Pattern I've Seen Work:
76
77	Companies that succeed with AI agents already have:
78	- On-call rotations for production systems
79	- Incident response playbooks
80	- Blameless postmortem culture
81	- Clear escalation paths
82
83	Companies that struggle:
84	- Manual deployment processes
85	- Hero culture ("Steve fixes everything")
86	- No formal incident response
87	- Blame-focused culture
88
89	Decision Criteria:
90
91	Before demoing autonomous AI to enterprises, ask yourself: "If this breaks their production, who on their team owns the fix?" If you can't answer, they can't buy.
92
93	---
94
95	### 2. Copilot vs Agent vs Teammate (Three Different GTM Motions)
96
97	The Positioning Trap:
98
99	Early enterprise conversations, we positioned as "autonomous AI agent." Buyers flinched. One word change — "autonomous" → "AI teammate" — and deal progression improved measurably.
100
101	Why? Word choice shapes buyer psychology.
102
103	The Three Framings:
104
105	1. Copilot (Safest, Lowest Value)
106	- What it means: AI suggests, human decides every time
107	- Buyer psychology: Feels safe, non-threatening
108	- GTM motion: Developer adoption, bottoms-up
109	- Use case: Code completion, writing assistance, search
110	- Objection: "Is this worth paying for?" (low perceived value)
111
112	2. Agent (Scariest, Highest Value)
113	- What it means: AI acts autonomously, human reviews periodically
114	- Buyer psychology: Scary, implies replacing humans
115	- GTM motion: Enterprise sales, top-down
116	- Use case: Batch processing, automated workflows, ops
117	- Objection: "What if it breaks production?" (accountability fear)
118
119	3. Teammate (Sweet Spot)
120	- What it means: AI and human collaborate, split the work
121	- Buyer psychology: Partnership, not replacement
122	- GTM motion: Hybrid (dev adoption + manager approval)
123	- Use case: Most AI

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6 picks

Type