StackSwap · AI-Native · GTM stack audit

AI-Native GTM Stack Audit

AI-native teams run the leanest stacks — but still pay for legacy tools out of habit where an AI-native option already covers the job, and for overlapping automation platforms acquired as the team experimented.

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The ai-native peer median

Modeled from the ai-native cohort within 100,000 stacks:

Median tools
8
Spend / employee
$2,422
per GTM employee / yr
AI-native score
66
Overlaps
1
categories
Recoverable / yr
$0

At $2,422 per GTM employee, the median ai-native team carries 1 overlapping categories — concentrated in the patterns below — and sits at 66/100 on AI-native coverage, leaving room to move legacy tools to AI-native replacements. Full cross-cohort tables: GTM Stack Benchmark.

Where ai-native stacks overpay

Legacy holdovers

A legacy tool kept "because it works" where an AI-native replacement covers the same job at lower cost and higher leverage.

Point tools an agent replaces

Several single-purpose tools whose combined job a single AI agent or workflow now does end to end.

Overlapping automation platforms

Zapier plus a second automation/agent platform plus in-tool automations — three ways to run the same workflow.

How to run the audit

The method is the same four layers for every motion — overlap, spend per employee, AI-native coverage, and handoffs — applied to your specific tools. See the full GTM stack audit method and the audit checklist, or let the tool run all four against your stack automatically.

Frequently asked questions

What does a ai-native GTM stack audit check?
It runs the four standard audit layers — overlap, spend per GTM employee, AI-native coverage, and handoffs — weighted to where ai-native teams overpay. The median ai-native stack runs 8 tools with 1 overlapping categories and $0 in modeled annual recoverable spend.
How much do ai-native teams spend on GTM software?
The median ai-native team spends $2,422 per GTM employee per year, with $0 in modeled annual recoverable spend — spend per employee is the cleanest way to compare across teams, since raw total just tracks size.
What are the most common ai-native tool overlaps?
legacy holdovers, point tools an agent replaces, overlapping automation platforms. A legacy tool kept "because it works" where an AI-native replacement covers the same job at lower cost and higher leverage.

Audit another motion

Sales-Led audit →Product-Led (PLG) audit →Marketing-Led audit →Founder-Led / Early-Stage audit →Enterprise RevOps audit →

Medians modeled from 100,000 synthetic GTM stacks run through the same scoring engine that powers StackScan. Methodology.

Run your ai-native stack audit

Free, modeled in about a minute, scored against your ai-native peer cohort. No signup to see the results.

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