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Stand up an AI-native GTM motion

You are bolting AI onto outbound and you do not have four weeks to build it step by step. This is the condensed version of the AI-Native GTM Series: the same score, enrich, watch, and learn moves run in one sitting instead of across four episodes.

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[ BUILT GTM ]

Stand up an AI-native GTM motion

Solvethe problem worth solving
Problem · 01GTM Strategy

You are bolting AI onto outbound and you do not have four weeks to build it step by step.

This is the condensed version of the AI-Native GTM Series: the same score, enrich, watch, and learn moves run in one sitting instead of across four episodes.

Stackthe workflow, box by box
Salesforce01
Turn a pile of accounts into a stack rank with a reason on every row.
Score and stack-rank the market, channel-fair.
Powers up with
AI
Deepline02
Fill the gaps: email, phone, LinkedIn, title.
Fill the gaps so the score has data to run on.
Powers up with
AI
Claude03
Turn a pile of accounts into a stack rank with a reason on every row.
Re-run the score on the newly-enriched Tier 1, so the rank reflects real data instead of the pre-enrichment guess.
Connect to analyze
AI
Amplitude04
Read how an account actually uses you.
Turn usage into a signal a rep can act on.
Powers up with
AI
Salesforce05
Watch your analysts, and turn every miss into a lesson.
Watch the motion and turn every miss into a lesson.
Powers up with
AI
Claude06
Turn feedback and outcomes into proposed skill fixes you approve.
Close the loop so the system improves itself.
Connect to analyze
AI + human
Splithow control passes: agents branch left, humans right
HUMAN + AI, IN THE LOOP
How control passes.
AgentsHuman
Agent01
Turn a pile of accounts into a stack rank with a reason on every row.
Salesforce runs it.
Agent02
Fill the gaps: email, phone, LinkedIn, title.
Deepline runs it.
Agent03
Turn a pile of accounts into a stack rank with a reason on every row.
Claude runs it.
Agent04
Read how an account actually uses you.
Amplitude runs it.
Agent05
Watch your analysts, and turn every miss into a lesson.
Salesforce runs it.
AI + human06
Turn feedback and outcomes into proposed skill fixes you approve.
AI drafts, a human checks.
The skills run the work. You stay in the loop on the calls that need judgment.
Outputwhat the workflow produces
OUTPUT · 01

In one pass

OUTPUT · 02

Not four episodes: a scored market

OUTPUT · 03

Enriched accounts

OUTPUT · 04

A fresh re-score on real data

Outcomethe result it drives
OUTCOME · 01

The 7th analyst

The QA agent that watches the whole motion.

OUTCOME · 02

A 33-cent bake-off

The answer was a signal-ownership waterfall, not one vendor.

OUTCOME · 03

The product channel

Usage turned into the best-converting channel.

Built GTM
── 01 / WHY RUN IT

You keep bolting an AI tool onto the same outbound motion and the number does not move, because a scored list, a usage read, and a QA layer added separately never talk to each other. Meanwhile the full 4-week AI-Native GTM Series feels like too much to commit to before you have even seen the shape of it.

RUN THIS AND YOU CAN
✓One pass that scores the whole market, channel-fair
✓Enrichment that gives the score real data to run on, not a guess
✓A product-usage read turned into a signal a rep can act on
✓A QA and evolution layer that watches the motion and proposes its own fixes
── 02 / THE RUN, STEP BY STEP

The workflow that solves it, one step at a time.

Each step is the plain question you are already asking. The skills answer them in order, one handing its receipt to the next.

1✓ TESTED
Turn a pile of accounts into a stack rank with a reason on every row.

Fair across channels, blank fields never scored as zero, with a why behind each account's score.

YOU GETThe three score modes, the gates and escalators, and per-account reasoning.
POWER IT UPSalesforceHubSpotAttioPipedriveDynamicsClay
OPEN THE SKILL →
2✓ TESTED
Fill the gaps: email, phone, LinkedIn, title.

Give it a name and company, or a domain, and get contacts or firmographics back. Waterfalls across your tools.

YOU GETThe waterfall logic across your tools and the fields it fills.
POWER IT UPClayApolloZoomInfoClearbitCognismCommon Room
OPEN THE SKILL →
3✓ TESTED
Turn a pile of accounts into a stack rank with a reason on every row.

Fair across channels, blank fields never scored as zero, with a why behind each account's score.

YOU GETThe three score modes, the gates and escalators, and per-account reasoning.
POWER IT UPSalesforceHubSpotAttioPipedriveDynamicsClay
OPEN THE SKILL →
4✓ TESTED
Read how an account actually uses you.

Turns raw usage into per-capability adoption tiers, a 12-week trend on each, power-user IDs, and a ghost-active flag.

YOU GETThe adoption-tier rubric, the trend classifier, and the ghost-active check.
POWER IT UPAmplitudeMixpanelPostHogHeapPendoSegment
OPEN THE SKILL →
5✓ TESTED
Watch your analysts, and turn every miss into a lesson.

The check on your other analysts: it surfaces scoring blind spots, CRM hygiene gaps, and workflow drift, then hands back a weekly QA digest.

YOU GETThe six-part audit, the quality gates, and a weekly QA digest example.
POWER IT UPSalesforceHubSpotAttioPipedriveDynamicsNotion
OPEN THE SKILL →
6✓ TESTED
Turn feedback and outcomes into proposed skill fixes you approve.

Reads QA findings, feedback, and outcomes, then proposes concrete changes to your skills and scoring. You approve, nothing self-edits.

YOU GETThe feedback streams, the four change types, and an approval gate on every merge.
POWER IT UPClaudeChatGPTGeminiCopilotNotionGoogle Drive
OPEN THE SKILL →
WHERE YOU LAND

In one pass, not four episodes: a scored market, enriched accounts, a fresh re-score on real data, a product-usage read, a QA agent that watches it, and a loop that learns.

EVERY STEP, TESTED AND PASSED
EVAL RESULTPASS4/4 dimensions
HOW WE EVAL →
✓Skills resolve
✓Structure + proof
✓Example chat
✓Quality judge
  • All 6 chained skills resolve to real, scored skills.
  • Has a problem, an outcome, 2+ steps, and proof from a real build.
  • Ships an example chat showing the play run in Claude.
  • Reads in plain operator voice, grounded in a real build.
Last eval run 2026-08-05
DEEP EVAL · PASS
THE STANDARD →

We did not just check the structure. A judge read this play against the full text of every skill it chains (composition), then ran it on a real scenario end to end (simulated). It passed both. Test scenario: A RevOps lead wants an AI-native motion running today, not spread across a 4-week series.

── 03 / SEE IT RUN

Watch the skills answer in order.

Describe your situation in plain language. The right skill fires on its own and hands its receipt to the next.

EXAMPLE CHATPriya, RevOps Lead, running the loop in one sitting
P
We keep bolting AI onto the same outbound. I want a scored motion that runs itself and gets better, not another dashboard. Start by scoring the market.
S
Step 1· Icp Scoring
I scored and stack-ranked your full account list, fair across channels so inbound and outbound each get judged on their own signals. Every row carries a reason, and blanks are never scored as zero.
Receipt
2,400 accounts ranked into 4 tiers; 210 in Tier 1 with a why on each.
P
Half of Tier 1 is missing emails and firmographics. Fill it in.
S
Step 2· Enrichment Analyst
I waterfalled across your tools to fill the gaps: email, phone, LinkedIn, title on the contacts, and headcount plus stack on the accounts. That data is ready for the score to run on again.
Receipt
Filled gaps on 188 of 210 Tier 1 accounts, adding 640 verified contact emails.
P
Good, now put that back through the score so Tier 1 actually reflects it.
S
Step 3· Icp Scoring
Re-ran the score on the enriched data. A few accounts moved tiers now that the gaps are filled instead of guessed, so Tier 1 is current, not stale.
Receipt
Re-score moved 14 accounts into Tier 1 and 6 out, now that real firmographic and contact data replaced the blanks.
P
For the accounts already using us, tell me how they actually use the product so reps get a real signal.
S
Step 4· Product Usage Analyst
I turned raw usage into per-capability adoption tiers with a 12-week trend on each, named the power users, and flagged the ghost-active accounts that look busy but are not really adopting.
Receipt
32 accounts trending up and expansion-ready; 11 flagged ghost-active despite high login counts.
P
Now put a check on the whole thing so it does not quietly drift.
S
Step 5· Qa Agent
I set the QA agent to watch the motion: scoring blind spots, CRM hygiene gaps, and workflow drift. It runs the six-part audit and hands back a weekly digest instead of you finding the miss in the forecast.
Receipt
First audit caught 3 scoring blind spots, including a segment scored 20 points too low.
P
Close the loop. I want the misses to actually change the system.
S
Step 6· Evolution Agent
I wired the loop so QA findings and real outcomes turn into proposed fixes to your scoring and skills. Nothing self-edits. You approve each change, and the system tightens week over week.
Receipt
4 proposed fixes queued from week one, each with a rationale tied to data; the precision lift gets measured after approval, over a trailing window, not estimated up front.
── 04 / SEE IT IN PRODUCTION

Before you run it, I ran it for real.

This workflow is genericized from a real build. Here is the number it moved, and what I actually did.

THE BUILD · AI-NATIVE THESIS
The 7th Analyst
0.775 AUC
a won account outranks a lost one, on the model the loop rebuilt
1.5x lift
94% of the model Warm-plus accounts are wins, vs a 62% base rate
7 versions
each caught a flaw the last one shipped, by the loop not a customer
0 of 60
lost accounts that reached the top tier, the top of the funnel stays clean
WHAT I DID ›
01
Give every analyst an audit trail. Each agent logs what it decided and why, every override, every missing field, every enrichment miss. The exhaust of the system becomes its memory.
02
Build the seventh analyst to read them. A meta-analyst reads the other six's audit trails and turns each mistake into a training signal: a rule tightened, a gap flagged, a pattern learned.
03
Close the loop. The corrections feed back into the system, so the next run is better than the last. That is the difference between a tool and a system that improves itself.

AI-native is not how many agents you have. It is whether one of them watches the others and turns their mistakes into improvements. Build the analyst that reads the audit trail. That is the one that compounds.

Dig into the full build →
THE BUILD · DATA VENDOR STRATEGY
The Enrichment Bake-Off
>1 in 3
of pipeline dollars booked, up from about 1 in 5
2x+
new-business bookings per AE
$0.33
the bake-off that started it
Cell phone
the scarce signal it all turned on
WHAT I DID ›
01
Test both vendors on the hard signal. I ran both vendors against the same accounts for about thirty-three cents total, scoring them on the field that actually mattered: a valid, reachable cell phone, not just an email.
02
Map coverage, not a winner. Neither vendor won. They covered different halves, so on any given contact one might have the cell and the other the email. The right answer was a waterfall that ran each for what it owned, then verified, not a single pick.
03
Find the cheapest predictive question. The most predictive signal was a free MX-record lookup. A simple domain check instantly flagged which accounts ran our best-fit email stack, and it cost nothing. The expensive data was not the valuable data.

Do not run a vendor bake-off to crown a winner. Run it to map what each one covers, then rank the stack by cost and orchestrate it as one workflow: cheapest predictive field first, the expensive vendor last. You get the same data for a fraction of the spend, a model that scales, and contact data good enough that a dollar of pipeline books better than 1 in 3 instead of 1 in 5.

Dig into the full build →
THE BUILD · PRODUCT-LED MOTION
The Product Channel
~47%
qualified win rate, the highest of any channel
$1M+
in sales-qualified pipeline, built from zero
Best GRR + NRR
of any channel, it retained and expanded best too
414% of plan
the pipeline the channel actually delivered
WHAT I DID ›
01
Map the events first. I mapped what users actually did in the product to what it meant for a sale. Not every event, the ones that predicted buying.
02
Centralize the signal. I centralized our Snowflake product data with enrichment and signal data by implementing Common Room and Deepline, so a usage spike at an account arrived already enriched, already scored, already routed.
03
Build the motion on top. The signals worth acting on, the play that worked them, and the routing that put a real user in front of a real rep with a real reason to talk.

If you sell a product people can use before they buy, you already have a product-led channel. The question is never whether it exists. It is whether your usage data has been turned into context a rep can act on. Map the events first. The motion comes after.

Dig into the full build →
05 / INSTALL THE WHOLE PLAYBOOK

One plugin. 6 skills, bundled.

The whole playbook installs as a single plugin in Claude Code, no copy-pasting 6 times. Or grab any single step above. It runs on what you paste; connect your stack to go live.

OR KICK IT OFF IN CHAT
“Here is my situation: [describe your workflow or account in a sentence or two]. Walk me through Stand up an AI-native GTM motion step by step, and run each skill as we go.”
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