Sales & GTM for AI Scale-ups · Andrew Wesbecher

00 · Why this exists

ChatGPT changed everything. The math held.

I have carried a bag and built GTM engines for 26 years: TIBCO, Meraki, ThousandEyes, Lacework, Contrast, Traceable. The last three years, I have run AI and AI security scale-ups as a fractional CRO and advisor, with an AI-first GTM stack doing the ops work.

This playbook is what survived the collision: the enterprise foundations that still close seven-figure deals, and the modern layer that AI-era buyers now expect.

Nothing in it is aspirational. It is what I run now.

26

Years enterprise GTM

10

GTM plans, AI cos

4

AI security clients

Who this is for

Founders and boards scaling $0 → $20M ARR. Every framework here has shipped inside a real plan.

01 · The shift

Your buyer moved. Three years ago.

01Research happens in the answer box. Buyers ask ChatGPT, Claude and Perplexity who the leaders are before your SDR ever calls.
02Adoption starts at the practitioner. Engineers install the OSS and form opinions first. The exec meeting only ratifies it.
03Proof means production. A slideware demo earns nothing. Buyers expect a POV in a prod-like environment inside days, not a quarter.
04Procurement grew an AI checklist. Model risk, data boundaries, agent governance. Meet it in week 2, or lose to it in week 11.
05Categories form in months. A model release can commoditize your moat mid-quarter. Speed of iteration is the durable advantage.

The job

Get into the model's answer, the practitioner's terminal, and production, in that order. The rest is execution.

The GTM delta, in one table.

Same disciplines. Different mechanics. The right-hand column is where AI-native companies actually operate.

Discipline	The old playbook	The 2026 playbook
Demand capture	SEO, SEM, gated PDFs	AEO: be the answer the model gives. Ungated technical depth
Entry point	Top-down exec outreach	Bottoms-up practitioner adoption; exec alignment engineered later
Proof	Custom demo, 90-day pilot	Self-serve trial + scoped POV with signed success criteria
Technical selling	SE demos over Zoom	FDE builds alongside the customer, in their environment
GTM operations	SDR armies and ops headcount	Headless agent workflows; humans on judgment and calls
Content	Quarterly gated whitepaper	Continuous technical content practitioners actually share

The tell

The left column still works in legacy categories. In AI, it reads as a vendor who has not used their own product.

What 26 years says still wins.

Six disciplines that predate the model era and survive it intact.

01

MEDDPICC

Metrics, economic buyer, decision and paper process, pain, champion, competition. Deals get inspected, not narrated.

02

Command of the Message

Value framing the economic buyer signs, and discovery that earns the right to deliver it. AI buyers still buy outcomes.

03

Manufactured pipeline

PG quotas next to ARR quotas, activity math, coverage inspected weekly. Hope is not a pipeline source. It never was.

04

POV discipline

Pre-agreed success criteria, frozen scope, readout to the economic buyer. The POV got faster, never optional.

05

Coaching on cadence

Call review, scorecards, 60-day ramp gates on every new hire. AI does not fix a rep who cannot run discovery.

06

Forecast as arithmetic

Stage exit criteria in CRM. Commit, upside, closest-to-pin. Judgment sits on top of math, never instead of it.

The point

AI changed the how. It did not repeal the math. Every module after this one sits on top of these six.

02 · Modern motions

Two engines, one capacity model.

Enterprise and velocity run on different math. They land in the same forecast.

Field engine · Enterprise + Growth

ACV $80-400K
by tier; 90-120 day cycles
MEDDPICC + POV
run through the nine stages below
Anchors
meeting → POV 4-5% · POV → win 75-85%
Staffing
AE + BDR pods, SE at 1:3, FDE on strategic

Velocity engine · PLG + Community

$30-60K entry
AI-native and mid-market; 30-45 days
PQL-gated
seats + prod-adjacent usage + findings
Anchors
PQL → win 15-25% · reverse trials, 14 days
Staffing
one velocity pod works the whole flow

Rule

One capacity model, one forecast. Separate staffing, separate math, one number. Module 05 builds the model.

The nine stages have not changed.

Exit criteria, not activities. A stage advances when the criterion is met, not when the rep feels good.

#	Stage	Close %	Exit criteria	Advance →
0	Qualify	0%	ICP fit, BANT, a real initiative	50-55%
1	Discovery	10%	Pain quantified, champion identified	~30%
2	Scoping	20%	Technical fit, budget confirmed, POV criteria drafted	~30%
3	EB Alignment	35%	Economic buyer met, reverse timeline agreed	~90%
4	POV	50%	Success criteria signed, scope frozen	~80%
5	Win the Decision	75%	POV readout to the EB, verbal to proceed	~90%
6	Negotiate & Close	90%	Proposal, mutual action plan, paper process mapped	~95%
7	Closing	99%	Signatures in motion	100%
8	Closed Won	100%	Order form executed	–

Stage 4 is shaded because that is where the deal gets decided.

3-4%

Meeting → win

Nothing structural changed. The POV now runs in days, not weeks, so the whole table spins faster.

The FDE motion: selling by building.

The forward-deployed engineer is a sales motion with an engineering cost structure. Scope it like one.

What it is, when to run it

Forward-deployed engineers embed with the customer, build the integration in their environment, and map expansion. The build is the proof, and the customer keeps it.

Deploy on agentic or complex products, design-partner phases, and lighthouse logos where the demo cannot carry the sale. Never on a deal an SE can close alone.

Palantir normalized it. OpenAI and Anthropic industrialized it. AI-native buyers now expect a builder in the room, and they can tell inside an hour if you sent one.

The rules that keep it a sales motion

·Scope FDE time like a POV: success criteria, exit date, EB readout
·FDE cost is CAC, not services revenue. Watch the margin line
·1 FDE per 2-3 strategic deals in flight; classic SE 1:3 for growth
·FDE builds twice, product ships once. Weekly roadmap loop
·Exit criteria: repeatability. Without it you built a consultancy

Scope

Like a POV

criteria, exit date, EB readout

Cost

CAC, not services

watch the margin line

Exit

Repeatability

without it you built a consultancy

Bottoms-up and top-down, same account.

The practitioner installs it. The economic buyer contracts it. Four steps, in order.

01Practitioner installs. OSS or free tier, no gate, generous limits. Treat the giveaway as marketing spend, not lost revenue.
02Champion forms. Usage becomes evidence: findings, saved hours, incidents avoided. The AI stack spots the signal.
03Platform team scopes a POV. Success criteria signed in writing, not implied on a call. Economic buyer aligned on a reverse timeline from go-live.
04The EB contracts the platform. Land $50-120K. Security review done once at the platform, then inherited by every team after.

Expand vectors

Workloads → environments → modules → teams. Expansion runs on triggers, not on an account manager's mood. NRR 105-110% landing year, 115-120% at scale.

Pricing architecture

Value metric: per governed workload, not per seat

Floors by tier; exceptions are CEO-only

Annual prepay default; multi-year needs cash up front

Free tier generous; enterprise buys governance

The point

Free adoption is the demand gen line item. The contract is signed one floor up, by someone who never touched the product.

Qualify the company, then the committee.

Five company prerequisites. Three buying rings. Nobody prospects outside them.

ICP prerequisites. All five, or it is not ICP.

01AI agents or LLM apps in production, or within two quarters
02A named owner of the AI platform on the org chart
03Security budget with an AI line item, this fiscal year
04A compliance driver: EU AI Act, SOC 2, model risk management
05Meaningful model and inference spend, growing

1-2 people

Economic

CISO, with the CFO co-signing. Buys risk reduction and audit readiness

3-6 people

Product decision

VP AI Platform · Dir AI Infra. Owns the platform your product lands on

8-15 people

Evaluators

AI + security engineers · MLOps. Daily users. Opinions form in the free tier. Scored weekly by the AI stack. Nobody prospects off-list.

Primary axis

VP AI Platform → Dir AI Infra → AI Engineers. Sell where the pain lives. Contract where the budget lives. The POV bridges the two.

03 · The AI-native engine

The stack I pay for but never log into.

Every tool below is run by Claude, through official MCPs or Claude in Chrome. I prompt; the stack executes.

Know · Data, research, record

Attio
CRM + call recording. System of record

Clay + Apollo
Enrichment, ICP + persona sourcing

Parallel
Account research + web scraping

AthenaHQ
AEO: visibility in AI answers

One interface: a prompt

Claude

The control plane

Official MCPs · Claude in Chrome
Cowork agents · skills · projects

Slack + CRM
Where insights and coaching land

Act · Execution channels

Origami + Instantly
Outbound email sequencing

HeyReach
LinkedIn outbound campaigns

Nooks
AI autodialer + call sessions

AgentMail
Agent-owned inbox: inbound SDR

The point

Ten paid tools. Zero logins. A human approves every external send. Everything else runs headless.

A prompt is the new login.

Four prompts I actually run. Each one ends in a system I never opened.

“Move Acme to Stage 3 in Attio, log the EB meeting, set next steps.”

→CRM updated, tasks created. Attio never opened.

“Find 50 Series B AI companies hiring security. Enrich in Clay, draft N=1 copy.”

→List, enrichment and copy live in Origami within the hour.

“Launch the HeyReach campaign for the conference follow-up list.”

→LinkedIn touches running by lunch, replies triaged back to me.

“Parse today's call recordings. Coach the team in #sales, log notes to CRM.”

→Every recording lands in a Claude project; insights hit Slack.

Always on, no prompt needed

Meeting intelligence. Every call parsed. Coaching to Slack, notes and next steps to CRM.

Inbound agent. Claude plus AgentMail: replies in minutes, qualifies, books the meeting.

Copy engine. Cadence copy drafted by Claude skills, tuned on what converts.

The operating model

Claude is the interface. The stack is the execution layer. One operator runs what used to take a pod.

The requirement

An official MCP, or Claude in Chrome. That covers every tool above, today. No custom integration work, no engineering ticket.

AEO is the new demand capture.

Buyers ask an assistant before they run a search. Five moves, four measures.

01Entity-clean docs and llms.txt. Make the product legible to models: crisp category language, structured docs, consistent naming.
02Comparison and alternatives pages. Answer the questions buyers actually ask assistants: X vs Y, best tools for Z.
03Community surface. Reddit, HN, Stack Overflow, GitHub. Models source opinions where practitioners argue.
04Review corpora. G2 and peer reviews are retrieval fodder. A 30-review gap is a demand-capture gap.
05Ungated technical depth. Models cite what they can read. Every gated PDF is invisible to the answer box.

Measure it

Monthly share-of-answer audits across ChatGPT, Claude and Perplexity

AI-referral sessions tracked as their own channel

Branded-query lift measured after content ships

Percent of inbound that says an assistant recommended you

The shift

Rank #1 on Google and still lose the deal. The model handed your competitor a buyer who never searched.

The headless workflow catalog.

Eight jobs that used to be headcount. Every external send still passes a human.

Workflow	Stack	Cadence	Human gate
Signal sensing: funding, hiring, tech adoption	Claude Cowork	Daily	None: read-only
Enrichment waterfall + account scoring	Clay + Apollo	On signal	None
Outbound cadences, drafted N=1	Claude → Instantly	Daily	Every send approved
LinkedIn campaigns + reply triage	Claude → HeyReach	Daily	Every send approved
CRM hygiene + stage enforcement	Attio / Salesforce	Nightly	Exceptions flagged
AEO, SEM and content pipeline	Claude Cowork	Weekly	Publish approved
Event ops: lists, pre-books, follow-ups	Claude + CRM	Per event	Sends approved
Win/loss tags + forecast prep	Claude + CRM	Weekly	CRO judgment

The hire

One GTM engineer runs this entire table. That role replaces the ops admin as the first RevOps seat, by roughly $3M ARR.

What the engine buys you.

Not fewer people. The same people, spending their hours where judgment actually pays.

100%

Account coverage

Every tiered account researched continuously, not the top 20%.

Under 5 min

Speed to lead, 24/7

PQLs and inbound routed, researched and answered around the clock.

~3 FTE

SDR capacity added

Without the headcount. Pods spend their hours in live conversations.

15 → 2 min

Research time

Brief, angle and opening line waiting in the CRM before the rep dials.

N=1

Personalization

Copy drafted per account from live signals, never from merge tokens.

Nightly

CRM hygiene runs

Stages, next steps and close dates enforced before the forecast call.

Not a concept

My daily driver.

This is the production stack I run across portfolio companies today. Claude Cowork orchestrates Attio, Clay, Apollo, HeyReach and Instantly on headless daily jobs, with human-approved sends.

This playbook was researched, written and designed by that same stack.

Attio · Clay · Apollo · HeyReach · Instantly · Nooks · AthenaHQ

The trade

Headcount stays. The hours move to judgment, relationships and closing.

04 · The scale path

Four stages, one question each.

You graduate on evidence, or you do not get to spend like the next stage.

How to read it

Stages are gates, not vibes. The next four panels are the playbook for each one.

Companion page How I read product-market fit. Three paths, an 18-statement diagnostic, and the same stage gate this module runs on.

Stage 1 · $0-1M: sell truth, not scale.

Nothing here scales. That is the point. You are buying evidence, not bookings.

01Founder sells. An FDE builds. Nobody hires a VP Sales yet. The founder cannot outsource learning what makes buyers move.
0210-20 design partners at real prices. Discounts trade for references and roadmap input, never for silence. Free pilots teach nothing about willingness to pay.
03Run pricing experiments on purpose. Three packaging tests before you claim repeatability. The metric that scales is tied to value delivered.
04Instrument everything from deal one. Win/loss on every deal, CRM discipline from the first opportunity, signal capture running even now.

The stage in one number

10-20

Design partners, real prices

Graduate when

Two net-new logos, sourced outside the network, live through a full renewal cycle.

Failure mode

Renting revenue from the founder's network and calling it repeatability.

The only proof

Two logos with no tie to the founder. Bought, deployed, renewed. Everything before that is anecdote.

05 · Sales capacity planning

Sales capacity planning, defined.

The model that turns a growth target into a hiring calendar, and a hiring calendar into a number you can commit.

A capacity model forecasts ARR from rep production, seat by seat. Ambition is not one of the inputs.

Each seat carries a ramp schedule and an expected first-year yield. The sum of scheduled seats, net of a failure haircut, is the number you can commit.

The fundamental advantage: the revenue plan and the headcount plan become the same document. You cannot change one without repricing the other.

How it works

01Define the unit. One seat's ramp schedule and first-year yield, at the real median deal size.
02Schedule the seats. Hire dates decide when capacity exists. The grid is the calendar.
03Haircut the grid. Some reps will miss. The model absorbs 30% before the board has to.
04Commit the output. What survives the haircut is the number, and it is already a hiring plan.

Why it predicts

Misses become seat-level variances you diagnose on Friday. Not annual surprises you explain in Q4. Executed well, the growth number is an arithmetic consequence of hiring dates.

A rep is a $600K asset in year one.

Example scenario: a company exits this year at $1.2M ARR, targeting $7.0M in 12 months. Start with one seat, one ramp.

Bookings ramp, one new seat

$0

$125K

$225K

$250K

Q1

onboarding + pipe

Q2

$41.7K / mo

Q3

$75K / mo

Q4

$83.3K / mo

The unit, summarized

$600K

Year-1 yield

$1.0M/yr

Steady, M10+

$120K

Median deal

$300-330K

OTE, 50/50

The rule

Plan capacity at production, not quota. Haircut the team 30%. Front-load hiring: late seats pay next year.

The activity math behind the $600K.

One ramping seat, first 12 months, locked at the $120K median deal. Drag the deal size and watch the same $600K reprice the year.

The funnel, compounded from the nine-stage system

147

Meetings held

16%

23

Qualified opps

27%

6

POVs started

81%

5 wins

= $600K booked

Median deal (ACV) $120K

$120K is the locked plan. The $75K sensitivity: 8 logos, ~229 meetings.

Activity, per ramped seat	Annual	Qtr	Mo	Wk
New discovery meetings (S0)	147	37	12	3
Qualified opportunities (S2)	23	6	2	0.5
POVs started	6	1.5	0.5	0.1
Closed-won new logos	5	1.3	0.4	0.1

Compounded from the nine-stage table: meeting → qualified 16% · qualified → POV 27% · POV → win 81%. 147 meetings = 3 a week × 49 working weeks.

The link

Three net-new meetings a week per seat: exactly the pod's dual PG quota (AE 1 + BDR 2). The capacity model and the activity system are the same machine.

From $1.2M to $7.0M: a hiring schedule.

Example scenario: a company exits this year at $1.2M ARR and wants to reach $7.0M total ARR in the next 12 months. Change a hire date and the ARR number reprices itself.

Failure haircut 30%

Plan range 25-30%. The deck plans at 30.

Expansion, net of churn $0.93M

Workbook models $1.0M expansion less $70K churn.

Median deal (ACV) $120K

Prices the logo count, and the activity math above.

Advanced: starting ARR and the ramp

Starting ARR Ramp Q1 Ramp Q2 Ramp Q3 Ramp Q4

Bookings per quarter of tenure. The Q4 rate holds from month 10 on: the $1.0M/yr steady state.

Seat

Start

Q1

Q2

Q3

Q4

Year 1

Cell shading steps with the dollar figure: the capacity staircase, made visible. 2026 cloud sales benchmarks.

Canonical scenario: 12 seats, 8 new hires by July.

Gross capacity

$6.96M

12 seats

Failure haircut

$2.09M

30% of the grid

Net new logo

$4.87M

~41 at $120K

Starting ARR

$1.20M

the ARR carried in

Expansion, net

$0.93M

base, less churn

Exit ARR

$7.00M

total company ARR

Standalone tool Plan your own year. The capacity planner: eight inputs, the hiring schedule, the activity math, and the burn. Share it by link.

06 · The team

The functions you are actually hiring.

Sixteen functions. Which exist by $5M, $10M and $20M is the hiring argument.

By milestone

New logos & expansion

Sales function

RSMs5M

Enterprise Sales

AEs5M

Commercial Sales

BDRs5M

Business Development

SEs5M

Sales Engineering

Marketing function

Pipeline5M

Demand Generation

DevRel5M

Dev Community

PMM10M

Product Marketing

Content10M

Content Marketing

Channel & alliances

CAMs20M

Channel Sales

Partnerships20M

GTM Alliances

Field20M

Field & Channel Marketing

Co-marketing20M

Partner Co-Marketing

Renewals & success

AMs10M

Account Managers

CSEs10M

Customer Success Engineers

Advocacy20M

Customer Marketing

Brand20M

Brand & Corporate

The 17th function

It spans every function above. Systems, forecast, comp, territory, data. One person: the GTM engineer.

Who to hire, and when.

Four stages. The seat count is the plan; the role each stage unlocks is the argument.

Stage	Sellers	Support	Role the stage unlocks
$0-1M	Founder + 1 FDE	Contract SE help	FDE: the builder in the room
$1-5M	2-3 AEs + 1-2 BDRs	1 SE · DevRel	GTM engineer by ~$3M
$5-10M	6-8 AEs, dual-track + velocity pod	2-3 SEs · 2 BDRs	CRO or VP Sales, player-coach
$10-20M	10-14 AEs · EMEA pod	SE team · channel lead	First-line managers · VP Marketing

Pair every seller hire

Two at a time, shared onboarding, and an honest A/B on whether the bar held.

Hire ahead of the ramp

A month-7 hire contributes a stub this year. The ramp is the constraint.

Backfill on day 60

Two misses at the 60-day activity gate, open the req that same day.

The rule

Every seat on this table is a line in the capacity model. Change a hire date and the ARR number reprices itself.

What the team costs.

2026 ranges for AI and AI-security scale-ups in US metros. Quota-to-OTE is the number that decides the model.

Role	OTE range	Split	Mechanics
Enterprise AE	$300-330K	50 / 50	Quota-to-OTE 3-3.5x. Accelerators 1.25x above plan, no cliffs.
FDE	$220-280K	80 / 20	Bonus on POV wins and on repeats that get productized.
Sales Engineer	$230-260K	70 / 30	Variable on POV win rate and POV cycle time.
MM / Velocity AE	$200-250K	50 / 50 to 60 / 40	Paid on tier wins and on conversion SLAs, not on discovery volume.
GTM Engineer	$160-200K	Base + bonus	Owns the AI stack. Bonus on pipeline-per-pod efficiency.
BDR	$90-110K	75 / 25	Paid on held meetings that convert to Stage 2, never on dials.

55-60%

Year-1 yield vs quota

Nobody carries a full number before month seven. Plan year-one production against this yield, and gate 10% of variable comp on pipeline generation.

07 · The operating system

The operating rhythm.

Leading indicators get inspected. Lagging indicators get judged. The calendar enforces both.

Leading · Inspected weekly

Stage-0 meetings by pod and source, vs the weekly bar
PQL flow vs gate, velocity pod SLA compliance
Coverage entering quarter: 3.5x new · 1.2x expansion
Stale pipeline under 15% older than two quarters
MEDDPICC score on every Stage-2+ opportunity

Lagging · Judged monthly

Bookings vs plan, by segment and by source
Blended new-logo ACV vs the field-economics floor
Stage-2 to win 20%+ · POV-to-win 75-85%
NRR 115%+ · GRR 90%+
CAC payback under 18 months · burn multiple

The cadence

Mon

Commit and upside call

Wed

Pipeline scrub by stage

Fri

PG scorecard by pod

Monthly

Win/loss and pricing

Quarterly

QBRs and the board pack

The rhythm

Nothing on this page is a status meeting. Every session ends in a decision, an owner and a date.

Five ways AI scale-ups die.

Every risk gets a mitigation and a numeric tripwire. When the tripwire fires, the decision is already made.

01Adoption runs slower than the hypeUnder 40 PQLs in a quarter

Mitigation

Anchor on what is in production today. PLG keeps CAC low while the category matures.

Tripwire → action

Under 40 PQLs in a quarter → shift the mix to field, revisit the segment bets

02Priced as a tool, not a platformField ACV under $120K for two quarters

Mitigation

Pricing floors, platform packaging, POV outcomes the economic buyer signed.

Tripwire → action

Field ACV under $120K for two quarters → repackage and requalify up-market

03Rep failure runs past the haircutTwo reps below gates at day 60

Mitigation

Hold the hiring bar. Scorecards from day one, activity gates at day 60.

Tripwire → action

Two reps below gates at day 60 → reallocate pipeline, open the backfill req that day

04Paid tiers burn community goodwillDownloads down 20% for two months

Mitigation

Clean OSS and paid boundary: the community keeps the tool, enterprise buys governance.

Tripwire → action

Downloads down 20% for two months → packaging review with founders

05Enterprise deals go single-threadedOver 30% of late-stage pipeline single-threaded

Mitigation

MEDDPICC enforced: no Stage 3 without an economic buyer meeting on the calendar.

Tripwire → action

Over 30% of late-stage pipeline single-threaded → daily deal reviews on those accounts

Why this exists

A plan without failure conditions is a pitch. Wire each tripwire to a decision and review all five at every QBR.

08 · The operator

The operator behind the playbook.

Enterprise revenue, rebuilt around an AI-native stack.

Andrew Wesbecher

26 years building enterprise security and infrastructure revenue.

VP Sales and CRO roles scaling early-stage SaaS.

Today, fractional CRO across AI security: AI-SPM, identity, agent security, detection and response.

This playbook was researched, written and designed with the AI stack it describes.

Where the work happened

TIBCO · Meraki · ThousandEyes
Lacework · Contrast · Traceable

$11B+

Combined exit & peak value

Lacework $8.3B peak. Meraki $1.2B and ThousandEyes $925M to Cisco. Traceable $900M to Harness.

$1M → $25M

Early-stage ARR, scaled

The path this playbook maps, run more than once as first or second sales leader.

4

AI security clients today

Fractional CRO and advisory work. One client is an RSAC 2026 Innovation Sandbox finalist.

10

GTM plans for AI companies

Every framework in this playbook shipped in a real plan first, then survived a real pipeline.

Next step

If this maps to your next twelve months, I would rather talk than pitch. awesbecher@gmail.com

ChatGPT changed everything. The math held.

Your buyer moved. Three years ago.

The GTM delta, in one table.

What 26 years says still wins.

Two engines, one capacity model.

The nine stages have not changed.

The FDE motion: selling by building.

Bottoms-up and top-down, same account.

Expand vectors

Pricing architecture

Qualify the company, then the committee.

Economic

Product decision

Evaluators

The stack I pay for but never log into.

A prompt is the new login.

Always on, no prompt needed

The operating model

AEO is the new demand capture.

Measure it

The headless workflow catalog.

What the engine buys you.

Not a concept

Four stages, one question each.

Stage 1 · $0-1M: sell truth, not scale.

The stage in one number

Graduate when

Failure mode

Stage 2 · $1-5M: the first repeatable pod.

The stage in one number

Graduate when

Failure mode

Stage 3 · $5-10M: the engine, math shown.

Stage 4 · $10-20M: make growth compound.

International

The board forecast

Graduate when

Failure mode

Sales capacity planning, defined.

A rep is a $600K asset in year one.

The activity math behind the $600K.

From $1.2M to $7.0M: a hiring schedule.

The functions you are actually hiring.

Who to hire, and when.

Pair every seller hire

Hire ahead of the ramp

Backfill on day 60

What the team costs.

The operating rhythm.

Five ways AI scale-ups die.

Mitigation

Tripwire → action

Mitigation

Tripwire → action

Mitigation

Tripwire → action

Mitigation

Tripwire → action

Mitigation

Tripwire → action

The operator behind the playbook.