CRO︱VP SALES︱GTM OPS
AI is ready for GTM. Your data isn't.
Most teams are investing in AI and still stalling on pipeline. The architecture is why.
Sound familiar?
Four Signs The Architecture Is The Problem
01
Finance wants numbers you don't have
Cost per outcome. Most teams can't answer at renewal.
02
AI breaks monthly credit limits
Burst usage, pipeline spikes. Seat allotments weren't built for this.
03
Paying 2–3× for the same data
Separate pools for prospecting, enrichment, API. Same vendor.
04
More sources, same coverage gaps
No shared layer means new vendors deliver into silos.
Root cause
The data's fine. The architecture isn't.
Cost multiplies
Separate pools = 2–3× spend for one foundation.
Coverage stalls
New vendors add duplicates, not reach.
Visibility disappears
No shared layer means no single place to see what's working. Budget decisions run on gut, not data.
AI can't function
AI models need unified, clean signals. Fragmented inputs create noise — and noisy inputs break accurate outputs.
HOW MOST TEAMS RUN TODAY
Prospecting
monthly cap
Enrichment
separate budget
API/MCP
third contract
No shared layer → 2–3× cost, 0% uplift
WHERE YOU NEED TO BE
One foundation → every motion, measured by pipeline
The hidden tax
You're paying full price
for things that cancel each other out.
Three separate bills. All touching the same contacts.
AI models need unified, clean signals. Fragmented inputs create noise — and noisy inputs break accurate outputs.
They all pull from the same universe of B2B contacts — and you pay for each one separately. When one pool runs dry, work doesn't stop. It just gets expensive in ways you can't see
They all pull from the same universe of B2B contacts — and you pay for each one separately. When one pool runs dry, work doesn't stop. It just gets expensive in ways you can't see
What a $10k / month data budget actually looks like
~40% of those records exist in two or more pools. You paid roughly $4,000 this month for contacts you already owned.
2–3×
Redundant spend
You're not getting 3× the data. You're buying the same contacts from three separate buckets — and paying for each one individually.
0%
Coverage uplift
Adding a second or third vendor doesn't expand your reach. Without a shared matching layer, new sources surface the same people you already have — just in a different silo.
4–5
Shadow vendors
When credits run out in week 3, reps don't stop. They use personal cards and buy from other tools. Finance doesn't see it. You don't measure it. But it's happening.
The only metric that matters: records attributed to pipeline.
Real teams. Real results.
Three Companies That Fixed It
Eitan Altman
Dir. Revenue Operations
Tonic.ai · Series B
Tonic.ai · Series B
Reps drop a name in Salesforce. Enrichment runs automatically. Only works with a universal pool — monthly caps blow out in week one.
Think less, sell more.
Ben Kanellitsas
Dir. GTM Operations
Klarity · Series B
Klarity · Series B
Five motions, one pool, zero overlap. Coverage test expanded TAM by 20%.
Contact enrichment is effectively free at this point.
Matthew Johnston
Dir. Revenue Operations
Adaptive Security · Series B
Adaptive Security · Series B
494K credits consumed via API in 3.5 weeks. Found a 15× price gap vs. ZoomInfo.
95% of contact data is via API. We shouldn't pay for 2 credit buckets.
Three companies. One move: stop rationing data.
Where is your team?
The GTM Data Maturity Curve
The companies winning pipeline in 2026 changed the architecture — one universal pool, flexible consumption, measured by records attributed to pipeline.
Most teams are at Stage 1 or 2. Click your stage.
Fragmented
Multiple vendors, siloed pools. AI workflows starve or overpay. Most teams here don't realise the architecture is the problem.
Siloed credits
Cost multiplied
No attribution
Consolidated
One vendor, still seat-based. Motions run independently. Consumption spikes still break the model quarterly.
One vendor
Monthly caps
Still rationed
Target State
Architected
One shared pool — prospecting, enrichment, API, MCP. Value measured in records attributed to pipeline. This is the target.
Universal credits
Flexible
Pipeline attribution
Infrastructure
Data flows automatically. Reps never think about credits. Pipeline scales with consumption, not headcount.
Fully automated
AI-native
No ceiling
Stage 1 → 3
Collapse the silos first
Move everything onto one Universal Credits pool.
Usually a 4–6 week migration
Stage 2 → 4
Automate the last mile
Add on-create enrichment, champion tracking, buying committee.
Typically unlocked in first 90 days
Ready to move?
See exactly where you stand.
30 minutes. Map your architecture, quantify the cost, model flexible consumption.