GTM Engineering: How Top Teams Are Treating Go-To-Market Like a Product

GTM Engineering: How Top Teams Are Treating Go-To-Market Like a Product

What if your go-to-market motion didn’t rely on guesswork, one-off campaigns, or scattered tools — but instead ran like a product?

This was the central idea in our latest XgridTalks session featuring Everett Berry, Head of GTM Engineering at Clay. And if his insights are a preview of where revenue teams are heading in 2025, one thing is clear:

The future of growth is engineered — not executed.

Everett’s perspective reframes how CMOs, RevOps leaders, Marketing Ops teams, and founders should think about building revenue. GTM Engineering isn’t a trend. It’s becoming the operating system behind the fastest-growing companies in the world.

The Secret to Scalable Revenue? GTM Engineering

GTM Engineering is exactly what it sounds like:

“Treating go-to-market like a product — designed, built, released, measured, and constantly improved.”
— Everett Berry, Clay

Where traditional teams run campaigns, GTM-mature teams build systems:

• Systems that get smarter every week 
• Systems that self-optimize using AI and signals 
• Systems that deliver predictable pipeline without human bottlenecks 

Everett calls GTM engineers “the most technically advanced go-to-market people in the market right now.”
They blend APIs, automation logic, CRM architecture, enrichment, and AI models into a single unified GTM engine.

This is where Ops meets AI — and it’s rapidly becoming the cornerstone of modern revenue organizations.

The Modern GTM Stack: 4 Core Layers of an Engineered Growth System

Everett breaks the GTM Engineering stack into four essential components — and stresses that if even one layer breaks, the rest of the system becomes unreliable.

Below is the deeper, more strategic view of each layer:

1. System of Record — Your GTM Brain

(HubSpot, Salesforce, Snowflake)

This is the foundation: your customer and account source of truth.

It defines:

• ICP qualification 
• Account ownership 
• Deal stages & lifecycle 
• Revenue history 
• Attribution 
• Buying committee structure 

Everett’s golden rule:

“You can’t automate what you can’t trust.”

If your CRM is messy:

• enrichment falls apart 
• scoring becomes meaningless 
• signals misfire 
• outbound goes to the wrong people 
• analytics show lies 

Most GTM failures begin at the data layer — not the strategy layer.

2. The Enrichment Layer — Turning Raw Data Into Intelligence

(Clay, Clearbit, ZoomInfo, third-party providers)

Enrichment is the quality engine of GTM. It updates and cleans your data continuously, not once per quarter.

This layer handles:

• Valid email & phone waterfalling 
• Job changers 
• Role verification 
• Sub-industry segmentation 
• Tech stack identification 
• Employee counts 
• Accurate locations 
• Social URLs 
• Buying committee mapping 

Everett calls this layer:

“The layer that transforms basic CRM entries into GTM intelligence.”

If this layer is weak, personalization is weak — and automation breaks.

3. The Action Layer — Where GTM Actually Executes

(Outreach, Apollo, LinkedIn, dialers, Clay actions, multi-channel outreach)

This layer is responsible for the actual doing:

• outbound sequences 
• LinkedIn touches 
• calls 
• nurtures 
• alerts 
• routing 
• content delivery 
• ABM workflows 

Everett is blunt here:

“The action layer shouldn’t think — it should execute.
All the thinking happens above it.”

Signals + enrichment → tell the action layer exactly what to do, when, and with whom.

This is where AI-generated copy, adaptive messaging, and multi-channel sequencing begin to shine.

4. Analytics Layer — The GTM Debugger

(Gong, Looker, dashboards, reporting tools, Clay analytics)

This is the feedback loop that makes every sprint smarter:

It reveals:

• which signals actually drive pipeline 
• which channels convert 
• which messaging resonates 
• which campaigns die 
• where reps break process 
• where accounts stall 

Everett calls this layer:

“The debugger of GTM systems.”

This is what turns GTM from campaign-driven → system-driven.

Don’t Respond Faster — Respond Smarter: The New Role of Signals

One of the biggest misunderstandings in GTM today?

Treating signals like triggers.

Many teams still believe:
“Signal fires → send outreach immediately.”

Everett warns against this:

“Signals tell you who’s warming up — not who to bombard.”

Top teams instead use signals to calculate heat, not initiate spam.

A modern GTM signals model includes 8–12 data points:

• ad interactions 
• LinkedIn engagement 
• website visits 
• event attendance 
• outbound replies 
• founder interactions 
• ICP persona match 
• buying committee movement 

As heat rises → the account moves into deeper levels of personalization.

Signals become strategy, not noise.

From Ops to “Agentic Workflows” — GTM’s Next Evolution

Most people imagine autonomous AI SDRs taking over outreach.

Everett gives a more grounded view:

We’re not at “full agents” yet.
We’re at agentic workflows — and they are incredibly effective.

He defines them as:

“Workflows that can make small decisions for you — without losing control.”

Here’s what agentic workflows look like in real GTM systems:

1. Continuous Qualification

Accounts are re-scored continuously based on:

• enrichment updates 
• job changes 
• website activity 
• persona behavior 
• buying signals 

Not quarterly.
Not monthly.
Continuous.

2. Adaptive Messaging

Sequences adjust as new data arrives:

• new hires 
• new AI product launches 
• new pages added 
• new funding 
• new tech stack signals 

Outbound evolves as the account evolves.

3. Signal-Driven Channel Selection

Not “email-first.”

Instead:

• LinkedIn for social personas 
• WhatsApp/iMessage for high-trust contacts 
• calls for C-levels 
• email only when warm 

The system chooses the channel — not the rep.

4. Human-in-the-Loop Oversight

Agentic workflows don’t replace people.

They replace low-level work so humans can focus on:

• strategy 
• creative outbound 
• play design 
• high-value interactions 

Everett is clear:

“AI amplifies good GTM — it doesn’t replace it.”

Run GTM Like Product Sprints

Everett’s biggest recommendation for modern teams:

“Run your GTM in two-week sprints — just like your product team.”

This turns GTM from random experiments to disciplined iteration.

Here’s what a real GTM sprint looks like:

1. Sprint Planning (Day 1)

Define:

• segments 
• signals to refine 
• message variants 
• channels to prioritize 
• hypotheses 
• success metrics 

This removes guessing entirely.

2. Execution Window (Days 2–10)

Accounts flow through the GTM engine:

• signals update heat 
• workflows route accounts 
• messaging adapts 
• SDRs + automation run in parallel 
• enrichment sharpens personalization 

This is where the system learns.

3. Measurement & Review (Days 11–13)

Teams examine:

• which signals correlated with movement 
• which channels spiked 
• which segments activated 
• what died 
• where bottlenecks appeared 

This prevents stagnation.

4. Sprint Retro (Day 14)

Teams adjust:

• scale winning messaging 
• adjust signal weighting 
• retire failing plays 
• plan the next sprint 

This rhythm compounds the pipeline every two weeks.

Why This Matters for CMOs & RevOps Leaders

Choosing another tool won’t help you scale.

Choosing a new operating model will.

Here’s Everett’s playbook for 2025:

✓ Start with data quality

Everything else breaks without it.

✓ Automate what already works

Don’t automate noise — automate proven processes.

✓ Treat signals as strategy, not triggers

Composite heat scoring is the new ICP.

✓ Adopt sprint-style GTM iteration

Your GTM should improve every two weeks.

✓ Engineer your GTM like a product

Not a calendar. Not a sequence. A system.

The teams that win in 2025 will be the teams that build their GTM — not the ones that run it manually.

The Future Belongs to GTM Engineers

GTM Engineering is not a niche skill anymore.
It’s the backbone of scalable, AI-powered growth.

Everett’s final message is the clearest signal of all:

The companies who adopt this mindset will build revenue machines.
The ones who don’t will keep running campaigns — and wondering why growth is slowing.

Watch the Full XgridTalks Episode

GTM Engineering Masterclass | Signals, Automation & AI with Head of GTM Engineering at Clay