The Rise of GTM Engineering – Clay + AI
Artificial intelligence is fundamentally changing revenue operations — from inbound qualification to buyer intent scoring to outbound targeting.
In this Xgrid Talks session, Everett Berry, Head of GTM Engineering at Clay, explains how AI now automates decisions that GTM teams once made manually — and why companies must build engineered, automated GTM systems to stay competitive.
The New Era of GTM: AI Handles Decisions. Humans Engineer the System.
Over the last two years, the biggest shift in go-to-market hasn’t been email volume, outbound tactics, or new tools.
It’s this:
AI can now make judgment calls inside your funnel.
Lead qualification.
Routing.
Prioritization.
Custom signal detection.
Inbound and outbound workflows.
What used to require SDRs, RevOps analysts, or manual processes can now be automated end-to-end with AI.
But Everett is clear:
Automation doesn’t simplify GTM.
It transforms GTM into an engineering problem.
As AI takes over decisions, companies need people who can design, structure, maintain, and optimize the GTM engine itself.
That’s where GTM Engineering comes in — a discipline that combines systems thinking, workflow design, API fluency, and signal-based automation.
What You’ll Learn in This Talk
1. How AI has permanently changed go-to-market
AI is no longer a “copywriter.”
It now drives real operational logic — evaluating accounts, prioritizing outreach, enriching inbound leads, and powering multi-step workflows.
Learn why human-driven GTM is too slow and inconsistent in the era of intent signals and AI decisioning.
2. How leading companies automate inbound qualification & routing
Figma, Vanta, Plaid, and others are now running:
– AI-powered inbound qualification
– automated lead routing
– enrichment-based scoring
– instant response flows
Everett breaks down the exact mechanics of inbound automation, including how Clay uses enriched data + signals to trigger the right next step.
3. Why GTM Engineering has become a core revenue function
GTM Engineers build the logic behind AI GTM — workflows, If/Then automation, scoring systems, data pipelines, and custom signals.
AI doesn’t eliminate people.
It requires system builders who understand enrichment, scoring, orchestration, and pipeline automation.
4. What an AI-powered GTM system actually looks like
Everett shares real examples including:
– Clay enrichment workflows
– custom intent signals (FAA violations, website changes, social signals)
– agentic GTM workflows
– composite heat scoring
– outbound targeting engines
– multi-system orchestration
This is GTM designed like software: predictable, automated, and measurable.
Explore Xgrid’s GTM Engineering Services
5. The skills and mindset of a modern GTM Engineer
Learn the core competencies required to operate next-generation GTM:
✔ If/Then thinking
✔ API fluency
✔ signal-based logic
✔ enrichment strategy
✔ prompt design
✔ workflow architecture
✔ data quality management
✔ test → measure → iterate cycles
Summary Insights
This talk delivers some of Everett’s most important insights on modern GTM:
• Signals don’t mean “reach out now”; they inform account heat
• Custom signals outperform generic intent data
• Outbound is now a targeting problem, not a volume problem
• AI agents inside workflows—not standalone agents—are what actually work today
• Data quality is the foundation of all automation
• GTM is shifting from cold email → multi-channel orchestration (LinkedIn, iMessage, WhatsApp, events)
• AI makes the decisions; GTM Engineers build the decisions into the system
These insights are essential for teams navigating the future of outbound, demand gen, pipeline automation, and AI-driven GTM.
About the Speaker
Everett Berry
Head of GTM Engineering — Clay
Everett is one of the leading voices behind the GTM Engineering movement.
At Clay, he has helped shape the frameworks, workflows, and system architectures that fast-growing companies use to build AI-powered revenue engines.
He works across AI automation, pipeline operations, enrichment infrastructure, signal design, API integrations, and GTM systems architecture — helping teams move from manual processes to engineered GTM systems.
Why This Talk Matters
The companies scaling fastest aren’t sending more emails or adding headcount.
They’re building automated GTM engines that:
• run on enriched data
• score accounts using dozens of signals
• orchestrate multi-channel outreach automatically
• reduce manual decisions
• move leads faster from inbound → pipeline
• combine AI + system design for precision
AI removed the manual workload.
But it increased the need for people who can architect the system behind the scenes.
If your GTM strategy involves outbound, inbound automation, RevOps, ICP targeting, or AI workflows — this talk is essential.
