#36 - GTM Engineering
and the new class of SDRs
I’ve experimented with a few AI SDRs that promise to handle everything from lead sourcing to outreach execution. These platforms have been, frankly, a mess.
In 2025, when standing out is the only way to convert meetings, bots that can’t understand nuance—or worse, deliver painfully obvious “personalized” messages—just burn through good leads with nothing to show for it.
That said, expertly bundling human and virtual agents is becoming a necessity to drive productivity and reduce costs. Many of the best orgs are already doing this.
I’ve spoken at length about Clay. I like the platform and I’m not paid to say that.
But it’s not a silver bullet. It’s powerful but still very hard to use effectively. And depending on your specific needs, there may be more intuitive tools out there.
Still, Clay is pushing an important shift in how we think about go-to-market strategy—especially with its advocacy for a new kind of role: GTM Engineering.
Replacing your SDR function with GTM Engineering will be the smarter path to sustainable, profitable growth.
What is GTM Engineering?
GTM (Go-to-Market) Engineering is an emerging discipline at the intersection of growth, operations, and automation. Its purpose is to build scalable systems that:
Identify, enrich, and qualify leads
Personalize outreach using real-time data
Automate routing, follow-ups, and CRM workflows
Continuously test, learn, and optimize outbound motions
Rather than scaling headcount to build pipeline—where outdated systems dilute ROI—GTM Engineering challenges reps to treat pipeline like a product: engineered for efficiency, repeatability, and scale. When built right, this “product” generates pipeline growth without adding incremental cost, allowing reps to drive more revenue from the same inputs.
Why GTM Engineering is Replacing SDRs
1. Efficiency > Headcount
Traditional SDR teams operate on high-volume, low-yield tactics. But tools like Clay, Apollo, and Clearbit can now automate much of that work—faster, cheaper, and often with better results.
2. Personalization at Scale
No SDR can handwrite 200 thoughtful emails a day. GTM Engineers build systems that respond to live signals—like job changes, funding rounds, or hiring sprees—and automatically tailor outreach.
But a human must own the build-test-iterate loop. No set-it-and-forget-it here.
3. Systematic Pipeline Creation
Instead of hiring more SDRs, GTM Engineers create workflows that:
Pull in enriched, intent-based leads
Trigger personalized messaging
Push sequences into Outreach or HubSpot
Automatically log interactions into the CRM
The result: a scalable, always-on pipeline engine.
What a GTM Engineer Actually Does
A GTM Engineer is part technologist, part operator, part growth hacker. Responsibilities typically include:
Using low-code tools (Clay, Zapier, Retool) to build automation
Integrating data sources (LinkedIn, Crunchbase, job boards) to enrich targeting
Running experiments to test messaging and timing
Setting up trigger-based sequences (e.g. “Company X raised $20M → send this sequence”)
Translating GTM strategy into systems that scale
Where It’s Already Happening
Early-stage SaaS: Founders build automation instead of hiring reps to generate initial market feedback, and land their first few customers.
Bootstrapped startups: Stay lean to stay profitable.
Series B+ orgs: Replacing SDR headcount with GTM Engineers for predictable, cost-efficient growth as markets shift towards valuing profitability and EBITDA vs. Revenue growth.
What This Means for GTM Teams
The old-school pod model (AE + SDR + CSM) is giving way to a more modular, systems-driven approach:
AEs focus on closing
GTM Engineers own pipeline creation
RevOps ensures optimization and insight
AI copilots support every role along the way
Where to Start
Audit your SDR function—what’s truly manual vs. what can be automated
Pilot tools like Clay, OpenAI, and Clearbit to build systems
Hire or upskill a GTM Engineer before adding more headcount
This isn’t about killing the SDR role—it’s about evolving it.
While fully automated “soup-to-nuts” AI SDRs may someday show promise, I remain skeptical. The moment one tactic gains traction, it’s copied en masse—flooding inboxes and turning once-effective outreach into noise. The only sustainable path to scalable pipeline growth is blending human strategy with automated systems—using them to test, learn, and iterate continuously. Humans guide the “why”; automation powers the “how.”