The Half-Baked Discipline: What GTM Engineering Should Claim Next
Four unclaimed territories that turn GTM engineering from a toolbox into a strategic function. Explore audience analysis, competitive intelligence, positioning validation, and signal-to-strategy feedback loops.
The Half-Baked Discipline: What GTM Engineering Should Claim Next
Four unclaimed territories that turn GTM engineering from a toolbox into a strategic function.
I wrote recently about GTM engineering’s scope problem: the discipline defined itself around the tools it adopted and stopped short of the strategic work those tools make possible. Enrichment, scoring, outbound automation, workflow orchestration. All valuable. All incomplete without a strategy layer.
So what does it actually look like when GTM engineering claims the full scope?
Four areas stand out. None of them require inventing new technology. They require deciding that the boundary we drew was premature.
Audience Analysis as an Engineering Problem
Most segmentation still runs on firmographics. Industry, company size, revenue band, maybe some technographic data layered on top. It’s the same segmentation playbook from 2015 with better data sources. But the engineering capability exists right now to do behavioral clustering across account populations, to identify signal correlations that firmographic cuts miss entirely, to detect patterns in engagement data that reveal segments your marketing team hasn’t named yet.
Companies like 6sense and Demandbase have been pushing into predictive account identification for years, but the underlying methodology, building programmatic segmentation that adapts to behavioral signals rather than static attributes, is engineering work. It belongs in GTM engineering’s scope, and almost nobody’s doing it there.
Competitive Intelligence Systems
Right now, competitive intel at most companies lives in a slide deck that gets updated quarterly, maybe. Someone from product marketing spends a few days reading competitor websites, checking G2 reviews, and summarizing what changed.
That’s a monitoring problem. It’s an engineering problem. Building systems that track competitor positioning shifts, pricing changes, product launches, job postings that signal strategic pivots, hiring patterns that reveal where they’re investing. Klue and Crayon have built products around this, but the custom layer, the one that ties competitive signals to your specific positioning and triggers strategic review when something shifts, that’s GTM engineering work. Unclaimed.
Positioning Validation Through Data
We A/B test email subject lines religiously. We test landing page copy, CTA button colors, send times. And then we run our core positioning, the foundational messaging that determines everything downstream, on gut feel and quarterly brand surveys.
The gap is staggering. Testing whether your positioning actually resonates with the segments you’ve defined, at a statistical level, across channels and touchpoints, is engineering work. It’s the same experimental design infrastructure we already use for conversion optimization, applied one layer up.
Signal-to-Strategy Feedback Loops
This is the big one. Every enrichment pipeline, every scoring model, every signal monitoring system generates data about whether your go-to-market assumptions hold. Your enrichment data tells you whether your ICP definition is still accurate or whether it’s drifted. Your engagement signals tell you whether your positioning is landing with the accounts you intended to reach. Your conversion patterns tell you whether the segments you defined actually behave the way you predicted.
Right now, that information flows through GTM engineering’s systems and disappears into dashboards that nobody connects back to strategic decisions. The feedback loop that connects execution data to strategy refinement is the single most valuable thing GTM engineering could build. And it’s the thing almost nobody’s building.
Why This Matters Now
Mike Rizzo, who runs the MO Pros community, has proposed “GTM Product Manager” as the role that should own strategic coordination of the go-to-market stack. His argument is specific: someone needs to own the technology “like a product,” stitching together capabilities across platforms to enable a buyer journey focused on business outcomes. He’s even building a certification around it.
He’s identifying the right gap. Someone needs to own the space between execution tools and strategic decisions. But proposing a new title to fill a gap that an existing discipline could claim feels like building a bridge when you could just extend the road.
The AI layer makes this more urgent, not less. If you’re building LLM-powered enrichment and scoring systems, and increasingly everyone is, those systems generate strategic insight as a byproduct. An AI-powered enrichment pipeline doesn’t just fill in missing firmographic fields. It can identify patterns in the data that challenge your existing segmentation. It can surface account characteristics that correlate with conversion but don’t map to any segment you’ve defined. Ignoring that output because “strategy isn’t our job” is leaving insight on the table while someone else scrambles to find it manually.
The discipline that claims both execution and the strategic feedback from execution becomes indispensable. The one that stays in the toolbox, no matter how good the tools are, stays replaceable. Not because the work isn’t valuable, but because execution without strategic input is a commodity. Someone will always be able to run your playbook cheaper or faster. Nobody can replace the function that tells leadership what the playbook should be.
Closing the Loop
I’m not proposing a framework or a manifesto. The principle is straightforward: every system you build should have a measurement surface that feeds information back upstream.
Enrichment data doesn’t just score accounts. It validates or invalidates your ICP definition. If your highest-converting accounts consistently fall outside your defined ICP, that’s not an enrichment problem. That’s strategic intelligence telling you your targeting is wrong.
Signal monitoring doesn’t just trigger outbound sequences. It tests whether your competitive positioning holds. When a competitor shifts their messaging and your engagement rates change in response, that’s a signal that should reach your positioning team the same day, not three months later in a win/loss review.
The discipline grows by growing what it’s accountable for. Not by adding another integration, another tool, another workflow. By closing the loop between what our systems see and what our organizations decide.
I’ve been building toward this in my own work, designing scoring systems and signal layers specifically to surface this kind of strategic feedback. The technical infrastructure isn’t the hard part. The hard part is the discipline collectively deciding that strategy belongs in the job description.
We’ve come a long way from RevOps with better tooling. But there are miles to go before we sleep.