Complex GTM stacks are being sunset (you thought I said dead)

You feel it already in your Rev Ops world: the jammed tech stack, the countless tools, the dashboards no one opens, the click-paths no one loves. And you’re not wrong—the era of complex GTM stacks is entering its twilight.

But what comes next?

I saw a post from Aaron Ross recently. He asked: “Who’s building the CRM you can talk to?”

That question hits a different level.

Imagine this workflow:

• You sit down and say: “Let’s get started.”

• No clicking through tabs, no hunting down data fields, no juggling calendar, email, CRM and five other silos.

• The system surfaces who you need to call, which deals need attention, and what’s on your agenda.

• You speak. It records what you said. It translates your words into performance dashboards, action items, and orchestration logic.

Sounds futuristic? Closer than you think.

If the original cloth loom mechanized weaving, the conversational CRM is mechanizing execution. The analogy holds deeper than you’d expect.

From loom to voice-first GTM

The killer metaphor: the Jacquard loom. Invented in the early-1800s, it used punched cards to tell the machine which warp threads to raise for each pass. A human weaver didn’t have to handle each repetitive motion anymore. 

The loom didn’t replace the weaver; it replaced the repetitive tasks. The pattern designer still defined what needed to be made. The pains of manual repetition gave way to scalable precision.

Now, think of your GTM stack. All those tools, dashboards, click-paths—they’re the hand-weaving equivalent. What we’re heading toward is the voice-first orchestration layer, the AI system we talk to, not click through. We’re not just automating tasks. We’re re-architecting the pattern.

But here’s where the story overlaps and the danger begins

Let’s be clear: Rev Ops was never about data entry. That role belonged to sales and marketing. Rev Ops has always been the pattern designer, the orchestration architect—defining how leads flow, how deals advance, how capacity is modelled, how resources are aligned.

That means when you hear about “AI for Rev Ops” today, you need to ask: Does it build capacity models? Does it optimise resource trade-offs? Does it encode runway, growth versus efficiency, political and human constraints?

Here’s why most don’t:

• AI tools can summarise calls or generate follow-ups—but they don’t design a usable capacity model for 2026.

• They can’t account for runway, quotas, churn, ramp-time, cross-functional hand-offs with nuance.

• I’ve seen AI generate GTM tech stack recommendations that would’ve landed a Junior Analyst—and maybe a VP—in trouble.

In short: the technology exists, yes—but the orchestration, the design, the pattern—still lives in the heads of Rev Ops leaders.

The new job description for Rev Ops

The next generation of Rev Ops won’t be clicking through dashboards or training AI assistants. They won’t be the ones with the caste of “we fix the inputs.” No.

They’ll be building the orchestration layer the assistants run on. They’ll craft the flows, tune the logic, define the joins, ensure the machine’s inputs make sense and serve the greater pattern.

We used to say: “If it’s not in SFDC it didn’t happen.” Now we might say: “If I said it, it already counted.”

Why the tech correction matters (and why you should care)

The correction to GTM tech has been overdue. We poured money into stacks, dashboards, tools—adding more instead of building smarter. Now we’re pivoting.

Watching how AI is reshaping go-to-market is arguably one of the most interesting business events in our industry I’ve seen in my career. Not just because it kills clicks, but because it surfaces the real question: who designs motion?

As Rev Ops leaders, you’re not being replaced—you’re being elevated. The craft is shifting. The tools no longer do the work. They execute the work. And that means the architects of motion are back in demand.

Want to dig deeper into how you build an AI-native GTM system? We can walk through real-world orchestration patterns, capacity modelling frameworks, and what the next-gen Rev Ops toolkit actually looks like.

(But we’ll save that for another post.)