Two thirds of B2B teams are using AI in go-to-market today (according to Scale Venture Partners’ State of GTM AI report). Eighty-five percent say it’s helping.

But here’s the problem: most of them can’t prove it. And the ones who CAN prove it? They’re using AI very differently from the rest.

Craig Rosenberg, Chief Platform Officer at Scale Venture Partners, has spent the last year building the State of GTM AI Report along with his team. It took hundreds of survey results, individual interviews, and AI labs/hackathons to compile the data.

When Craig sat down with us on the GTM Science Podcast to go over the report’s findings, one stuck out. There was a distinct split that explained why the vast majority of teams struggle to measure ROI from AI.

In this newsletter, we’re going over Craig’s key insights on why some teams are seeing 3-5x better results than everyone else.

Listen to the full podcast episode on Spotify here or Apple podcasts here.

The Split: Phase One vs Phase Two

The report identifies two distinct phases of AI integration. The gap between them explains why most teams can’t measure ROI.

Phase one is about doing more with less:

• AI-drafted emails
• ChatGPT for account research
• Tools focused on ease of adoption and frequency

Phase two is about using AI to drive business outcomes:

• Account scoring
• Campaign analysis
• Micro-segmentation
• Skills coaching for reps

Here’s the critical difference: phase one makes people faster at doing tasks. Phase two helps teams make better decisions about which tasks to do and how.

“Phase one is about individual productivity,” Craig explains. Phase one gains are real, but they’re fuzzy when you try to quantify ROI. Did that AI-drafted email actually lead to a meeting? How much time did it really save?

“Now let’s look at phase two where we start to have these downstream changes to metrics,” Craig says.

It’s not just that teams in phase two are better at measuring the right metrics. It’s that the activities themselves create different outcomes.

From the report: marketing teams using AI for campaign analysis and account scoring were 3x more likely to see increased pipeline volume and faster pipeline velocity, and 5x more likely to see increased conversion rates.

“That’s real data. That’s really happening,” Craig says. “The folks that said they were using AI in both phases, they saw the most lift for sure.”

The catch? “Not a ton of them” are doing phase two yet. Most teams are stuck in productivity mode while the real, measurable ROI lives in strategic applications.

What Phase Two Teams Are Actually Doing

Phase two teams are seeing better results because they’re using AI to solve fundamentally different problems that directly impact revenue metrics.

The report found specific patterns in teams achieving phase two results:

1. They’re targeting better
2. They’re analyzing what worked before scaling it

Micro-segmentation showed up repeatedly in successful implementations. Teams were using AI to analyze external signals, third-party data, and internal data to identify tighter account segments. Sometimes just a hundred accounts instead of thousands.

“From a business perspective, that makes sense right now,” Craig says. “We’re using AI to find a smaller amount of folks that we can concentrate on, and getting AI to help us create content for them. We’re not trying to boil the whole ocean.”

In Craig’s interviews, Sydney Sloan at G2 put it simply: without AI, they could never do this level of micro-segmentation.

Campaign analysis was the other major driver. Teams were using AI to analyze previous campaigns before launching new ones. They were looking at which mix of touchpoints worked, what messaging resonated, what timing converted.

The pattern: intentional segments, better data, more focused execution.

Craig references Dan Gottlieb at Gong for the test of AI readiness: successful teams don’t say, “Let’s use AI.” They say, “We’d like AI to create a QBR that’s four pages long that has this on the first page, this on the second, this on the third, this on the fourth and grabs from these six sources.”

Who Should Lead AI Discovery and Scaling

The report’s data on organizational structure also reveals something critical about scaling AI.

When RevOps leads AI initiatives, teams are 22% more likely to see high impact. Having a dedicated GTM engineer or AI lead accelerates results even further.

But here’s the reality Craig saw: “The vast majority of examples were someone just building it over a weekend.”

Individual contributors need the space to experiment and build. That’s valuable for discovery. But with one caveat.

“You can’t do that forever. It doesn’t scale,” Craig explains. “But that’s why we say, just start talking to the machines. Once you start to play with AI you can see what’s possible.”

The data shows that taking those “random acts of really interesting AI stuff” and systematizing them through RevOps is what creates repeatable impact.

Craig saw this pattern in action through hackathons and AI labs. They put sales reps or marketers in a room to build together, not because everything they create will be used, but because that’s how teams discover what’s possible. Then RevOps comes in to scale it.

What The Report Means for You

If you’re a CRO: Don’t start with “let’s use AI.” Start with “what are we trying to accomplish?” Then, don’t stop at productivity-based AI. The 3-5x results live in campaign analysis, account scoring, and micro-segmentation, not just drafting emails.

If you’re in RevOps: You should be leading this. The data shows you’re 22% more likely to drive impact when operations leads. Let people experiment, then systematize it.

If you’re a GTM leader: Focus on strategy first. AI isn’t going to fix targeting the wrong accounts or unclear messaging. It’ll just help you do the wrong things faster.

Our Take on This

(This section is written by Eddie Reynolds, Founder and CEO at Union Square Consulting.)

This aligns with what we’re seeing, but there’s more to this story IMHO. The teams performing best are doing the right things because they have their GTM foundation in place. Leadership is behind these initiatives and they have RevOps resources to do the heavy lifting. Both these things mean they can see things through and execute, not just tinker over the weekend and leave it stagnant. They’re not hamstrung by broken processes in GTM, bad data, or the team not executing the basics of GTM.

We’ve outlined those fundamentals in our GTM Efficiency Pyramid. For teams that need this foundation, we can help you build it. For teams with this foundation, we have the resources to help you leverage AI and take things to the next level. Reach out to us if you want to explore these options.

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