In the latest episode of What the Tech, we sat down with Jonathan Kvarfordt, VP of GTM Strategy at Momentum.io and founder of the GTM AI Academy. With over 15 years in revenue excellence and GTM acceleration, Jonathan has developed an AI GTM data orchestration platform that’s fundamentally changing how businesses capture, structure, and activate conversational data.
For customers like Zscaler, DemandBase, Ramp, and 1Password, this has translated into productivity gains of 3-10 hours per rep per week. But the real breakthrough isn’t just about time savings—it’s about unlocking a competitive moat that most companies don’t even realize they’re sitting on.
From Sales Rep to AI Educator
Jonathan's journey into AI started uniquely: working with someone who had built his own AI model from scratch. "I was in charge of doing the training and enablement for both customers and the sales team on what the heck is AI and how to use it," Jonathan explains. "I learned firsthand from a guy who built his own model about AI."
That hands-on education gave Jonathan something most people lacked when ChatGPT launched three years ago: a deep understanding of how AI actually works, not just the Terminator hype. When he saw people on LinkedIn talking about how they were using ChatGPT, he immediately recognized they were approaching it wrong.
"Everyone was just Googling," Jonathan recalls. "They'd do one-sentence things and assume the AI knew what they meant. The problem is people don't give enough information because they assume AI knows things it doesn't know."
His insight: Good prompting isn't optional—it's the difference between getting generic outputs and genuinely useful results. Even with today's more powerful models, the combination of advanced prompting techniques and stronger AI creates a compounding effect that far exceeds what either could accomplish alone.
The Conversational Data Goldmine Nobody's Mining
After launching the GTM AI Academy three years ago (with 10,000+ people going through his courses), Jonathan joined Momentum.io—first as a customer, then as an advisor, and eventually as VP of GTM Strategy.
What attracted him? A fundamental insight about an untapped resource:
"I don't think people realize how important conversational data is because we're not used to having it. All the conversations any team has—whether you have five people talking to customers or a thousand—there's so much data coming in that is unique to those conversations. Those conversations are a moat-able type of data."
Think about it: When your team talks to prospects and customers, they're gathering information that literally nobody else can replicate. The specific problems customers mention, the language they use, the objections they raise, the competitive alternatives they're considering—all of this is unique intelligence.
But here's the problem: It sits in Gong, Fathom, Chorus libraries doing absolutely nothing. You can't access it. You can't structure it. You can't act on it.
The Infrastructure Layer That Makes AI Work
This is where Momentum.io comes in, and Jonathan's explanation of the value proposition is brilliantly clear:
"We literally take the data coming from conversations, emails, tickets, calls, whatever—and we slice and dice it, extract it, clean it, validate it, and then put it into the CRM so you can trust the data going in there."
Why does this matter? Because AI thrives on clean data.
As Jonathan puts it: "The problem is people wanting to automate the CRM or other actions, but they can't because the data doesn't exist. Momentum becomes the infrastructure piece—the first layer of AI automations that dumps in data from conversations. From there you can use Zapier, Make, n8n, Agentforce, all sorts of stuff, both advanced and new, all based on you having data that's already structured, clean, and validated."
This is the foundational insight: all the flashy AI tools in the world are useless without clean, structured data feeding them. Momentum provides that infrastructure layer.
From Time Savings to Strategic Intelligence
The immediate benefit is obvious: sales reps save 3-10 hours per week because they're not manually updating CRMs or digging through notes before meetings.
But the more profound benefit? Sales reps never even touch the platform.
"The new world of AI tech isn't asking people to come to us—we want to go to them. We want to go to the workflow," Jonathan explains. "The sales reps love the tool, but they never touch it because it's doing all the work for them."
The real users? VPs of Revenue Operations, CROs, and other leaders who suddenly have access to structured, actionable intelligence from every customer conversation.
The Holistic Data View Nobody Has (Yet)
Jonathan sees a future where combining multiple data sets creates unprecedented strategic advantage:
"You can combine third-party data from Clay or Apollo, R&D data from companies like Boast, conversational data from Momentum, CRM data about deal sizes—you start having this holistic view of data we've never been able to do before. This opens up possibilities from an aggregate data point of view because you can match R&D data with finance, with sales data, with conversational data, with CRM data. You have this complete view that changes how you think about and make decisions on literally everything."
The connection to Boast is particularly relevant here. R&D tax credit data tells you what companies are actually building and where they're investing. When you layer that with conversational data about their problems and CRM data about deal progression, you suddenly have intelligence that competitors simply can't replicate.
Rethinking Pipeline Reviews
One of the most exciting applications Jonathan discussed: using AI-powered signals to augment or even replace traditional pipeline reviews.
Today's pipeline reviews rely heavily on subjective opinions from sales reps—and that creates obvious problems. "How would any of us feel if every week you have to go into a meeting where you have to justify your existence and not be subjective with the information? Because if it's bad information, you'll get fired. No one would actually do that, but we ask the sales team to do that all the time."
The result? Reps who are either way too critical or way too optimistic. CROs hear "we're gonna close 20 deals out of 30" when the reality is closer to five.
Jonathan's vision: pipeline reviews that combine subjective rep input with objective data signals:
- First-party conversational data from Momentum
- Third-party intent data from G2 and other sources
- Trend analysis (Is this competitor mention increasing week-over-week?)
- Pattern recognition across industries, deal sizes, and personas
"You now have this ability to see: is it going up, is it going down? When you mix it with third-party data, it becomes really, really powerful."
What's Next for 2026
Jonathan and the Momentum team are doubling down on deep research and aggregate analysis—moving beyond call-by-call insights to pattern recognition across entire data sets.
They're also building what Jonathan calls "signals" from first-party conversational data. For example: detecting when a new competitor emerges, not just flagging it but showing how often they're mentioned by industry, deal size, persona, and tracking changes week-over-week.
On the education front, Jonathan is launching something bold: an entirely AI-generated course in the GTM AI Academy. Everything except his intro video will be created by AI—content, quizzes, everything. "I don't know if it's gonna work, but we're gonna try it and see how it works. I won't know unless I try it."
Summary of Key Points
Conversational data is a moat – Your team's conversations with customers contain unique intelligence that competitors can't replicate. But only if you can structure and activate it.
AI needs clean data to work – All the automation tools in the world are useless without structured, validated data feeding them. Infrastructure matters.
The new workflow: AI comes to you – Legacy SaaS required people to log into platforms. Modern AI tools work in the background, going to your workflow instead of asking you to come to them.
Strategic decisions need objective + subjective – Sales rep insights are valuable, but combining them with data signals creates a complete picture that neither can provide alone.
Prompting still matters – Even with more powerful models, good prompting principles create compounding effects that far exceed generic queries.
Listen to the Full Episode
Want to hear more about Jonathan's journey from sales rep to AI educator, how Momentum.io is rethinking conversational data, and what pipeline reviews will look like in 2026?
