Why Revenue Teams That Build Their Own GTM Intelligence Win and What They Should Never Build Themselves
What actually gives a revenue team an edge today — better tools, or better understanding of their own data?
Because if you look at where time and budget are going right now, it’s overwhelmingly toward new tools. New AI platforms, new automation layers, new “intelligence” features that promise to make reps faster, smarter, and more efficient.
And yet, for a lot of teams, the outcome is less than stellar. So instead of asking what to buy next, it’s far more valuable to look at what’s working.
In a recent RevTech consolidation event, we sat down with Kyle Norton, CRO at Owner.com, and Jen Igartua, CEO at GoNimbly, to discuss what revenue teams should be building versus buying right now, and where most are getting it wrong.
You can watch the full virtual event here, or keep reading for the key takeaways and how to apply them.
For years, build vs. buy was a clean decision based on (insert). That worked when tech stacks were simpler, and AI wasn’t so (insert). But now, with the rapid pace of tech development, making the decision about what to build versus buy isnm’t always straightforward.
Most B2B teams are trying to answer questions such as:
At the same time, the stack itself is getting harder to control, with Salesforce flows running alongside Clay tables, Gong Orchestrate layered on top of n8n workflows, and internal scripts filling in the gaps as quick builds quietly turn into systems no one fully owns.
Individually, each of these decisions makes sense, but together, they don’t because everything is being built in isolation.
“We have this workflow automation sprawl… you have all of these workflows that sometimes are opposite of each other,” explains Jen.
One workflow updates a field. Another overrides it. A third triggers off of it. And suddenly, no one can answer why it changed. “I go into an account and all of a sudden my account type went from prospect to customer. And I’m like, why? … and now I don’t know where to look,” say Jen.
And unfortunately, this problem only gets worse when AI is layered in, because AI doesn’t fix fragmentation, it accelerates it. Rather than just dealing with workflows, you’re dealing with agents, prompts, automations, and logic being created across multiple systems, often by different people, with no shared governance model.
“The amount of like YOLO API tokens floating around… vibe coded stuff is really significant… there needs to be some control layer,” stresses Kyle.
Speed has never been higher, and you can build almost anything now — but just because you can build something doesn’t mean you should.
So, what’s the solution? Build what makes you different, and buy what needs to be reliable.
“Things that are the core intelligence of how you go to market, you want to build,” says Kyle. “Things that are undifferentiated but require stability, reliability, governance… those are good product categories to buy.”
Before we go any further, it’s worth getting clear on what we actually mean by “intelligence,” because this is where most of the confusion starts.
For the purposes of this article, intelligence does not mean more dashboards, more reporting, or another layer of surface-level insights pulled from a vendor tool. It’s not generic AI outputs, templated messaging, or black-box recommendations that can’t be traced back to how your business actually operates.
Instead, we’re talking about the underlying logic that drives how your team goes to market — the part that’s specific to your customers, your motion, and how you win.
In practice, that looks like:
And this is exactly why the build vs. buy decision matters because this kind of intelligence can’t be outsourced. Vendors can support it, but they can’t define it for you. The logic has to come from your data, your customers, and your experience in the market, which means if you don’t build it, no one else will.
Most teams try to solve this by adding more tools. Owner.com solved it by removing friction.
Instead of asking how to give reps more insights, they started with a much more practical question: where is time being lost, and what’s getting in the way of execution?
One of the biggest friction points was what happens between calls. Reps were spending two to three minutes researching each account before dialing — pulling up context, scanning notes, trying to piece together what mattered. On its own, that doesn’t seem like a major issue, but over the course of a day, it added up.
So instead of trying to optimize that process, they eliminated it. They built their own AI-powered pre-call research using internal data, designed specifically around how their team sells. Not a generic summary pulled from a third-party tool, but something grounded in their customers, their signals, and their motion.
From there, they layered in a connect rate model that prioritized leads based on likelihood to answer, allowing reps to move faster and focus on accounts that were more likely to connect.
Reps no longer had to stop and reset between calls, and they weren’t guessing which accounts to prioritize or what context mattered. The system handled that, which meant they could stay in flow and spend more time actually selling.
“That experiment led to an 87% increase in calls because you took the two to three minutes in between calls where somebody would open something up and scan it and get distracted, to now just compress that time to like nothing.” says Kyle.
As a result, meetings booked jumped from one or two per day to three or four. What’s more, overall efficiency per rep ended up three to four times higher than competitors.
This case study is a prime example of what can happen when your system is built around your intelligence, not a vendor’s, and you use that information to drive real-time decisions in your workflow.
Now that you have a clearer understanding of what belongs in your intelligence layer, and why the build vs. buy conversation breaks down without that distinction, the next step is applying it to your own GTM stack in a way that improves execution.
A good place to start is by stepping back and asking a few simple, but often overlooked questions:
Your answers to these questions will help you surface where your intelligence either isn’t defined, or isn’t being applied so you can be intentional about what you choose to own versus outsource. That said, as a general rule of thumb, you want to build the parts of your system that reflect how your business goes to market, and buy the parts that need to be stable, scalable, and consistent.
In practice, that typically looks like:
Build:
Buy:
The distinction here is about making sure the part of your system that drives outcomes — how you prioritize, engage, and execute — is something you understand, can evolve, and can improve over time.
Revenue teams should build anything that represents proprietary intelligence about their customers, market, or GTM motion. That’s the layer no vendor can replicate, because it’s based on your data, your context, and how your team sells.
At the same time, they should buy the infrastructure that requires stability and scale — things like CRM, data warehousing, and call recording platforms. These systems need to be reliable and governed, not reinvented.
The Intelligence Layer Principle is a simple way to rethink every GTM tech decision. Instead of asking “should we build or buy this tool,” you separate the decision into two categories:
Intelligence should be built and owned internally because it’s what differentiates your business. Infrastructure should be bought because it requires scale, governance, and reliability that vendors are better equipped to handle.
This shift removes the guesswork from build vs. buy by making the answer depend on what the system does, not what the tool is called.
So many revenue teams are getting this wrong because the market is pushing them toward tools, not clarity. With the rise of AI, vendors are packaging more and more “intelligence” into their platforms. At the same time, internal teams are building faster than ever. The result is a mix of vendor logic and internal workflows that don’t always align.
That’s what leads to workflow sprawl where multiple systems make decisions in different ways, with no clear source of truth. Without a clear definition of what should be owned internally, teams end up outsourcing the exact layer that should be driving their advantage.
The impact tends to show up in execution first. At Owner.com, removing just two to three minutes of research time between calls led to an 87% increase in calls per rep. When combined with better routing based on likelihood to answer, reps went from booking one to two meetings per day to three to four.
You see similar patterns elsewhere. Snowflake increased outbound reply rates from 1% to 5% by using proprietary product usage data to shape messaging. Intercom identified a high-risk segment based on support volume and NPS, then built a targeted campaign that generated pipeline almost immediately. Different use cases, same underlying shift — when intelligence is owned and applied, results compound
Start small, and start with execution. Look for places where reps are losing time, making decisions manually, or relying on generic workflows that don’t reflect how your team actually sells. That’s usually where the biggest opportunities are.
From there, focus on building one piece of intelligence that directly improves how work gets done — whether that’s better prioritization, better routing, or better preparation. The goal is to build something that’s effective and gets used.
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