Tools that once felt essential are being absorbed into something bigger. As RevTech consolidates, build vs. buy no longer goes far enough.
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Drift sold for $1.1 billion in 2024 and no longer exists as a standalone product. And it’s not the only tool on your stack that’s quietly being absorbed into something bigger.
AI is reshaping how B2B teams build, buy, and run go-to-market systems. Entire categories are getting compressed. Point solutions are being absorbed into larger platforms. And tools that once felt essential are starting to look interchangeable.
That raises a much bigger question than which tool to buy next. What parts of your go-to-market system do you actually own? Because when the platforms you depend on start consolidating, the difference between owning your system and renting it becomes impossible to ignore.
In this article, we’ll break down what’s fueling the current consolidation wave and why the traditional build-versus-buy debate no longer goes far enough.
In particular, we’ll explore:
Let’s dive in!
The consolidation happening across the RevTech landscape isn’t a single trend. It’s the collision of three forces hitting at the same time:
“It’s a bit of a perfect storm and you can’t pinpoint only one thing, but it’s multiple trends and factors all converging that I think is driving such rapid action,” says Kyle Norton, CRO at Owner.com.
And this isn’t just happening on the vendor side. Buyers are now planning 12 months or more ahead, factoring in seat compression and pricing pressure before it even shows up in their own stack. That forward-looking mindset is accelerating consolidation decisions inside companies, not just across them.
You can see this clearly in how leaders talk about recent acquisitions. According to Kyle, when Vista acquired Salesloft and Drift near the top of the market, the founders “nailed the timing.”
The bottom line? This consolidation wave isn’t bad news for revenue leaders who move early. Many of the tools being absorbed or sunset were solving problems that are now either commoditized by AI or better handled internally. What looks like disruption is also an opportunity to remove layers of your stack that were never delivering full value in the first place.
As tools consolidate, the next step is to define the role each one plays and where it belongs in your GTM system. At a practical level, every tool belongs in one of three places:
The most expensive mistake revenue leaders make is buying Intelligence layer tools from vendors who want to own that logic. The moment your prioritization, messaging, and account strategy live inside someone else’s platform, you lose the ability to differentiate your GTM motion.
You can see this play out in how teams are rethinking their stack. As Kyle describes it, the decision to move away from Gong wasn’t about features. It was about control. Gong operated as a “walled garden,” where synthesized call insights couldn’t be written back into specific Salesforce fields. That meant the intelligence was usable, but not operationalizable.
And if you can’t operationalize it, you can’t build on top of it.
That’s the shift most teams are still catching up to. The question is no longer which tool gives you insights. It’s whether you own the system those insights feed into.
“I’ve gone from fairly bearish two years ago, thinking this is the time that Salesforce is finally gonna get disrupted,” says Kyle. “And now I’ve become a Salesforce bull because actually I think that’s the core centerpiece technology that everything can plug into.”
Your GTM Intelligence Layer is the encoded logic of how your company finds, engages, and closes customers. It’s the only part of your stack where building internally creates a compounding advantage, not just a cost tradeoff.
Pre-call research, lead prioritization, and outreach personalization aren’t features. They’re decisions. And those decisions depend on your understanding of your customers, your product’s value drivers, and your market context. None of that is transferable to a generic model.
That’s why this layer can’t live inside a single tool.
The intelligence you build needs to travel. The same context that powers a BDR’s pre-call brief should show up in an AE’s discovery prep, a CSM’s renewal strategy, and an onboarding handoff. If it’s locked inside one platform, it breaks the moment your workflow spans across another.
You can see the downside of that in something as simple as outbound email. “I don’t like people using Gong’s email templates to go out,” explains Jen Igartua, CEO at Go Nimbly. “I receive those and I know that you didn’t write them.”
Personalization and human touch still matter, and always will, because buyers can tell the difference. When outreach feels templated or generic, it gets ignored. When it reflects a real understanding of their business, it earns a response.
Jen points to an example from Intercom, where teams use their own product usage data to drive highly specific outreach. “They looked at their customers who were seeing an increase in support cases and growing support teams, but their NPS scores were going down.”
“So you can reach out and say, ‘Hey, your team is growing and your volume is going up, but your customers are feeling that pain. Can we talk about Fin AI and how it can automate case management?,” explains Jen. “The amount of pipeline they created with that campaign was wild,” she adds. “They really understood their customer and used data that only they have to book that next meeting.”
That’s what Intelligence Layer thinking looks like in practice. It’s not just personalization for the sake of it. It’s applying your unique data and point of view in a way that makes your outreach feel specific, relevant, and hard to ignore.
The biggest operational risk in an AI-native revenue stack isn’t a data breach or a bad vendor decision. It’s the accumulation of conflicting automations built across five different platforms by five different people, with no governance layer connecting them.
Automations get created everywhere. Each one works on its own, but together they create a system no one can fully trace. The outcomes include:
“We have this workflow automation sprawl,” says Jen. “It’s democratized and everybody’s DIYing. And now the issue that we’re starting to feel the pain of is how do you manage all of these workflows that sometimes are opposite of each other?”
The risk isn’t just operational, it shows up in access as well. An agent connected to Gong with admin credentials can surface private call recordings to anyone who knows how to ask. Not because of a breach, but because permissioning wasn’t designed with that use case in mind.
At the same time, the rev ops function is being asked to perform product management work, including uptime ownership, user acceptance testing, bug triage, and feature iteration, without the product management training, resourcing, or mental models to do it safely at scale.
You can see the gap in how these systems behave. A workflow works for one user, then breaks when rolled out broadly. As Jen points out, even something like a quoting tool can become inaccessible once multiple users rely on it. “We just don’t talk about uptime in rev ops.”
Companies operating at the forefront of AI will need GTM tech architecture roles much earlier. The governance required to run this kind of system is a distinct function, not an extension of rev ops. And the teams moving fastest on internal builds are also accumulating the most technical debt. The advantage shifts to the teams that put governance in place early, before the system becomes too complex to manage.
When you build the Intelligence Layer correctly and pair it with disciplined enablement, the productivity gains are not incremental; they’re structural, and Owner.com’s outbound results demonstrate exactly what that looks like in production.
Owner.com built an AI Pre-Call Research (AIPCR) tool that distilled publicly available restaurant data into three specific inputs for every call:
During the pilot, reps weren’t allowed to open external tabs, which removed the distraction between calls. The goal was to trust the synthesized insights and stay focused on the conversation.
“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. Just read AIPCR, dial, and talk,” says Kyle.
Stacking AIPCR with a connect rate routing model that predicts which leads are most likely to answer more than doubled decision maker connect rates. Combining both systems more than doubled pipeline per outbound rep. The results were not tied to a single tool, but to how the Intelligence Layer was applied across the workflow.
The enablement component was as important as the technology. Kyle’s team ran ride-alongs where rev ops and applied AI team members sat alongside reps, observing workflow behavior, identifying friction points, and iterating on the tool in real time. That is product management discipline applied to GTM execution, and it is the difference between deploying a tool and actually changing behavior.
The takeaway? Less is more when it comes to information. The gain came from removing the option to research, not from providing a more comprehensive research tool. Most teams are optimizing in the opposite direction by adding more data instead of reducing it to the few inputs that actually drive action.
“The rev ops team of the future needs to look really different than it does today,” insists Kyle. “And it’s not about building tools and writing custom fields and simple workflows. The possibilities are so much greater now that rev ops teams can impact because they can build tooling themselves.”
The rev ops team that will drive competitive advantage in 2026 and beyond is defined by how well it can build, govern, and evolve an AI-native system.
Rev ops now needs to operate with roadmaps, user research, UAT protocols, uptime ownership, and iteration cycles. The work doesn’t stop at deployment. It extends into how tools are used, where they break, and how they improve over time.
That shift changes the shape of the team. Senior talent becomes more valuable while junior, execution-heavy work gets automated. Fewer people will do more of the thinking, design, and decision making that drives how the system operates.
The team shifts toward fewer people who can make architectural decisions, manage tradeoffs, and own how the system holds together.
At the same time, GTM tech architecture is separating into its own function. Larger companies have had business systems teams for years. The difference now is timing. That architectural layer is showing up earlier, often at Series B or C, because the complexity of an AI-native stack arrives much sooner.
You can see what this looks like in practice at Owner.com. Their internal tool, Cerebro, uses Claude to generate Salesforce objects, workflows, and fields from natural language inputs, then deploys those changes to sandbox for UAT before pushing them to production. The impact is measurable, with roadmap delivery compressed by more than 2x on complex builds and up to 4x to 5x on simpler ones.
Ultimately, the teams that fall behind won’t be the ones that ignore AI. They’ll be the ones that adopt it without the governance, senior talent, or product discipline to manage what they’ve built. And the rev ops leaders most excited about building their own tools are often the ones most at risk, because building changes what the role requires. The work moves away from configuration and toward architecture, from execution to ownership.
Applying the GTM Intelligence Stack Framework doesn’t require a platform migration or a new vendor. It requires a shift in how you think about your stack, and that shift can start immediately.
Here’s what to do in the next 30 to 90 days:
This framework replaces the build vs. buy debate and gets to a more important question: which layer does this tool belong in, and who should own it?
Infrastructure belongs to vendors with the governance to manage it and intelligence belongs to you, because it’s the encoded logic of how your company understands and engages its market.
The teams that act on this now will turn this shift into an advantage. The ones who wait for the vendor landscape to settle will find that the decision has already been made for them.
Which camp do you want to be in?
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