AI-Native Execution Replaces Incremental Tool Adoption as the Defining GTM Capability

Marketing and revenue teams across B2B have been told to use AI, and most of them now use it for something. The problem is that the work that produces incremental gains looks almost nothing like the work that produces real leverage. Companies that treat AI as a productivity boost on top of their existing workflows are getting marginal returns. Companies that treat it as the foundation of how their go-to-market organization operates are running laps around them. The difference shows up in mindset, structural ownership, operational readiness, and the willingness to redesign processes that already work.

Brandon Redlinger is a Fractional VP of Marketing and author of the Stack & Scale newsletter, where he shares AI tactics and tools for modern marketers. He has spent his career across marketing, sales, and revenue operations, advising organizations that range from early-stage startups to scaled enterprises on how to align marketing execution with revenue outcomes. His current vantage point across multiple client engagements lets him see, in real time, which teams are translating AI into structural advantage and which are settling for tools they barely use.

"A lot of what I'm seeing is point solutions or one-off tools with AI capabilities. Very, very few companies that I talk to or even know of are truly AI-first GTM organizations," Redlinger says. In his view, the distinction that separates AI-native organizations from the rest is a thread that runs through every operating decision. 

Someone has to own AI inside the GTM org

The first structural question Redlinger pushes clients toward is ownership. He believes a primary reason AI is not landing as an organization-wide capability is because no one has explicit responsibility for it. He points to Owner.com as an example of a company that's excelling in this area. "Kyle Norton is the CRO there, and he is always on top of AI. He's vibe-coding stuff himself. He's in Cursor and Claude Code. It takes someone with that level of dedication to really build a true AI-first organization."

Most companies, he says, will not get there with executive commitment alone. Redlinger's practical model for closing the ownership gap has two versions, sized to company stage. The first is to hire an AI-native employee whose job is to move across functions, sit alongside operators, and identify workflows ready to be redesigned. The second, for companies that cannot justify the headcount, is to elevate the person already doing the work. "Identify the person that is using AI the most and give them an elevated role. Ask them to document what they're doing every week and share it with the team," he advises. The mechanism matters as much as the appointment. Visibility and incentive structure are what move the rest of the organization. "Part of it is giving someone the spotlight and having others say, 'I want the spotlight, too,' or 'I'm going to get a promotion, too, if I adopt this.'"

Incremental usage is not native usage

The second separator is mindset. Redlinger draws a hard line between people who use AI as a productivity tool and people whose first instinct is to ask whether AI can solve the problem in an entirely different way. "When you have a problem, do you first think, 'I need to solve this with AI'? Or do you try to do some Googling and troubleshoot it yourself? AI-native people always ask how to solve problems with AI first."

In his perspective, the economic difference between those two postures is the gap between 10 percent gains and 10x gains. Using ChatGPT to write blog posts faster is real, but small. Building a tool that previously required outside development and a five-figure invoice is structural. "Previously, when we had an idea, we would spec it out, send it over to development, or hire an outside agency. I once paid $18,000 for an ROI calculator, but I've coded one in just a few hours." The shift from "marketing produces content" to "marketing ships products" is one of the cleanest examples Redlinger sees of native execution. Calculators, configurators, and interactive tools used to belong to engineering. They now belong to the marketing teams that can build them in an afternoon and use them as the centerpiece of higher-value pipeline campaigns.

The hiring implication is direct, and Redlinger has watched it play out across two AI-focused clients with opposite approaches. "One company decided to hire people who have expertise in a specific domain, like enterprise marketing or enterprise sales. The other company said, 'I care more that you are AI-native and you can figure the other piece out.' The one built around AI expertise is doing laps around the other."

AI scales what is already scalable

The third separator Redlinger identifies is operational readiness. AI does not fix broken systems. It amplifies them. "Are your systems ready to scale? Is what you do already scalable? If the answer is yes, that's probably a good time to start looking at AI." If not, he says, AI will only scale the mess faster. He describes a client that's struggling with duplicate data and routing issues. "We can't scale this yet, because if we scale this, things are going to be way worse and we're kicking the can down the road. It's going to be a bigger headache to try to fix this after we apply AI," he shares.

The same logic applies to measurement. Redlinger watches for AI tool access, real usage, and productivity gains on specific workflows, but with caution about Goodhart's Law. Originated in economics, it states that when a measure becomes a target, it ceases to be a good measure because people begin optimizing for the target rather than the intended outcome. The ultimate test, Redlinger says, still sits at the pipeline level. "At the end of the day, are we growing in revenue? Pipeline generated from your fully loaded marketing costs might be better for you than your company-level ARR per FTE."

Fragmentation gets worse before it gets better

The structural risk Redlinger is most concerned about is what happens when every function inside GTM pursues AI independently. The directive to use AI is being absorbed in silos, and the cross-functional collaboration that should make AI a shared operating layer is getting worse before it gets better. "Everyone's so focused on getting more out of one thing. It doesn't matter that you have your own AI initiative over there in content, when I've got mine in ops," he explains. The visible consequence is tool sprawl. Teams buy overlapping platforms because they cannot see what other functions have already deployed. "You've got one tool, I've got one tool. This feature that I don't use, you can actually use. Now we have multiple tools that we're overpaying for because we're not talking to each other."

The fix he prescribes is the same one that every operating problem in GTM has eventually required. Make AI someone's explicit responsibility, hire or elevate for AI-native capability before domain expertise, get the systems clean before scaling them, and design for a shared operating layer instead of letting each function build its own. "The 10x solutions are never about doing things the way you're doing a little bit better," he says. "They're about doing them completely differently."