The Marketer Who Builds the Machine
Transformation: Lusha's marketing restructuring emphasizes systems thinking, bridging creative execution with effective decision-making.
Builder Focus: Yael Abukasis advocates for marketers to act as 'builders,' creating automated systems, not just campaigns.
GTM Cohort: Expansion of 'GTM builder' user base led to increased revenue, prompting a team and strategy overhaul at Lusha.
AI Integration: Effective AI use depends on a cohesive system; fragmented data leads to inefficient AI application.
Hiring Insight: Abukasis values curiosity and adaptability, emphasizing builders who automate repetitive tasks and explore new tools.
What would you do to earn better LTV, and stronger retention? For one team, it was became possible with a shift in how marketing needed to function. Yael Abukasis, CMO and Head of Revenue Growth at Lusha, a go-to-market data and intelligence platform, approached marketing with a line of thinking. This enabled her team of GTM builders to boost company revenue by 21% in roughly one quarter.
So if the gap between what your team produces and what AI-native competitors are shipping keeps widening, read on.
When she joined the company, the marketing organization was executing well by traditional measures. But then she notice something.
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"As we scaled campaigns, efficiency began to decline even though the team was executing at a high level," she told me. The problem wasn't effort or skill. It was architecture. The team was equipped run campaigns. But nobody had built it to design the systems that make campaigns work, adapt, and compound over time.
What followed was a nine-month restructuring that changed the composition of her team, along with 16% ICP growth year-over-year while cutting paid media spend by 50%. Those numbers point to more than a strong creative strategy, they're the result of building better machines.
Shaping The System
Lusha started as a core data product offering accurate, verified and compliant B2B contact and company data for sales teams, through a platform and a Chrome extension.
She says, the product was excellent. But then AI arrived, their persona evolved, and they evolved.
Today Lusha is the deep data intelligence layer for go-to-market builders, based on machine learning, helping them reach the 5% of customers in-market for their solution based on their context and real-time buying signals, available wherever builders work—whether that's Clay, Claude, ChatGPT, or through their API and MCP.
That's what gets me out of bed. Building the infrastructure that makes modern GTM possible, while also running a team that has to practice what we preach. We use our own product to run our own marketing every day.
Abukasis remembers the moment she realized her team was executing at a high level and still losing ground.
And as Lusha scaled, efficiency kept declining, and it wasn't due to a lack of effort or talent. She admits that, "marketing has shifted from a purely creative discipline to a decision-making discipline."
Everything kept pointing to the roots beneath, so Abukasis took a step back to re-evaluate.
The traditional silos of content or growth don’t work if they’re not integrated into a system that identifies and prioritizes demand in real time.”
The questions she asked her team were simple. Do you know who you're targeting? When are you reaching out to your audience? And, how decisions are being made?
What Abukasis calls the shift to a "builder-first" culture is more than a rebrand of the same old roles. It's a fundamentally different idea about how marketers should operate.
Running a Campaign vs. Building the Machine
This realization prompted a nine-month restructuring of her team, not a reorganization chart exercise, but a fundamental rethinking of what kind of people marketing actually needs right now.
The answer she landed on is to implore her team to become builders. Abukasis highlights the difference between someone who runs a campaign and someone who designs the system that powers one. "A builder is a system architect who happens to sit in marketing," she says
In a traditional setup, you have specialists who stay in their lanes — copywriters who write, performance managers who manage campaigns, and the ops person who fixes the lead routing. A builder bridges that gap.
A builder asks different questions. Instead of "what's the message?" it's "what's the trigger?"
Rather than "who's the audience?", it's "what is the data enrichment logic, and how does the feedback loop improve the next campaign?" Then, they look at what their team does manually every week and ask how to automate it.
Abukasis shares that she lead with requests like, "I don't want you to work on a brief. I want you to build a machine that makes marketing happen at 10x the speed."
Some of the most tangible examples of this at Lusha aren't campaigns at all. Her team built a system that monitors competitor and industry websites for UI changes, pricing page updates, and A/B tests that never get publicly announced. They automated brand monitoring. And now, they're currently developing an end-to-end campaign automation system that takes human input and produces a fully deployable campaign including copy, creatives, ad assets formatted per platform all in a single workflow.
She admits that, "what used to take days of back-and-forth between teams now happens in one workflow. The talented humans on my team aren't doing less because of this."
They’re doing more of what only humans can do in using their taste and judgment, adding a human touch, monitoring the machines, knowing when something’s off, and deciding what to scale.
The Signal Guiding Her Decisions
The structural shift didn't come from a top-down mandate. It came from watching a new type of user show up inside Lusha's own platform.
At the end of 2025, what Abukasis and her team call "GTM builders", RevOps leaders, marketing ops professionals, founders building revenue agents from scratch, made up roughly 1% of their user base.
By the end of Q1 2026, that cohort had grown to more than 10% of new sign-ups and, represented 21% of company revenue. They showed higher net dollar retention, better lifetime value, and significantly stronger retention than any other user segment.
Once we saw that pattern, we had to ask whether our team structure was built to serve them. It wasn't. So we rebuilt it.
The second signal was more personal. Abukasis started noticing her own team members building small automations on the side, solving their own friction points with tools they'd taught themselves.
"I built my own automation. It was so easy that I built two things that solved massive pains for me in one day and felt like I had superpowers." That kind of grassroots energy told her where ambition was sitting untapped across the organization.
So whether you're thinking about this from a product perspective or a hiring new headcount, it's worth spending some time on.
The implication is that the people who help to design and define the systems can produce compounding returns.
What Restructuring Looked Like
The shift took nine months and required a reframe siloing content, revenue growth, and marketing operations from one another.
Today, Abukasis leads both marketing and revenue growth together. Her team built a dedicated operations engine under their director of marketing operations, specifically designed to own the tech stack and data flows.
Revenue growth folded directly into marketing rather than sitting adjacent to it. And, every team member receives access to pro versions of AI tools, which encouraged to build their own automations and agents.
"The first three months required heavy adjustment. By month six, consistency emerged. By month nine, the model was fully embedded."
She admits the human aspect of this timeline is more complex.
The thing nobody tells you about change management is how much of it is emotional. You can have a perfectly logical plan and still lose people in month two because they can’t see where they fit in the new picture.
Her solution was to make sure every person on the team could answer a simple question—what does this mean for me and my work?
She also rebuilt the channel mix almost entirely. The GTM builder persona doesn't respond to broad creative campaigns. "They live in professional communities, in Slack groups, in newsletters, and in partner ecosystems." So Lusha added budget to partnerships and affiliates and launched Lusha Campus, a learning environment where builders come to understand how to get real value from the data before ever becoming paying customers. Her team found that when people completed Campus they activated faster, retained better, and expanded more readily.
Where Most Leaders Get This Wrong
The failure mode Abukasis sees most often is marketing leaders layering AI on top of a broken foundation. It's been a common thread among leaders I've spoken with.
Of course, "AI reflects the quality of the system it's plugged into," she says. "When data is fragmented and targeting is broad, AI just helps you move faster in the wrong direction." Lusha experienced this directly. Stronger results only followed once inputs and logic were fully aligned.
For leaders operating within constraints, an existing team, fixed headcount, locked-in budget, you simply have to start somwhere.
Pick one workflow that matters and rebuild it end-to-end. Between Lead routing, ICP targeting, data enrichment, she says to just choose one. Bring marketing, RevOps, and data into the same room.
Define the logic, clean the inputs, automate it completely. "When one system works cleanly, the conversation about going further funds itself."
Her data supports the approach. After rebuilding targeting logic around a tighter, signals-based ICP, Lusha drove 16% ICP growth year-over-year while cutting paid media spend by 50%.
The story behind that number started with a question one of her team members asked during a planning session: "Do we actually know which company profile closes fastest?" They didn't have a clean answer. So they built one, mapping closed-won deals against firmographic and signal-based attributes until they found a profile that was dramatically more efficient than the broad ICP they'd been targeting. Then they rebuilt everything around it.
The team that built that analysis became the team that now runs our targeting logic, continuously — not as a quarterly exercise. The shift, from campaign thinking to system thinking, is the actual win.
What's Most Leaders Aren't Ready For
Even as Abukasis talks about the work her team has done, she's already focused on the next problem. And it's one most marketing organizations haven't grappled with yet.
AI agents are proliferating inside enterprise buying processes. IDC projects a billion of them running inside enterprises by 2029. In many purchasing workflows, the first entity to evaluate your brand won't be a human at all. It will be a system making eligibility determinations before any person sees your creative.
"The first entity to vet your brand in many buying processes is already a system that doesn't read your blog for inspiration — it queries your data schema to check if you're verified, structured, and compatible," Abukasis said. "If your data is fragmented and your API documentation is a mess, a human buyer may never even see your creative because an agent already filtered you out."
Building for the machine layer makes it possible for her team can focus on the human layer. The two aren't competing priorities. They're sequential ones.
This long way from worrying about ad copy. And it's why the marketers who understand systems, who think like builders —are the ones with work to do that won't be automated away.
What’s Next?
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