VP of Marketing Shares How She’s Shifting From An Off-the-shelf Marketing Stack To An AI-native Operating System
Discover how a VP of Marketing replaced a traditional martech stack with AI-driven workflows to accelerate execution, improve targeting, and drive revenue-focused decisions.
Emilia Korczynska
VP of Marketing at Userpilot
Revenue Engine: Emilia Korczynska focuses on transforming marketing into a measurable revenue-driving function at Userpilot.
AI Transformation: Userpilot's marketing strategy integrates AI to enhance precision, accountability, and streamline operations.
Account-Based Marketing: The shift to account-based marketing has led to highly targeted engagement via platforms like LinkedIn.
Marketing Automation: Automations have improved efficiency but require careful evaluation to ensure they drive meaningful outcomes.
Data-Driven Decisions: AI informs budget allocation and content prioritization based on performance metrics, enhancing strategic focus.
Emilia Korczynska is the VP of Marketing at Userpilot, a product growth platform. She is also the founder of ZenABM.
We sat down with Emilia to learn how she's turning an off-the-shelf marketing stack into an AI-native operating system. Here's what she told us.
Creating a measurable revenue engine
I’m Emilia Korczynska, VP of Marketing at Userpilot, where I focus on turning marketing from a cost center into a measurable revenue engine.
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I lead a relatively lean marketing organization structured around core growth functions rather than traditional channel silos. The team spans product marketing, demand generation, content and SEO, marketing operations, creative, and outbound support. It supports both product-led growth and account-based marketing, so we cover the full journey from self-serve acquisition to high-intent, sales-assisted conversion.
In terms of channels, ABM is our primary acquisition engine. We built the team and systems to drive account-level engagement and pipeline through highly targeted campaigns, particularly on LinkedIn. Organic complements this as our second major lever, where content plays a key role in capturing and nurturing intent, followed by Google Ads to capture high-intent demand and close the loop on acquisition.
Because the team is intentionally lean, we operate very cross-functionally. Content feeds directly into demand generation and ABM, marketing ops connects all the data and attribution, and we measure everything against pipeline and revenue rather than channel-specific metrics.
While the team is small, the strategy is quite sophisticated.
Fundamentally changing how marketing operates
My journey into marketing leadership has been hands-on and deeply tied to experimentation. I didn’t come up through traditional brand marketing – I grew through product-led growth, performance, and ABM, where the expectation is simple: if it doesn’t drive pipeline, it doesn’t matter. At Userpilot, I’ve led the shift from broad demand generation to highly targeted, account-based strategies, especially on LinkedIn, where we’ve had to rethink everything from audience building to attribution.
A key part of that journey involved challenging broken measurement models. I’ve spent a lot of time proving that a huge portion of B2B revenue — often over half — is invisible if you rely on contact-level attribution. That also led me to build ZenABM, a startup where we track marketing impact on pipeline at the account level and connect ad engagement directly to pipeline and revenue.
So, that's the foundation, and it naturally led me into AI.
The shift we’re seeing isn’t just about generating content faster — it’s about fundamentally changing how marketing operates. We’re moving from dashboards and manual analysis to systems where you can query your data, automate insights, and trigger actions instantly.
For me, this moment of AI transformation is less about hype and more about leverage. I focus on these questions:
- "How do we use AI to make marketing more precise, more accountable, and more connected to revenue than it’s ever been before?"
- "And how do we build systems where data, insights, and execution are no longer separate steps, but part of one continuous loop?"
That’s the journey I’m on right now.
How a marketing stack can become an AI-native operating system
One of the biggest changes we made was changing our off-the-shelf marketing stack into a more bespoke, AI-native operating system, with GitHub as the central “marketing brain” and Claude Code as the team's interface.
Instead of relying on disconnected tools and manual workflows, we moved execution into structured, repeatable AI workflows. We’ve built reusable processes for LinkedIn content generation, landing page creation, and competitive intelligence, orchestrated through cloud-based agents and skills.
As a result, we’ve significantly shortened the time from signal to action. Marketing is now far more systematic and scalable, and the team spends less time on execution and more on strategy, inputs, and optimizing outputs.
How AI can overhaul outbound
The most impactful shift has been in outbound. We built an intent-led outbound system that identifies engaged accounts from our ABM programs using ZenABM’s API, feeds the data into a custom cloud database, enriches it, and then prospects these accounts using our ICP criteria via Prospeo.
From there, we activate outreach through tools like Smartlead and HeyReach, allowing us to run highly targeted, personalized campaigns at scale based on real engagement signals.
What used to be a manual, fragmented process across multiple tools is now a connected, signal-driven system.
How AI informs marketing decisions and human creativity
We rely heavily on AI for data-rich, repeatable decisions that benefit from pattern recognition at scale…But we deliberately keep human involvement in areas requiring taste, context, and originality.
Overall, we rely heavily on AI for data-rich, repeatable decisions that benefit from pattern recognition at scale.
For example, AI increasingly informs budget allocation using performance and revenue attribution data. Instead of manually shifting spend based on lagging indicators, we use systems that analyze which campaigns and accounts drive pipeline and revenue, and adjust investment accordingly.
This also applies to SEO. We use AI to prioritize content based on signals like click-through rate, search volume, and predicted traffic and engagement. This allows us to focus on opportunities with the highest expected return, rather than relying on intuition or static keyword lists.
But we deliberately keep human involvement in areas requiring taste, context, and originality.
Design decisions are a good example; what “looks right” for a specific audience, brand, or moment still relies heavily on human judgment. The same applies to events and conference strategy, where understanding nuance, relationships, and brand perception matters more than pure data.
On the content side, it’s a hybrid. AI effectively generates variations, structures, and even ad ideas, but core demand gen angles still come from creative research and a deep understanding of the audience. That’s where the differentiation happens.
Why automations must justify themselves
The biggest positive impacts are obvious: efficiency and scalability.
Automating manual workflows has saved the team significant time in everything from content production to outbound execution. This directly translates into output: we can run more experiments, launch faster, and operate with a smaller team than previously required.
AI has also improved decision-making speed. With AI analyzing performance and attribution data, we can move much faster from insight to action, especially in areas like budget allocation or campaign optimization.
However, over-automation is a big risk.
It's easy to spend time building and optimizing workflows that are operationally interesting but don't drive meaningful business outcomes. In some cases, system outputs aren't strong enough to justify the effort, especially if they lack the necessary nuance or quality.
So, that's my advice: Don't automate for the sake of automating. Remain disciplined about where AI creates real leverage versus where it merely adds complexity.
Follow along
You can follow along with Emilia's work on LinkedIn. And check out ZenABM.
More expert interviews to come on The CMO Club!
