AI-referred demand is up 1,850% at HubSpot. The person who saw it coming in 2022 — before most of the industry was paying attention — explains what she did about it.
In late 2022, before most marketers had worked out what to do with ChatGPT, Aja Frost was already making the case internally at HubSpot that large language models were about to change how people find software — and that HubSpot needed a strategy for it before the shift became obvious.
That pitch turned into a cross-functional initiative spanning growth, product, engineering, brand, and communications. The result: HubSpot became the most visible CRM in LLM responses, with citations up more than 4,000% and AI-referred demand up over 1,850%. It also led to HubSpot’s acquisition of Xfunnel and the launch of the HubSpot AEO Grader, the first free tool designed to help companies understand their visibility in AI answer engines.
Frost is now HubSpot’s Senior Director of Global Growth and Paid, leading the teams responsible for top-of-funnel demand through SEO, LLM optimization, and paid media. In this conversation, she talks about why the website has become the last stop — not the first — on the modern buyer journey, what it actually means to give algorithms more control without handing over strategy, and how a company that sells marketing software to marketers is thinking about AI automating a significant chunk of what those marketers do.
Excerpts from the interview;
Your role sits at the intersection of growth, paid, and AI. What does your focus look like today?
My team owns top-of-funnel demand for HubSpot, with a focus on paid, SEO, and AEO. We sit at the intersection of Marketing, Sales, Analytics/Ops, and Product. Right now, a significant portion of my team’s time is on answer engine optimization (AEO) — making sure HubSpot shows up in the answers buyers are getting from ChatGPT, Gemini, and Perplexity — and turning what we’ve learned into a playbook our customers can use. Part of that work was recognizing there was no good way for marketers to see how the AI search landscape was shifting, so we built HubSpot’s AEO Sensor, a free tool that tracks AI visibility, citation, and traffic trends by industry. This helps people understand whether their strategy is working or the underlying models are changing.
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How has HubSpot’s growth playbook evolved from the inbound era to the age of AI search?
For years, inbound marketing was the playbook. It worked because buyers were searching on Google, and we met them there. Now, people are going to ChatGPT or Gemini, having an in-depth conversation where they identify a problem, evaluate solutions, make a shortlist, and then, and only then, go to your website. The website has gone from an early stop on the buyer’s journey to the final one. In response, HubSpot has stopped targeting high-volume educational keywords and started building visibility in answer engines. We’re getting less traffic, but it’s much more valuable: Customers who arrive after doing their research in an LLM convert at about 3x the rate of traditional search visitors. They’re pre-qualified and ready to purchase.
How do you scale paid growth globally without losing local relevance?
When going global, marketers typically run a single playbook everywhere or fully decentralize, letting each region do its own thing. Neither works. We use a global operating system that includes shared segmentation, KPIs, and a shared narrative. Execution is local: we adapt channels, messaging, and offers to each market’s buying behavior and digital maturity. Lastly, we test before we expand. A campaign earns its way into new markets based on data, not assumptions about what should translate.
Paid media has changed dramatically. How has HubSpot adapted, and what’s working today that wasn’t two years ago?
Paid got harder when cheap targeting stopped being reliable. iOS changes, cookie disruption, and rising CPMs have pushed the industry away from easy scale. We’ve had to get much more disciplined about where paid adds value — and much less dependent on third-party signals. First-party data and well-built intent signals are far more impactful now than broad reach. That has also changed what we optimize for. Traffic volume is no longer the North Star. We care more about visibility, branded demand, conversion rate, pipeline quality, and revenue because they are better indicators of whether we’re actually influencing buyers throughout a fragmented journey.
In practice, that means paid is less about buying broad top-of-funnel traffic and more about amplifying strong signals and strong creative around higher-intent destinations. What’s working now that probably wouldn’t have worked two years ago is this combination of first-party precision, off-site amplification, and integrated paid support. The channels and content types that drive AI citations, such as YouTube, newsletters, podcasts, and Reddit forums, also happen to be where buyers spend time. Today, 90% of HubSpot’s leads come from non-blog sources, with YouTube leads up 100% and newsletter leads up 90%. The paid strategy follows the same logic.
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How much control should marketers give AI-driven ad platforms—and where do you draw the line?
We’re giving algorithms more room than we used to, but we aren’t handing over the strategy. The most important parts are still in human hands: who we want to reach, what counts as real intent, what message we want in the market, and how we judge success. The machine can optimize delivery, but it shouldn’t define the objective.
Automation is worth it when it operates within strong guardrails and leverages first-party data, explicit intent signals, and strong creative. You’re giving up tactical control, not strategic control, and that’s only worth doing when the algorithm is paired with strong inputs and rigorous measurement. If you’re using automation with weak data or vague goals, it’s just spending efficiently against the wrong objective.
Where has AI delivered the biggest measurable impact across HubSpot’s growth operations?
AI isn’t something my team does off to the side; it’s part of our day-to-day operating model. On the creative side, we use AI to generate creative and scale testing, produce search ad variations, and make sure assets are on-brand and speak to our persona before they go live. Our internal heuristic is: use AI to go faster, but keep a human in the loop. We’re also using AI in optimization and execution, including value-based bidding, dynamic personalization, and A/B testing at scale. The results are pretty incredible: AI-referred demand is up 1,850%, email personalization has driven an 82% improvement in conversion rates, and 94% of HubSpotters use AI weekly.
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As AI automates more marketing work, how is HubSpot redefining the marketer’s role?
AI is automating many traditional marketing activities, including content production, campaign setup, reporting, and personalization. But internally and across our customer base, we’re seeing demand for marketing “judgment” or taste rise. AI raises the ceiling on what a marketing team can do — it doesn’t lower the floor on strategic thinking. At HubSpot, 94% of our team uses AI weekly. We’re not asking whether to use it; we’re asking whether the output is driving outcomes. Building on that, we don’t tell customers that AI replaces the marketer. We’re showing them how to use it to do more of the work that matters.
