The fastest route to finding alpha isn’t doing more of what already works or what other people are doing. It’s spotting signals before they’re obvious.

That’s exactly how I’m looking at AI search and Answer Engine Optimization (AEO). It’s not just an SEO tweak or an addendum to an existing strategy but a fundamentally new surface for GTM. Platforms like Perplexity, Claude, ChatGPT, and Google’s AI Overviews are shaping a different kind of discovery experience. And when the discovery layer changes, your distribution model needs to change with it.

We’re not treating AEO like a side project. We’re treating it like a potential edge. With no one having fully cracked AEO or having written the defining playbook, that's a sign there is alpha to be found.

Have a Portfolio-Based Approach to Growth

Growth at Ramp is portfolio-based. Some bets pay off next quarter. Others take time to mature. AI search falls into that second category, but it’s the kind of thing that compounds if you get in early and build the system before it’s table stakes.

We run dozens of tests a month with some expected to pay off immediately and others long term bets. But all of them have a risk adjusted outcome we are looking for and there are a few leading indicators I’m watching:

• Do we consistently show up in high-intent AI-generated results and are we cited as an authoritative source?
• Can we trace that exposure back to qualified traffic?
• Are there content formats or structures that repeatably work across engines?

When those align, it becomes less of a test and more of a motion. That’s when we resource it the same way we’d treat any other scalable channel and start to look towards automation.

Our Wins on the Board

We’ve already seen many of our content pieces surface in key answer engine outputs - the kind of queries that show up right before someone hits the demo form. That changes the internal narrative.

It’s not massive traffic. But it’s signal and its growing fast. It tells us we’re structuring the content the right way, and that the engines are starting to index it as useful and authoritative.

When the team sees the traction, that’s the moment you get internal buy-in. Then the org starts paying attention. One win turns into a system.

Generally, we try to keep an eye to the long term. Things that are small today, but have large potential in the future, are more important than small optimizations on already large engines.

Implement a Rapid Experimentation Philosophy

We don’t launch content. We launch experiments.

That’s how we think about AEO and AI search. Everything starts with a hypothesis: "This content type or this structure, should surface in these engines and produce this result." We measure, adjust, repeat.

Not everything works, 90%-plus doesn’t. But if we are learning something in every test, then thats a win. And the team knows the bar isn’t perfection. The bar is velocity of learning. The more we ship, the faster the system improves.

It’s as scientific as possible within the bound of velocity as a first priority. And we document all of it so we don’t relearn the same lessons twice.

Assign Risks To Different Content Types for Experimentation

Yes, early mover advantage matters. But so does credibility. Especially when your brand is showing up next to AI-generated answers. You can’t afford to be sloppy, but you also can’t move so slowly that you miss the window.

The way we balance that is by assigning risk levels to different content types and experiments. Some pieces need to be fast and functional - get it out, see what happens, adjust. Others are more brand-forward or high-risk, and those go through a few more layers of review.

We’re aiming for “structured enough to learn, good enough to trust.”

Why You Need a Cross-Functional Search Team

You can’t treat AEO like a bolt-on to SEO. It touches too many parts of the GTM engine: brand, content, data, product marketing, etc.

The team needs to be cross-functional and technical by default. You need:

• Structured content designers
• Prompt-aware writers
• Analysts who can spot weak signals
• People who know how to ship quickly and iterate without losing the thread
• Growth engineers to maximize our manual efforts

And across it all, everyone needs to have an eye for automation and know what is possible to get the most out of our engineering resources.

As a leader, your job isn’t to micromanage it. It’s to ask the right questions, define clear outcomes, and stay close enough to the work that you can course-correct if needed. I always say: know how to do everyone’s job, but poorly, enough to ask the right questions and step in if needed, but mainly to support the amazing people doing it.

Bake AI Into Your GTM Workflows

AEO is the tip of the spear, but AI is baked into a lot of our GTM workflows now.

We’re using it to speed up copy iteration, surface insights from campaign data, automate internal processes, and explore how agents interact with our brand.

The tools help with throughput, but they also unlock strategic clarity. They help us test faster, fail smarter, and personalize more effectively.

This is not short-term edge, it’s becoming foundational. And the orgs that combine technical leverage with human judgment are the ones that are going to win the next cycle.

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George Bonaci is the VP of Growth and Demand at Ramp. He is 2x founder, marketer, and angel investor. He has extensive marketing experience, building orgs that supported growth from $0 to >$650M in ARR. He was a chemist in another life.