Andrew Higgins is CEO of Parsnipp, an AI search marketing company.
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Marketers have worked hard to get their brands mentioned in AI-generated answers. Answer engine optimization (AEO) and generative engine optimization (GEO) have largely focused on whether your brand shows up.
Visibility matters—if you never show up, everything afterward is irrelevant. But we’re treating AI search like traditional search, and our data suggests it behaves differently. Being mentioned doesn’t necessarily mean being recommended. We’re finding brands with phenomenal visibility that fall out of the recommendation set as a conversation continues.
That’s the messy middle of AI search, and marketers aren’t paying enough attention to it.
AI Search Is Consolidating The Marketing Funnel
I don’t think AI search is changing the traditional marketing funnel. It’s just owning more of it. Historically, the customer journey spanned different channels. Marketers optimized for visibility in Google search, directed people to websites and landing pages and used conversion rate optimization to move them toward a purchase.
In traditional search, visibility was effectively the recommendation. If you showed up in Google’s first 10 links, you were being recommended. Customers clicked through, evaluated options and searched again as their needs evolved.
AI search works differently because much of that refinement happens within the same conversation. People ask follow-up questions, introduce new parameters and change their minds based on the information presented. A car shopper might narrow options by price, features or intended use. All that discovery and consideration is taking place between a human and an AI assistant, potentially within one conversation.
Yet we’re still measuring visibility like it’s the win because historically it was.
Visibility Doesn’t Equal Recommendation
This is where I think marketers are looking at AI search incorrectly.
In research involving 320 simulated shopper conversations, we found that nearly three in 10 brand mentions never became recommendations. Instead of checking which brands appear in predetermined prompts, we’re simulating conversations across the buying journey.
Brands that show up first aren’t necessarily those that show up last. And ultimately, when we’re talking about selling goods and services, that’s what matters. Questions during consideration differ from those during discovery. Evaluating premium versus price-point products can change which brands remain in the conversation.
We’re finding that a lot of luxury and premium-priced products fall out of consideration because AI assistants tend to eliminate them when shoppers introduce price parameters. Consider someone shopping for a vehicle. They might initially say they don’t want an expensive car. With a price constraint, AI may eliminate premium options before customers understand their value.
But what if that more expensive vehicle has four-wheel drive? Does the customer regularly travel to the mountains? Do they ski or visit a family cabin? Those are the kinds of conversations luxury brands have been having with consumers forever. They’re opportunities to demonstrate why a product might be worth paying more for. Now, those conversations are increasingly being handled by AI assistants, and brands risk losing the opportunity to communicate their value.
That’s why marketers need to think beyond the initial prompt response. Mentions aren’t enough. You need to understand what happens during follow-up questions. What causes your brand to remain in consideration? What causes it to disappear?
If you’re not finding ways to help AI answer those questions, you’re potentially losing customers before they ever have the opportunity to evaluate your product.
Paid Advertising Is The Next Challenge
As AI search becomes a more sophisticated channel, paid advertising will become increasingly important. You can’t just create organic content forever and expect to reach every customer at every stage of their journey. As conversations become more detailed, brands will need ways to reach specific customers with relevant messages.
Today, the biggest challenge with AI advertising is targeting. Traditional search advertising developed around keywords, customer intent and increasingly sophisticated targeting capabilities. Replicating that precision within an evolving AI conversation is difficult.
Marketers need to understand who their customers are, what they’re looking for and how to reach them at different stages of consideration. I don’t think targeting will remain the biggest challenge forever, but it’s something marketers need to start thinking about, testing and preparing for now.
Agentic Commerce Must Close The Loop
If recommendation matters, then the transaction matters, too. There’s a lot of anticipation around agentic commerce, but we’re still early. Presently, technology and consumer behavior are holding it back.
On the technology side, competing protocols mean we don’t yet have the basic infrastructure to make transactions incredibly easy across the internet.
Consumers are also becoming more comfortable using AI assistants for awareness and consideration, but allowing those assistants to complete purchases represents another behavioral shift. We’re seeing demonstrations of AI assistants buying things like pizza, which is interesting. But moving from those demonstrations to AI acting as a personal shopper across a meaningful percentage of transactions is another step. That wave is coming. It’s just not mature enough yet.
In the meantime, brands need to consider whether AI assistants can access the information required to complete a purchase, including sizing, availability and inventory. If shoppers make it all the way through consideration but can’t buy, the journey remains incomplete.
Start Optimizing For The Entire Journey
We’ve put most of our energy into figuring out what we can do to show up in an AI-generated response. Some of those tactics will have longevity. Others are growth hacks. But if we aren’t thinking about AI search as a full-funnel channel, we’re missing a significant piece of the opportunity.
We need to present our brands to AI in intelligent enough ways that we’re arming these assistants to be good sales advocates, from initial awareness through consideration and eventually purchase. This is how we were trained as marketers to think about the customer journey. We’ve simply gotten accustomed to optimizing each stage separately. AI is bringing more of those stages together.
The goal was never just to get mentioned. It was to turn awareness into business results. And if we’re only optimizing for that first interaction, we’re not necessarily getting where we think we’re going.
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