This image is intended to provide levity. The conversation with the printer in question was entirely friendly and positive.  

After last week's column on AI search optimization, the author received a phone call that every writer should hope to get. Not because the caller agreed with the conclusions of the article, but because he didn't.

The caller was the president and CEO of a forward-thinking, marketing-focused franchise print location. His company tracks “everything,” including AI referrals. According to his analytics, the traffic coming to the company's website from AI tools is higher than average. That alone makes them ahead of most of the industry.

But here's where he pushed back. He wasn't seeing evidence that AI referrals were producing higher-margin or more complex jobs than any other marketing channel. Thus, he was right to question the conclusion.

So…what happened?

The Assumption Behind the Argument

First, it’s important to embrace pushback. All of the data is early, and even those companies flooding your email inbox with promises of AEO analyses and top-tier results are still trying to figure it out. Pushback and contrary experiences are all data points we factor in as we learn.

Second, the premise that AI search lends itself to higher-value work comes from anecdotal evidence—conversations with printers on the leading edge of AI search—not from hard data.

Conversations that look like this:

• Printers indicating that they are speaking more and more frequently with customers who found them through AI search.
• Printers indicating that those customers come to them already inclined to place an order and the feeling that all they have to do, as sales people, is not screw it up.
• Printers (really smart printers) putting eyebrow-raising investments into AI search.
• Printers who have landed those "bigger fish" thanks to those investments.

Clearly, conversations with individual printers is not the same thing as a representative sample. But those representative samples don’t really exist . . . yet. Thus, we work with what we have.

“Why Don't My Results Look Like That?”

By anecdotal measures, we might expect this printer’s results to look similar to the others in the experience bucket. But that’s not happening. Why not?

We can’t know for sure, but for this printer, there is one factor that is different from the others. He is part of a franchise. As part of a franchise, his company already has a high level of existing visibility and established market perceptions about its capabilities. Is it possible that this is impacting how potential buyers respond to the results from AI search?

A recent example suggests the answer may be yes. This came up during an AI search presentation to a gathering of local printers. One of the slides showed the results of an AI search based on specific job parameters and included a (different) franchise printer. When the slide was shown, one attendee blurted out, “Everybody knows they don’t do those jobs in-house!”

AI recommendation dismissed.

The AI system had surfaced a printer that could, in fact, handle the work, either directly or through its network, but the human in the room overruled the recommendation based on preexisting beliefs about that brand.

When AI Search Meets Brand Perception

This is the tension at the heart of the original argument and this CEO’s data. If AI search is, in part, a “capability discovery” engine, then it makes sense that specialists and marketing-savvy independents positioning around complex applications might see a different pattern of AI-referred work than a franchise location with a deeply entrenched brand image (rightly or wrongly) as a quick-turn, commodity provider.

Put differently, two printers can show up in AI search results for the same complex job, but the buyer’s reaction may be very different depending on the logo they see.

This doesn’t mean the data from the franchise is wrong. It also doesn’t mean the conclusion based on anecdotes from early adopters is wrong. It means that it’s complicated. AI search doesn’t operate in a vacuum. It operates in the real world, where brand, reputation, and long-standing assumptions still shape which recommendations buyers act on.

Keep that feedback coming!