Surfaced - Search Intelligence - July 1st

So…how was everybody’s Cannes Lion this year? Did you live vicariously through a LinkedIn feed like me?

One thing I've been digesting from attendees is the search relevance to brand marketing. Search is becoming way less performance-based (never really was), and more akin to what PR and Comms aim to achieve - it’s been a demand capture channel for the longest time. But recently, it’s morphing into a brand discovery channel through conversation.

Search marketing is no longer just about keywords and rankings; it has become an extension of brand marketing.

The brands winning in this AI-first era aren't the ones gaming a secret content algorithm—they are the ones tearing down the silos between SEO, content, PR, and brand, aligning their story, and fighting to become the definitive answer.

PR firms & technology obviously have a stake in this, but check this social proofing if you need it:

❝

"For the last twenty years, the job of a brand was to be discoverable. In an AI-first world, the job is to be the answer. If your brand isn't being cited, you're not in the consideration set."

Chris Hackney, Chief Product Officer of Meltwater

Search teams should really start to think about owning Direct, perhaps partnered with brand marketing teams. There is a lot that influences general search, but direct traffic is becoming an indicator of AI search success. Team sport, for sure.

Unrelated to search, but a very apt description of where AI adoption is headed from Greg Shove, CEO of Section, who helps with AI training at enterprise orgs:

As technology advances and everyone can vibe code something in a weekend, we’re finding that judgment, taste, and experiential knowledge are where humans will be present. I feel like anyone can grab value from this simple illustration.

Onward!

The big release of the last week: Semrush published its expanded 2026 AI Visibility Index, looking at 126 million real US prompts analyzed across top platforms from January through April.

This is one of the most comprehensive brand-in-AI datasets published so far! A few findings I’ll share:

1. Only 36 brands maintained top-100 visibility across all four platforms simultaneously. Wow. The "Universal 36" — YouTube, Amazon, Reddit, Wikipedia, Apple, Walmart — share two traits: 1) broad consumer reach and 2) a clear role helping users complete discovery or transaction tasks.

2. ChatGPT and Google AI Mode agree on which brands to mention 67% of the time, but only agree on which sources to cite 30% of the time. Same brand mention, completely different citations. Different strategies for different platforms might make your head explode a bit, but at the moment, it’s safe to stick with Google.

3. ChatGPT cites an average of 15 sources per response, and Gemini cites 3. Same content investment but radically different citation count. Makes sense considering ChatGPT has a lot more to prove, and Google has already earned trust.

4. Teams that integrate SEO and AI visibility into a single workflow show an 81% success rate in growing AI platform traffic, vs. 36% for teams running them as separate programs. That delta is the internal business case for every marketer trying to get org alignment on AI search strategy right now.

Another platform moves this week: OpenAI's advertising pilot crossed $100M in annualized revenue and is expanding to the UK. GPT-5.5 is now the default model across ChatGPT plans, with GPT-5.6 in limited preview. If you last profiled your brand's AI appearance under GPT-5.2, time to retest.

Soon, we'll be looking at ChatGPT like “holistic search”, with paid and organic battling it out, and operators looking to find the right mix.

Reddit this week is pure measurement frustration…by a lot of people.

The "how do I actually know if I'm showing up in ChatGPT and Perplexity?" thread pattern is running hot across r/SEO and r/digital_marketing. What's shifted is the intensity: people have bought into AI search mattering, but have no reliable way to show their work.

The Semrush 45% stat is being cited in comments as relief: "oh good, it's not just me."

The community is validating the problem in real-time, and if you think prompt measurement isn't getting the job done, you're not alone.

LinkedIn split into two camps: the opportunity frame (Adobe data showing AI-referred traffic converting 54% better than organic — more on that below) and the measurement crisis frame (nobody knows how to track it). Both camps are staring at the same data.

A lot more studies are appearing on social channels, yet some are not citing sources. I have yet to find a prompt list or methodology list that goes into the details of how data is pulled.

I’m not saying I need to peek at their IP, but being in data long enough, I’ve seen how to manipulate data to tell the story. Be cautious when viewing the claims.

The most important thing you can measure in AI search isn't impressions, it's conversions, cause that speaks to business impact. And right now, most teams are undercounting them significantly, because the conversion mechanism for AI-driven purchases is fundamentally different from how we've been tracking search for the last decade.

The zero-click attribution gap

Most conversions driven by AI search are zero-click. A user sees your brand or product mentioned in an AI-generated answer, doesn't click the citation link, opens a new tab, and navigates to your site directly or via branded search. In a last-touch attribution model, that conversion gets credited to direct or organic brand, and AI gets nothing.

SparkToro's June data puts the scale of zero-click in context: 68% of Google searches now end without a click, the fastest acceleration in a decade. The same behavior pattern applies to AI search: the answer provides enough information that clicking is optional.

Similarly, SEO content skews earlier in the user journey.

It surfaces the brand during research, not at the moment of conversion. Ads tend to skew toward users who are already close to buying. For high-consideration purchases, users typically encounter a brand multiple times before converting (up to 30 touches for B2B), which means last-touch reporting for SEO traffic tells maybe a third of the story, at best.

The n8n case study: self-reported vs. last touch

n8n and Graphite (AEO Agency) ran a direct comparison between last-touch GA4 data and self-reported "How Did You Hear About Us" (HDYHAU) /self-reported attribution surveys. The results are both insane and validating. I asked Ethan what the data set is, and he said, “huge”. I trust.

• AEO conversions: 10x difference. Last touch showed 0.9% of conversions attributable to AI/AEO. HDYHAU showed 9%. Last-touch is capturing roughly one-tenth of the actual impact, which is an insane number to put in your back pocket.

• SEO conversions: 4x difference. Last touch showed 7%. HDYHAU showed 30%. High-consideration buyers don't convert the first time they encounter your brand; they research, encounter you across multiple surfaces, then convert on their own terms. Last touch will snag credit for that final moment and misses everything before it.

• SEO still outperforms AEO on conversion volume: SEO conversions were 3x AEO conversions in the study. This will absolutely normalize AI search results scale, especially when Google flips to AI Mode exclusively.

Self-reported surveys aren't perfect. Human memory is fallible, recency bias skews toward the most recent touchpoint, and asking users to select a single channel ignores the reality of multi-touch journeys. Multi-touch attribution modeling gives a more complete view, but can also feel pretty voodoo at times.

But a 10x delta tells you the directional truth even if the exact number moves around, which is more than enough evidence.

One practical tip: on HDYHAU /self-reported attribution implementation: show the survey modal post-conversion, not before it. A pre-conversion survey will reduce your conversion rate. Wait until they've committed before asking. The signal is worth the extra step.

Separately, Adobe's data through May 2026 shows AI-referred traffic converting 54% better than non-AI traffic — a huge reversal from early 2025, when the same traffic converted 38% worse. AI surfaces are maturing as people begin to trust responses more.

If anyone on your team is still citing "AI search traffic doesn't convert", tell them to kick rocks, that framing is more than a year out of date.

Two things to act on this week:

1. Add a HDYHAU/self-reported attribution survey to your post-conversion flow. Coming off the n8n/Graphite survey, and it doesn't have to be complex — one question: "How did you hear about us?" with options that include AI search (ChatGPT, Perplexity, Google AI Overview), organic search, social, referral, direct, or anything else you participate in for demand gen.

Run it for 30 days alongside your GA4 last-touch data and compare. The gap will tell you something real about where AI is actually driving growth.

If you want to get more precise over time, layer in multi-touch attribution modeling — but HDYHAU /self-reported attribution is the fastest way to get an initial signal without some six-month messy implementation with a vendor.

2. Get to know your brand/PR/comms. Seriously. Take an interest in their goals, how they operate, and how they measure. Set up some time with their teams and get close to them because your work is influencing them more than they know (and their work for you).

Develop a working relationship and find common KPIs. They don’t need to know how search works, but they should know what is influencing what they report on.

Not too long ago I was working with a global financial services firm, and I made fast friends with the communications director because I knew they were going to be a crucial lever at our awareness strategy for capturing audiences.

While they focused on Tier 1 outlets, I worked with them to develop Tier 2/Tier 3 outreach to attract new audiences. I supported them, and we attributed success to one another over time. It was love at first mention 🙂

❝

"We've been investing in AI search for six months. How do I show leadership it's actually working?"

This is the most common conversation I'm seeing with clients right now, and the honest answer starts with this: last-touch reporting is the wrong tool for evaluating AI search performance. If GA4 last-touch is your primary lens, you're measuring somewhere between 10–25% of what's actually happening, depending on your category and consideration cycle.

What I tell teams in this situation: build a parallel measurement track now, not after you've already made the case to leadership. Add that HDYHAU survey I mentioned above. Track direct and branded search trends over the same window you're running AI search programs. Watch for a lift in branded query volume — when your AI-eligible content is performing, branded search typically moves before GA4 attribution catches up. Bring that correlation, not just the GA4 number, to your next leadership conversation.

Measurement in AI Search is more akin to brand marketing and polling than performance marketing, and incrementality is a good way to measure success.

The harder part is getting buy-in on proxy metrics. That's a change management problem as much as a measurement one. But it starts with being honest about what your current reporting is and isn't showing — because pretending last-touch attribution tells the whole story will eventually catch up with you when the numbers don't match what's actually happening in the business, and that’s where marketing loses credibility.

I said this to someone recently, that marketing is fueling your last-click attribution model at your company. In B2B, marketing throws the ball for the alley-oop for sales to dunk it.

Attribution isn't the sexiest topic in AI search cause it’s a widely debated topic, but it's the one that decides whether the work gets funded next quarter.

Let me know if I can help you solve a problem, and don’t hesitate to reach out.

Appreciate you all. Until the next time,

Andrew