Surfaced - AI Search Intelligence - September 16, 2026

Last issue, I was ready for everyone’s brains to turn back on after summer.

Apparently mine decided to sign up for Astra, compare things to Claude, max my tokens to find out how good it actually is. Verdict: Real good.

I’ve been spending a lot of time having conversations with marketing leaders on how they run search programs in the AI era, and the awkward space between having a finding and knowing what to do about it.

I’ll say that organizations are not as far ahead as LinkedIn might make you believe.

This week, a Google ranking survey from Zyppy Signal caught my attention because it connects with what I’ve been sharing quite a bit: with all the new things we could do, how do we choose what deserves our time?

I don’t care if you’re at a startup or enterprise, ruthless prioritization is necessary (unless you love friction??)

Onward!

Google’s fundamentals still matter, shocker!

Cyrus Shepard’s Google Ranking Factors Expert Survey last week prompted a practical question for me: how much of your next quarter’s search plan addresses the things buyers still need from your website?

My take: before adding another AEO initiative, look at where buyers get stuck today. If your team can fix an unanswered question on a page that already attracts prospects, that deserves a serious look before another round of content production.

Cyrus produces amazing research. I’ll comment on the top 3.

Relevance: get specific about the decision you’re helping someone make.

It’s easy to say a page is relevant because it mentions the right topic. I want to know whether it answers the question a buyer brought with them.

Imagine someone comparing enterprise software who needs to understand implementation time, migration support and integration limitations. A beautifully written page about “transforming the future of work” leaves them doing the homework themselves. They came there for an answer…give it to them!

Before commissioning another article, I’d ask sales what prospects keep asking after they’ve already read the website. That gap is a very good place to investigate. Use your Gong, Granola, whatever - but find those questions.

Backlinks: ask what would make someone reference you.

My first question would be whether we have anything worth pointing to, like original research, useful documentation, a clear explanation, a tool that solves an annoying problem, or an expert with something specific to say.

Then I’d ask yourself how the right people will discover it. Creating the resource and distribution are different. The publishing button, sadly, does not handle the second one😅

For a larger organization, that discussion can involve PR, partnerships, product marketing and subject experts.

Content quality: show your work!

My practical test is whether the reader leaves better equipped to decide. Can they see the evidence? Are limitations explained? Are the examples useful? Is the product information accurate? Can they tell where your experience adds something?

Sometimes the answer is a shorter, clearer page. Sometimes it requires a deeper explanation or a better comparison. Word count by itself wouldn’t settle that decision for me.

And where does AI search fit?

Ask your team to check how your brand appears in the AI products your customers use. Being easy to find on Google doesn’t tell you whether an AI answer describes your product accurately or includes it in a recommendation.

Choose one important buying question and compare the experience across Google, AI answers and your own website. Where does a prospect get incomplete or conflicting information? That gives you a specific problem to assign.

Adobe put some numbers behind the work

A June Adobe article by Karthik Muralidharan reports a 5x increase in Firefly citations, a 200% increase in Acrobat’s LLM visibility, and a 41% lift in LLM referral traffic within weeks of applying GEO discipline to Adobe.com.

For a marketing leader deciding whether to fund AEO work, this is encouraging evidence that a focused effort can produce measurable gains.

These are Adobe’s reported results, and they don’t establish a revenue gain or a return your team should expect. Keep visibility, visits and sales separate when you set your own targets.

My take: approve a small test with a clear owner and a review date. Choose a product or service where better information could help a buyer decide. Agree up front what would justify expanding the work, whether that’s more accurate recommendations, more qualified visits or more enquiries.

What is your GEO dashboard actually watching?

An r/seogrowth discussion raises a concern worth taking to your measurement provider: the answers in a tracking dashboard may differ from what a customer sees when they open an AI app. The thread includes tool vendors promoting their own products, so treat it as a prompt to investigate.

Ask your provider one plain question: “Does this report capture answers from the app our customers use, or does it generate a separate version?” Have them show you an example and explain what can differ. Your team should be able to explain that distinction without needing an engineering degree.

Then ask your team to run five important buying questions in both the tracker and the customer-facing app over several days. Compare whether your brand appears, what the answer says about you, and which sources it uses. If the results disagree, understand why before using the score to set priorities or report progress.

Who owns AI search…and, like, who can actually change anything?

Finding a problem is only useful if someone can get it fixed.

Suppose an AI answer gives prospects the wrong information about your pricing or implementation time. The correction might involve your website, product documentation, a partner listing or an old article. Your search lead can identify the issue, but other teams control the information.

As the marketing leader, your job is to make that handoff work.

Assign one person to coordinate the correction, agree who confirms the facts, and get a delivery date from whoever can update the source. Give the work a place in those teams’ priorities, then have your search lead check whether the answer improves.

Adobe put some numbers behind the work

A little late, but a June Adobe article by Karthik Muralidharan reports a 5x increase in Firefly citations, a 200% increase in Acrobat’s LLM visibility, and a 41% lift in LLM referral traffic within weeks of applying GEO discipline to Adobe.com.

For a marketing leader deciding whether to fund AEO work, this is encouraging evidence that a focused effort can produce measurable gains (use this if you’re making the case for budget or resources!).

These are Adobe’s reported results, and they don’t establish a revenue gain or a return your team should expect. Keep visibility, visits and sales separate when you set your own targets.

My take: approve a small test with a clear owner and a review date. Choose a product or service where better information could help a buyer decide. Agree up front what would justify expanding the work, whether that’s more accurate recommendations, more qualified visits or more enquiries.

More use but less enthusiasm??

Fractl’s Q2 2026 research, covering 1,008 U.S. consumers and 150 marketers, found that 54% of consumers rated AI more helpful than traditional search, down from 82% in 2025. Meanwhile, 70% said their AI use for search had increased over the past year.

Yikes, so people are using it more…but giving it a worse review.

My read: more frequent use doesn’t mean customers have stopped questioning the answers. You can use a tool every day and still check its work.

For your brand, that means the next click has a job to do. If someone checks an AI recommendation against your product page, 1) can they verify the claim 2) Make the evidence easy to find, 3) keep limitations visible, and 4) fix contradictory information. Give the skeptical buyer something solid to work with, social proof is very very very important.

The budget arrived, yet explaining the return is still messy.

SEOFOMO’s 2026 survey (shoutout Aleyda) collected responses from 171 search professionals across 36 countries, August 17–27. Among respondents, 69% reported allocated AI search budget and 77% said SEO leads the work. On revenue, 35% couldn’t identify AI search’s contribution; 46% estimated 0–5%.

If your team is struggling to connect AEO activity to revenue, this survey suggests you have company. But there’s a useful difference between a small measured contribution and a gap in your reporting. Each calls for a different decision.

If the contribution is small, look at the quality of the visits and whether the work is worth expanding. If you can’t identify it, ask your analytics team (or maybe that’s YOU) what they can reliably track today and what will remain unknown.

For your next budget review, ask for a short account of what changed, what it cost, and what happened afterward. Show improvements in AI answers alongside identifiable visits and qualified enquiries. Give each measure its own line so a rise in mentions doesn’t get presented as a rise in sales.

I talked about it a bit more on LinkedIn.

Take one useful question alllll the way through

Give your team one buying question to work on over the next two weeks. Choose something that regularly comes up in sales conversations and assign one person to see the improvement through. If you aren’t using sales or customer data yet, I don’t know what to tell you 🙃

1. Choose the question with sales or customer success. Use something prospects actually ask, including their constraints and objections. (Tip: look for who, what, when, where using ReGex)

2. Inspect the current experience. Review your relevant page, Google results and answers from the AI platforms you’re investigating. Have your team save the answers and repeat the check over several days. (Or just use your prompt tracking software, ask me if you need helo deciding.)

3. Identify one concrete gap. Missing evidence? Unclear implementation details? Contradictory product information? Choose something you can explain without a visibility score by these platforms.

4. Name the people and the change. Agree who supplies the facts, who approves them and who ships the update.

5. Define the check before launch. Record what you expect to improve and when you’ll review it. Agree how you’ll assess answer accuracy, relevant visits and qualified enquiries. Report each separately.

If the test fails, write down what it taught you. If it works, decide what makes it worth extending to another page or topic.

❝

“Our AI visibility went up. Is that enough to call the program a success?”

It’s progress if you’re showing up more often for relevant buying questions and the answers describe you accurately. Calling the whole program a success needs a little more evidence.

Start by asking whether the report still measures the same questions in the same way. Then ask what improved for the buyer. Did your product enter a relevant shortlist? Did an incorrect claim get corrected? Or did the score move while the answers stayed much the same?

Your leadership update can be simple: what the team changed, what improved in the answers, and what you can see in visits or qualified enquiries. Include customer feedback about how they found you, while keeping it separate from tracked traffic.

If visibility improved but inquiries haven’t, say it plainly!

The decision is whether to keep investing, change the work, or stop. A useful report helps you make that call. Ask the person presenting the dashboard what they recommend doing next, who needs to do it, and why.

If your team is working through that, reply and tell me where you’re stuck: choosing the work, getting it shipped, or explaining the value. Those are useful conversations, and I’m happy to help. Having worked at a few of them, I know what I am talking about :)

I mentioned sharing some of the agents I’ve been building in the last issue. That’s still something I want to come back to. This week, the research deserved the space, thanks to Cyrus and what he provided.

Until next time,

Andrew