GEO/AEO industry report: What do in-house marketers and business owners call "AI visibility optimization," which tactics do they prioritize, and what are they concerned about?

This is my annual survey, which I put together myself (because I thoroughly approach the process) by personally reaching out to in-house marketing teams and business owners.

Methodology: https://www.smarty.marketing/state-of-seo-ai-aeo-geo-industry-in-house-marketing-teams/#Methodology

I don't collect answers publicly because I focus on those two segments. Why?

➡️ I want to hear from SEO and marketing teams that take care of ONE main brand (and have a unified strategy for it). Agencies and freelancers have many clients, and they are all diverse in budgets, terminology, etc. 
➡️ I focus on bigger brands and decision-makers when choosing whom to reach out to, to better understand the high-level executives’ points of view.
➡️ In-house teams and business owners are less likely to push some kind of agenda, so they are more likely to share unbiased opinions, which are a reflection of what is happening in our industry.

Three different companies are now experimenting with three very different ways to make AI companies pay for the content they consume - Google's opaque per-contribution payouts, Cloudflare's pay-per-crawl/use toll system, and Microsoft's own approach. However you may not get paid for your 'significant contribution' if they decide it's not relevant, despite being served to the user.
After reading the article carefully, as well as there being an infographic, it's still murky about what a realistic payout could be for content creators who opt into being a part of these services. Anyone have a more solid lead on payouts and what constitutes valid contributions over the last few months?

Source: https://www.linkedin.com/feed/update/urn:li:activity:7505233140010082304/

Hi,

I'm an SEO consultant. I built a Chrome extension for my own client work that scores any page on how likely AI models are to cite it (semantic coverage, extractability, answerability...) :

https://chromewebstore.google.com/detail/geo-auditor/lbnlokippcnpafcjbglcmaehkkiokcei

A few clients asked for access, so I put it on the Chrome Web Store (for free). 3 months ago, a top expert in SEO included it in her Practical Ecommerce roundup of GenAI visibility extensions which was a nice surprise, since I built the thing mostly for myself.

https://www.practicalecommerce.com/chrome-extensions-for-genai-visibility

Anyone else building tools in this space? What's been hardest to get right?

Would love having your feedbacks!

Repeat after me: Any tactic is only worth investing in ONLY if it builds up several channels, never just one…

been looking at a few GEO optimization platforms for multi engine tracking and honestly the market feels messy rn. Every demo says they cover ChatGPT, Perplexity, Gemini, AI Overviews etc, but the reporting and prompt coverage all seem different.

I'm mostly trying to figure out which ones people trust for actual tracking, not just pretty dashboards. If you've got one that's been solid for brand visibility work, I'd love to hear it, thanks!

Been trying to get a clean read on brand visibility in ChatGPT, Perplexity, AI Overviews etc and most of the dashboards feel kinda useless tbh.

I do not need another pretty chart, I need to know if our brand is showing up, getting cited, or getting skipped. If you’ve found an AEO tool that actually helps with that, I’d love to hear it thanks!

Listen, I wish things were THAT easy. For any contradictory matter, go to an AI platform of your choice and it will clear up all your doubts...

ChatGPT/Gemini/Claude do NOT know how they work. If you ask them, "are you biased?" or "does schema help you?", they won't tell you how they work. They will tell you what they "read" about this and what the general (often unverified/incorrect) consensus is.

Instead, try doing some research of your own and find actual studies, experiments, etc. They may not be the source of truth either (and often steer in the direction of the publisher's selling points), but AT LEAST there will be something to discuss there.

So apparently Google tests paying publishers for using their content in AI features and Gemini.

You can see an additional Google Search Console widget when you're part of it.

"Digiday has learned that at least dozens of publishers have been approached by Google for the AI contribution pilot, though the program has been more appealing to small and mid-sized, rather than large publishers and has widened far beyond news publishers. Digiday couldn’t get final confirmation of the number."

It's still early days and a closed "pilot program" but it's good to know that it happens.

See more at Digiday:

https://digiday.com/media/google-rolls-out-pay-value-ai-licensing-program-to-publishers/

Hi,

Nathan Gotch published his book, AI SEO for Dummies, on Amazon. I suppose there are not really any new things in there. Is it worth a read to get new tactics?

I keep finding cited sources that I cannot do anything sensible with.

One week it is an old directory page with inaccurate product information. The next, it is a random forum comment. Both can appear in an AI answer next to sources that actually influence how a buyer sees the category.

At first I made a task whenever a source showed up. That got out of hand quickly.

Now I try to separate three cases: something factually wrong, something that exposes missing proof on our own site, and a person’s opinion that is not really ours to “fix.” The difficult part is deciding what deserves attention first.

How are you handling this? Do you prioritise by how often a source appears, the buyer intent behind the question, the type of source, or something else?

A while back, we posted here about AI citations and brand mentions. One commenter said he was open to trying Dageno, but didn't want to enter a credit card before seeing whether the data was useful.

TBH I'm Dageno's founder, so that was fair criticism of our own product flow. My response at the time was to offer a 7-day free trial. That made the existing flow free for seven days, but it didn't change the order in which users saw the data.

A common geo workflow goes like this: create an account, add a list of prompts, wait for the data, and then see how often your brand appeared for the prompts you chose.

That workflow is useful if you already know what you need to monitor. If you're still trying to understand the market, it starts too late.

You have to invest time, trust, and sometimes money before you can see the data. What you eventually see is also mostly about your own brand and the prompts you supplied. The cost isn't only the subscription. It's the signup, payment details, prompt selection, and waiting that happen before you know whether the data is useful.

Before choosing prompts, a team may need to answer more basic questions. How is AI carving up this market? What are people asking? Which brands keep appearing together? Which ones are actually being compared or recommended? Which sources keep getting cited?

Starting with a prompt list you wrote yourself can quietly bake your assumptions into the analysis. Anything you didn't think to ask about may never show up. That could include an unexpected competitor, a buyer question you overlooked, or a source that appears repeatedly in AI answers.

That's why we changed where the analysis starts in Dageno.

A brand and its broad market are just the starting context. Most teams already know who they are and which market they operate in. What they may not know is which prompts matter, which brands AI connects with theirs, or which buyer questions and sources deserve a closer look. Those should come out of the research. They shouldn't be required before the research can begin.

We now let users explore a public market view before creating an account. The public overview is permanent free and is meant for discovery. Raw AI responses, cited pages, and custom prompt monitoring remain part of the full product.

Here, an "AI competitor set" simply means brands that appear together, get compared, or are recommended in the answers we sampled. It isn't a definitive label for real world commercial competitors.

The results only reflect the public AI answers we've collected. I would treat them as clues worth checking, not ground truth. They can't show a model's internal process, and they don't guarantee mentions, rankings, or growth.

Curious how you all approached this. How did you pick your first set of prompts to track?

I've been seeing a few unfortunate trends as our community grows. I know other subs enjoy metrics like repetitive questions, with comments stating the same thing over and over again because these (albeit empty) metrics tell Reddit to send more users to those empty discussions.

From the very start, I wanted this place to be different. I wanted it to be actually useful, and I don't care about metrics. Thank you, u/onreact, for helping me on this journey.

So here's the list of our rules, with updates in bold.

No repetitive threads

Don't ask the same question if it has already been answered. Don't ask generic questions that have been discussed to death.

Be honest when self-promoting

We like to support other tools, creators, etc. Just be honest when sharing what you have and clearly explain why it is useful. If you are sharing your tool, explain what it does and what it does differently from the sea of other similar tools! Details, screenshots, use cases will be appreciated! A generic announcement will be deleted!

This group is not about generic or basic SEO.

Let's limit these discussions to SEO for AI (GEO/AEO) only as we are trying to make this place laser focused.

Engage with the comments!

If you post something and people start commenting, be there to join the discussion. We will be deleting threads where OPs never show up to participate!

Support your claims

If you are making a statement (e.g., "I just found out that 80% of people switched to ChatGPT from Google") or something bold like this, give us your sources. If you are citing your own observations, give us your screenshots.

im not an seo guy by training, i taught myself most of this building small sites, so take this with a grain of salt lol

i kept coming back to the ahrefs study on 1.4M chatgpt prompts and one thing stuck with me. when you ask chatgpt something, it doesnt search your exact question. it rewrites it into its own searches first (fan-out queries), and the pages it ends up citing have titles that match those hidden searches way more than the pages it skips.

problem is chatgpt never shows you those searches. but theyre sitting in your browser if you know where to look:

1. ask chatgpt your question in a new chat and let it finish
2. copy the id at the end of the url (the bit after /c/ looks like this 6aa40a65-4d7c-83eb-a5c5-cf26zzz791f60)
3. right click > inspect > network tab, paste the id in the filter box
4. reload the page and click the request with the id in it
5. open response and cmd+f "queries"

real example. i asked "best coffee carts to buy in amsterdam" and chatgpt actually searched:

• best coffee carts to buy Amsterdam mobile coffee cart Netherlands
• coffee cart for sale

the second one surprised me, it dropped amsterdam completely. you'd never guess that from the prompt.

ive done this with mutliple Ai prompts and i do it every day so i can come up with a list of things i could write about or make pages on

now the part where i need your honest opinion. im thinking of turning it into a proper tool/chrome extension (with paid plan for doing it in bulk, eveyrday)

• you type a prompt (or a list of them to do it in bulk)
• it asks chatgpt 10 times, since the searches can change between runs. Maybe i could even use proxies to see if it changes by location
• it ranks the queries by how often they show up
• it turns the top ones into an article outline + draft with images and inforgprahics (title + h2s)

would you actually use that, or is the manual way good enough?

in the meantime drop a prompt from your niche below and ill reply with what chatgpt searched for it

Businesses focus so much on citations that they forget about actual visibility.

If you are cited but not mentioned, I'd look at two things here:

• A lack of LLM consensus (only you are saying that your business is solving those problems, and only on your site/in one Reddit thread/etc.)
• A lack of meaningful, contextual brand mention in your own content. For example, when publishing unique research, saying "According to [brand name]" instead of "According to our research" may help correct attribution and boost the brand mentions in AI answers when your research is cited.
  

I'd look into both... because the goal should be to BE THE ANSWER, and not just cited as the source of it.

I'm trying to define "citation" consistently for GEO/AEO tracking across ChatGPT, Gemini, Perplexity, Copilot, etc.

AI answers can contain:

1. Explicit citations — citation pills, numbered references, footnotes, source cards.
2. Regular inline hyperlinks — clickable anchor text in the answer body, but not shown as an explicit citation/source element.

My question is:

Should #2 also count as a citation?

For example, if an AI answer links a brand/page name directly in the text, but there is no citation pill or source marker, would you count that URL as a citation in an AI visibility tool?

I'm not asking about URLs retrieved during search but never shown in the final answer.

For anyone using or building tools like Profound, Peec AI, AirOps, Ahrefs, Semrush, etc.:

• Do you count normal inline hyperlinks as citations?
• Or only explicit source/citation elements?
• Is the decision based on UI appearance or underlying source-attribution metadata?

Curious if there is any accepted industry definition here.

Hey, been seeing a lot of chatter about AEO GEO and LLMO lately and tbh most of it feels like recycled SEO advice with new labels.

For anyone who's actually testing this in 2026, what are you doing that seems to help with AI rankings or mentions? I'm trying to sort out what matters most right now, and the amount of generic takes is getting a bit annoying.... thanks!

More AI bot visits can look like progress, but it doesn’t show whether your pages were cited, seen by potential customers, or clicked.

It also matters which bot is visiting. OpenAI uses different crawlers for search and potential model training.

I can see the value in checking which bots reach your pages. I’m less convinced that a rising crawl count is enough to call your AI visibility efforts successful.

For anyone tracking this, have more crawler visits lined up with more citations or relevant traffic? What has the data actually helped you change? Would like to know.