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Google’s Search Quality Rater Guidelines and YMYL in the Age of AI Search

Francine Monahan — Thu, 05 Feb 2026 12:00:00 +0000

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When I recently wrote a blog that walked through the process of performing a query fan-out and building a content plan, I was actually surprised by how many reputable sources were cited by LLMs and on the Google SERPs during my research.

I wrote about how a fictional financial firm could strategize content for queries relating to proper retirement saving (very much a “Your Money or Your Life” topic) and many results cited universities, government organizations like the IRS and the Department of Labor, and long-standing financial institutions like Fidelity and Charles Schwab.

The LLM results for this important YMYL topic were actually giving me information that I didn’t immediately discard like I would have when AI models first appeared. But how often does that actually happen these days? Was this a rare case?

And sure, Google has made some recent edits to its Search Quality Rater Guidelines (SQRG) to meet the needs of AI Search today, but are they helping? And is it enough?

This blog will discuss how AI Search is impacting Google’s search guidelines and look at some YMYL AI searches to see how their citations stack up.

AI Impacts on Google’s Search Quality Rater Guidelines

Think of Google’s SQRG as sort of the blueprint Google uses to train its algorithms. Around 16,000 humans rate and classify content based on its quality and value. And now that various LLMs, AI Overviews, and AI Mode are being used for searches, we need to understand their impacts on how Google defines quality.

1. The Expansion of YMYL

Your Money or Your Life topics can have a risk of harm or negative impact to a person’s health, financial stability, or safety. In the latest SQRG updates in January and September 2025, Google broadened the scope of YMYL:

Government, Civics and Society: This category now includes explicit mention of election and voting information, as well as content that impacts trust in public institutions.
Health or Safety: Google clarifies that while any page can be hypothetically harmful, YMYL status is reserved for topics that could “potentially impact people’s health, financial stability, or safety, or the welfare or well-being of society.”

2. The Rise of Scaled Content Abuse

With AI making it easy to churn out thousands of pages of content, the January 2025 update introduced a guideline that focused on spammy scaled content to better align with Google Search Web Spam Policies.

Google’s guidelines now explicitly state that the lowest quality rating applies if content is:

Copied or paraphrased from different sites, or AI-generated in a “low-effort way”
Lacking originality or added value compared to the other existing pages
Essentially filler created at scale meant to inflate a page’s length without adding substance, regardless of if a human or AI created it

If a chatbot could have written your article in 10 seconds without human oversight, Google’s raters are going to mark it as low quality.

3. E-E-A-T: "Experience" is the AI-Killer

In a slop-infested web, that first “E” in E-E-A-T (Experience) can become your most valuable asset. AI can summarize expertise, but it cannot experience things for itself.

Google’s systems prioritize content that proves real-world involvement. They want to see first-hand life experience. To align with the guidelines, your content should:

Use First-Person Insights: Use phrases like “In our tests,” or “Our survey found that…”
Include Evidence of Effort: Original images, unique data sets, and case studies are still essential to distinguish your business.
Strengthen Author Entities: Ensure your authors have visible credentials and a digital footprint (LinkedIn, professional bios) that Google’s AI can verify.

This is a very important point that we continue to drive home with Relevance Engineering by focusing on proprietary data, creditable research, and high-quality content.

4. Optimizing for Needs Met

The January 2025 update added new guidance on “assessing minor interpretations and intents for Needs Met ratings and added illustrative examples.”

It’s not as groundbreaking as the other updates, but I feel like this was a missed opportunity to include some specific language referring to AI Overviews or AI Mode. I think they fit into the category of Special Content Result Block (SCRB) pretty well, but maybe Google is saving that for a later update once they’ve been in use for several years (or once they finally take the leap and make AI Mode the default).

However, Google still refers to the internet as the “World Wide Web” in the guidelines, so they’re obviously not fully up to date quite yet.

ChatGPT Health and Finance

One of the more concerning product releases of the past year has to be ChatGPT’s HealthBench. It’s a benchmark that was created to measure the capabilities of AI systems for health-related queries. As OpenAI writes, “Built in partnership with 262 physicians who have practiced in 60 countries, HealthBench includes 5,000 realistic health conversations, each with a custom physician-created rubric to grade model responses.”

They claim that their models improved by 28% thanks to HealthBench and outperformed other models such as Claude 3.7 Sonnett and Gemini 2.5 Pro.

To put it bluntly, they could’ve trained their models with 5 million health conversations and I still wouldn’t trust it for health advice. AI is still just predicting text. It’s not understanding the full context, wider health issues and symptoms, reactions to certain treatments and, of course, how incredibly personalized and varied healthcare can be for each person.

In addition to trying to improve its health knowledge, OpenAI has also been focused on financial benchmarking. The company says they are “accelerating financial analysis, generating executive-ready summaries, automating routine documentation, and brainstorming strategic financial plans—all while considering compliance and accuracy standards.”

I couldn’t imagine revenue forecasting or budget planning with an AI tool for many of the same reasons I listed above. It’s very specific to each company. When budgeting, you have to consider promotions and raises, future employees, training and development, ad spend, equipment purchasing, events, etc. Entering in and calculating all of that is manual, so I don’t see much time savings with LLMs.

Who knows what can happen in the future, though.

YMYL in AI Search

You probably know by now how much I love screenshots, so let’s do some AI Search querying for serious, high-risk topics and see what kind of sources are being cited these days (and which model does the best job). I chose three queries in the categories of finance, health, and safety.

Finance YMYL Query: Choosing the Right Life Insurance

For my first search, I queried “how to choose the right life insurance.”

The AI Overview didn’t cite any sources, but the AI Mode result did, focusing on financial groups like the Western and Southern Financial Group, Farm Bureau Financial Services, and Stolly Insurance Group, as well as the nonprofits National Association of Insurance Commissioners and the Insurance Information Institute.

We know at this point that ChatGPT is allergic to citing sources unless you specifically prompt it to, and even then, it’s hit or miss on whether the source is a real one or the information is accurate. But this time, when I asked it to cite sources, its links to the Institute of Financial Wellness, Forbes, and the Louisiana Department of Insurance were legit and lead to helpful articles – a vast improvement from when ChatGPT first launched and cited broken links, fake studies, or nothing at all.

Gemini cited:

New York Life Insurance
Western and Southern Financial Group
Trusted Senior Specialists
Forbes
NerdWallet
Mutual of Omaha
Experian

I noticed a difference right away in that Gemini didn’t cite any nonprofit organizations, just mostly insurance companies (which can’t always be trusted as unbiased). I found this interesting since, you know, Gemini is owned by the same folks who own AI Mode. One would think they’d show similar results.

Perplexity only cited NerdWallet and a few insurance companies, one of which I had never even heard of before (Thrivent).

The Winner: If I had to choose a winner here, I would probably go with AI Mode for the most unbiased and helpful citations (like the two nonprofits it cited).

Health YMYL Query: Signs of a Heart Attack

My next query was “signs of a heart attack.”

I did not receive an AI Overview when I searched for this, but it gave a simple bullet list of symptoms from the Mayo Clinic in a Featured Snippet.

AI Mode cited:

Mayo Clinic
American Heart Association
Geisinger Health (a clinic)
Dignity Health (a clinic)
Houston Methodist (a clinic/academic institute)

I think this is another great selection of trustworthy sources.

ChatGPT cited:

American Heart Association
The CDC
National Heart, Lung, and Blood Institute
Mayo Clinic
CardioSmart (American College of Cardiology)

But, again, I had to specifically tell it to cite its sources. I don’t understand why I still need to do that when every other model cites them openly.

For some reason, Gemini did not cite any sources for this query. I used my ChatGPT method and told it to cite its sources and it still provided no links, but it mentioned in the text that the American Heart Association and the CDC were referenced. I would love to understand the reason behind that because I can’t figure it out.

Perplexity cited:

National Heart Association
CDC
National Health Service
Mayo Clinic
Geisinger Health
Cleveland Clinic

No complaints here. I think these are great sources to cite.

The Winner: I think this one is a 3-way tie between AI Mode, ChatGPT (even though I had to prompt it to cite sources) and Perplexity for citing great, legit sources. I’m still mad at Gemini for not citing anything until I asked, and then only citing two with no links to anything for additional research.

Safety YMYL Query: How to Detect a Gas Leak

For my final query, I chose “how to detect a gas leak in your home.” This is an interesting query because it focuses on safety but the results could also be product-oriented when displaying detection tools a person can buy, which could lead to biased content.

Again, there was no AI Overview for this query.

AI Mode cited mostly heating, plumbing and gas/energy supplier companies such as:

To the T Plumbing and Heating
Constellation (energy supplier)
Con Edison (energy supplier)
Gas South (energy supplier)
Waldrop Plumbing, Heating and Air

It also cited a page of Amazon “Best Sellers” listings for natural gas detectors:

Again, ChatGPT needed me to specify that I wanted it to cite its sources and it cited:

Constellation
Eversource
Renke (environmental monitoring devices like sensors)
Local Rooter and Plumbing
AAA
California Public Utilities Commission

Gemini cited its sources at the bottom for me this time without my asking (though it didn’t identify which information was taken from what source like the others). It cited:

American Red Cross
CDC
National Grid/SoCalGas
CT.gov (recognizing that I’m in Connecticut)

It even asked me if I wanted the emergency contact number for the local gas utility in my town because it knows where I live:

Perplexity gave me several videos to watch, which was interesting and unlike the other models. The videos were by a plumbing education brand, a local government organization called County Office, and an energy supplier (DTE Energy).

It cited many articles by various gas, plumbing, and energy companies, but it was the only model to cite Reddit. Roll your eyes if you want, but there were useful answers provided (aside from the person who wrote “A good way to tell is if you light a match and your house explodes you probably have a leak”).

The Winner: I think I have to give this one to Perplexity. Though I appreciated that Gemini knew my location and offered to provide localized help, the diversity of sources offered by Perplexity, as well as the useful videos, put it over the top.

YMYL in the AI Search Age

AI Search is impacting how trust is synthesized and displayed for the most important queries. While my initial skepticism regarding YMYL topics was met with surprisingly high-quality citations, it’s still kind of the wild west out there. Always be sure to fact-check the information you’re getting from AI models.

The winning LLMs to me in this case varied by query, and the selection of citations was vast, but the models all appeared to be catching on to the importance of trust.

Some takeaways:

AI can synthesize information, but it cannot replicate the “I was there” factor. Personal case studies, original imagery, and first-person insights are still your strongest defense against being labeled as “low-effort” scaled content.
With the 2025 updates, YMYL now covers more than just health and wealth. If your content impacts civic trust or societal well-being, expect the highest level of scrutiny from both algorithms and human raters.
Every AI model is different, so you’ll need to find your flavor when performing important searches. Regardless, whether it’s Perplexity’s diverse sources or Gemini’s localized emergency help, being the cited authority is the goal.

Google might still be calling the internet the “World Wide Web” in its guidelines, but the reality is that we’ve moved into an Answer Engine world. To stay relevant, businesses must build entities and prove through proprietary data and expert credentials that they are the source the AI (and humans) should trust.

Need help becoming more visible online?

Read about Relevance Engineering

The post Google’s Search Quality Rater Guidelines and YMYL in the Age of AI Search appeared first on iPullRank.

Query Fan-Out in Practice: Turning One Search into an Omnimedia Content Plan

Francine Monahan — Thu, 08 Jan 2026 12:00:00 +0000

If you’re a marketer, you’ve probably invested heavily in content marketing over the years, but the Organic Search payoff isn’t delivering, and you’re questioning your SEO strategy.

For example, for those in the finance industry, your retirement planning guides might rank pretty well, your pages explaining investment strategies get decent traffic, and your calculator tools actually help people. But maybe lately, something feels off.

Your rankings are holding steady, yet Organic Search traffic keeps declining. Is this the impact of AI Search?

Maybe you’re barely showing up in AI Overviews or AI Mode results for topics that are core to your business goals, and when potential clients ask ChatGPT or Perplexity about retirement planning, your firm doesn’t get mentioned at all.

The problem might be that you have an outdated perspective on SEO. You’re familiar with optimizing for single keywords, but that’s not how it works in AI Search. AI Search Platforms are leveraging query fan-out.

When someone searches for “What’s the best way to save for retirement,” Google’s Gemini is simultaneously running dozens of related searches for AI Overviews, such as 401(k) contribution limits, Roth IRA comparisons, retirement calculators, savings benchmarks by age, and common mistakes to avoid. If your content doesn’t include passages that satisfy your search across all of these relevant sub-queries, you’re losing visibility, brand awareness, and performance metrics.

So what’s a better, more modern strategy for AI Search? Let’s approach this problem using a fictional mid-sized financial services firm as an example. I’ll walk through the complete process: map out fan-out queries, analyze what actually ranks and gets cited by AI (including which formats and sources dominate), and build an omnimedia content plan. You can’t only worry about your owned properties, like your website. You need to cover every channel that contributes to AI Search results: YouTube, Reddit, industry trades, and every other channel where your prospects are searching.

Let’s get started.

What Is Query Fan-Out and Why Does It Matter for SEO Now?

Today, your content might rank on page one for your target keyword and still be invisible in AI Search results, and a lot of that has to do with query fan-out.

Query fan-out is the process by which AI systems and modern search engines expand a single user query into related sub-queries that run simultaneously in the background. Rather than treating your search as an isolated request, these systems interpret it as a starting point for exploration.

When you ask Google a question in AI Mode, what appears to be one search is actually dozens or even hundreds of synthetic queries working in parallel. As Mike King explained in his detailed technical analysis, these include:

Related queries that are semantically adjacent to your original search.
Implicit queries that capture what you likely meant but didn’t explicitly state.
Comparative queries that help you make decisions between options.
Personalized queries tailored to your search history, location, and behavioral patterns.

Mike’s research into Google’s patent applications revealed that it uses large language models like Gemini to generate these synthetic queries through structured prompting that emphasizes intent diversity, lexical variation, and entity-based reformulations. (If this feels like too much jargon, revisit Chapter 6 of our AI Search Manual, where we discuss some of these fundamentals that influence information retrieval.)

Modern AI Search systems use query fan-out to understand and satisfy your full search intent.

This approach enables AI systems to:

Synthesize comprehensive answers by pulling all relevant information.
Build reasoning chains that connect different aspects of a topic logically.
Anticipate follow-up questions before users ask them.
Diversify information sources to avoid relying on a single perspective.

The query fan-out process helps ensure that AI-generated responses are robust, multi-dimensional, and genuinely helpful.

Helping a Financial Brand Win Retirement Searches

What’s the practical application?

Let’s say you’re working with a mid-sized financial services firm specializing in retirement planning and investment management.

Their primary business goal is to increase qualified leads from people actively researching retirement planning options. To achieve this, they need to earn more visibility in search than direct and indirect competitors.

This company’s core audience persona is a mid-career professional in their 40s, earning over $90K annually, who has been contributing to a 401(k) for years. They have a couple of kids who are in high school and parents who have retired, so now they’re thinking about their own financial future.

The core audience also:

Have accumulated some retirement savings but lack confidence in their strategy.
Feel overwhelmed by financial jargon and competing advice.
Want personalized guidance but aren’t ready to commit to a financial advisor.
Increasingly turn to AI Search tools for quick answers to complex financial questions.

This person doesn’t search for “financial advisor near me” or “best IRA provider” (at least not yet). They start with broader, more exploratory questions.

So, we’re going to ask a broad question to start our search.

The Query

For this analysis, we’ll focus on a query that represents the prospective customer’s starting point in the retirement planning journey:

“What’s the best way to save for retirement?”

This question is exploratory rather than transactional, requires synthesized information from multiple sources, and triggers dozens of related questions that the searcher hasn’t yet articulated.

A little digging around in Ahrefs showed that this question is a difficult keyword for which to rank. The more succinct “best way to save for retirement” is more popular than the question form, which isn’t surprising. People tend to ask full questions to LLMs rather than Google these days.

We can also see where this phrase appears most often, including sites for the Department of Labor, Trinity College, Fidelity, Merrill Edge, Bankrate, and Reddit (remember these sites because they’ll appear often later in our research).

But for the financial company, ranking well for this single query in traditional search isn’t enough. They need to appear across all subqueries about 401(k) contribution limits, retirement calculators, average retirement savings by age, and much more.

Now that we’ve researched the initial query, let’s get to the query fan-out.

Generating a Query Fan-Out with Qforia

Qforia is a tool developed by Mike King at iPullRank to simulate a query fan-out. Begin by visiting the Qforia site, then enter a paid Gemini API key (the free ones won’t work) and enter your query. After a few moments of thinking, your results will appear:

Qforia leverages the same Gemini model used by AI Overviews and AI Mode, so the output is still probabilistic, but the goal is accuracy, not precision. Plus, Mike’s tool bakes in a lot of useful information to the results:

The “query” column shows you other related queries.
The “type” column shows you what kind of question the query is likely asking. Is it comparative like a 401K vs. Roth IRA or personalized like “retirement savings strategies for someone in their 30s”?
The “user intent” column tells you what the searcher is looking for with a particular query. Do they need to understand the basic options? Are they seeking advice tailored to a specific life stage? These classifications help you group your queries based on who is asking and why.

Implicit and personalized queries seem to be common in this fan-out, showing that asking about the best way to save for retirement can be very specific to a certain person.

Another useful bit of data is the “format reason” column. This is a relatively new feature of Qforia that lays out what type of content would be best to answer the query, be it a guide, article, table, interactive tool, etc.

In this example, Qforia suggests a table for a side-by-side comparison of 401Ks and Roth IRAs. And for “retirement savings strategies for someone in their 30s”, it recommends a checklist with actionable steps to take. The “steps to open an IRA account” phrase is another implicit query that would benefit from a numbered or bulleted step-by-step list to follow.

The query “what is an employer 401K match” is considered to be a frequently asked question, so Qforia recommends answering it within an FAQ page about retirement benefits. An interactive tool seems to be the best way to help someone answer “how much money should I save for retirement?”

Now that we’ve performed our fan-out research, let’s take a look at the search engine results pages (SERPs) and see what we can learn.

Analyzing the SERPs for Fan-Out Queries

For this experiment, I will choose five of the fan-out queries Qforia suggested (in addition to our original query) and see what the SERPs look like for each:

Beginner’s guide to saving for retirement
What are the different types of retirement accounts?
How much money should I save for retirement?
Retirement savings strategies for someone in their 30s
Common retirement savings mistakes to avoid

Let’s start with the SERP for the initial query: What’s the best way to save for retirement?

Once you get past the litany of sponsored posts and the AI Overview, the rest of the results give a wide range of helpful resources. There are top 10 lists of tips from Merrill Edge and the U.S. Department of Labor, Reddit and Quora forum answers, YouTube videos, and articles from universities and credit unions.

Bankrate even offers a retirement calculator to estimate how much you should save, even though I didn’t even ask that yet (we’ll see if it shows up again later for that query).

Business	Media Type	Content Type	Website Type
U.S. Dept. of Labor	PDF article	Listicle	Government site
Reddit	Forum posts	Various	Forum
Trinity College	Article	Beginner’s guide	College site
Texas Hospital Association	Article	Listicle	Brand site
Bankrate	Calculator	Informational	Brand site
Space Coast Credit Union	Article	Informational	Brand site
California Credit Union	Article	Listicle	Brand site

When it comes to the AI Overview and AI Mode results, they both offer a bulleted list of strategies for saving. The AI Overview is more detailed, though, with a table showing the different types of retirement accounts and an action plan to follow:

Now let’s see the results for “Beginner’s guide to saving for retirement”:

We see many similar results to the original query, such as the “6 Essential Steps” video, the “Retirement 101” article from Trinity College, the “top 10 ways to prepare for retirement” article from the U.S. Department of Labor, and the Reddit thread on how to start saving for retirement. The AI Overview was also another list of tips and steps to take to save.

Business	Media Type	Content Type	Website Type
NerdWallet	Calculator	Interactive	Brand site
SuperGuy	Video	List	Brand account
Trinity College	Article	Informational	College site
Amazon (Author Dan Clay)	Book	Informational	Brand site
U.S. Dept. of Labor	PDF article	Informational	Government site
Bankrate	Article	Informational	Brand site
Reddit	Forum posts	Multiple	Forum

The process can be applied to every fan-out sub-query, but here we want to focus on the organic rankings.

Next up is “What are the different types of retirement accounts?”:

This query is very specific, and so the results were highly specific lists of definitions. The AI Overview and AI Mode both had the same bullet lists, and the rest of the SERP featured articles from sources like Charles Schwab, Northwestern Mutual, and American Express that were all laid out in list form.

There’s also a “people also ask” section at the top of this query with even more related and highly specific questions.

Business	Media Type	Content Type	Website Type
IRS	Table of contents	Links to informative webpages on the site	Government site
Fidelity	Article	Listicle	Brand site
U.S. Dept. of Labor	Article	Informational	Government site
Thrivent	Article	Informational	Brand site
Reddit	Forum posts	Multiple	Forum
Equifax	Article	Informational	Brand site
Charles Schwab	Article	Listicle	Brand site
Northwestern Mutual	Article	Listicle	Brand site
American Express	Article	Informational	Brand site

As for “How much money should I save for retirement?”:

The results consisted of:

Articles that all gave the same recommendation: save between 10 and 15% of your annual income, implying that it really is a best practice that everyone follows (or simply everyone is copying each other’s advice).
A couple of retirement calculator tools from NerdWallet and Merrill Edge but there weren’t nearly as many as I expected to see (maybe they are difficult to build or not very accurate), so perhaps our financial organization can capitalize on that with a better one.

Based on the lack of diversity in results, it would appear that Google feels this question has already been answered and nothing else is needed.

Business	Media Type	Content Type	Website Type
NerdWallet	Calculator	Interactive	Brand site
Citizens Bank	Article	Informational	Brand site
Texas Hospital Association	Article	Listicle	Brand site
The People’s Bank	Article	Informational	Brand site
Merrill Edge	Calculator	Interactive	Brand site
Brookings	Article	Informational	Brand site

The AI Overview had some recommended guidelines, a table with age-based benchmarks for saving, and a list of methods to estimate how much, including a link to the NerdWallet calculator. The same result was found in AI Mode.

The query “Retirement savings strategies for someone in their 30s” gave a bunch of highly specific results mainly consisting of articles and videos making recommendations. The results even had a “find related products and services” section, implying that this query is so bottom of the funnel that people searching this very specific query probably want to select an actual account/financial institution.

However, one thing to note is that some of the results misunderstood the question. The article from Farther Financial talks about early retirement, as in how to retire when you’re in your 30s instead of starting to plan in your 30s.

Overall, nothing linked on this SERP was a repeat of the other queries:

Business	Media Type	Content Type	Website Type
Securian Financial	Article	Informational	Brand site
Reddit	Forum posts	Multiple	Forum
Fidelity	Article	Listicle	Brand site
Bankrate	Article	Listicle	Brand site
Thrivent	Article	Informational	Brand site
Hancock Whitney	Article	Informational	Brand site
MassMutual	Article	Informational	Brand site
Farther Financial	Article	Informational	Brand site
America’s Wealth Management Show – YouTube	Video	Informational podcast	Brand account

Aside from a few videos up top, the results for “Common retirement savings mistakes to avoid” were hilariously all list articles ranging from 10 important tips to 5 mistakes to avoid. AI Mode and the AI Overview simply listed out the mistakes grabbed from many of the articles listed on the SERP:

Business	Media Type	Content Type	Website Type
T. Rowe Price – YouTube	Video	Informational	Brand account
CBS Philadelphia – YouTube	Video	News story	News station account
MeaningfulMoney – YouTube	Video	Informational	Brand account
Louisiana Office of Financial Institutions	PDF Article	Informational	Government site
Morgan Stanley	Article	Listicle	Brand site
Charles Schwab	Article	Listicle	Brand site

Next, we’re going to look at all these results together and start analyzing what it all means.

Matching Content Formats to Intent

It’s clear that format choice matters just as much as topic choice. As we’ve seen from the screenshots, the SERPs seem to favor list articles, videos, and interactive calculator tools.

One thing I noticed (and was pretty excited about) was how many reputable sources were cited. Actual financial institutions with experience in the subject matter like Fidelity, Charles Schwab, Wells Fargo, and others topped the SERPs and were cited in AI Overviews and AI Mode. I read the AI Overviews and actually believed they were telling the truth, which isn’t something that happens often.

This is great for the people doing the searching, but a challenge for our financial organization to overcome in rankings. They will have to work hard to produce the kind of content that can compete with these huge firms. But thanks to our query fan-out and SERP research, we know what type of content ranks well to give them an idea of where to focus their marketing efforts.

However, it’s also important to consider the fact that a new type of content that hasn’t been seen on the SERPs like an FAQ page or guide might capture more eyeballs than the same listicle over and over. It’s all about experimentation and seeing what works.

A successful content strategy will need to be creative, helpful, and omnimedia to focus on every search channel in order to compete on these SERPs.

Expanding Beyond Web Pages into Omnimedia

What is an omnimedia and omnichannel strategy? It focuses on everything in every channel and in every media format: videos on YouTube, text in Reddit forums, social media, review site tables, TikTok videos, literally anywhere a person can search for something.

In our finance company example, there weren’t any instances of social media or TikTok posts showing up in the SERPs, but we did see quite a few YouTube videos and Reddit and Quora forum posts.

I looked at the same queries in a few LLMs (ChatGPT, Gemini, and Perplexity) to analyze their results. Rather than inundate you with mountains of screenshots, I will just sum up the findings for a few of the queries. I started with the original query of “What’s the best way to save for retirement”:

ChatGPT’s answer was a simple list explaining the types of retirement accounts, a recommended target percentage of income to save, and some tips for smart saving. I didn’t see any links or citations in this summary.
Gemini focused on tables that could be exported to Google Sheets that showed age benchmarks and how to choose the right account type. At the bottom, there was a sources button that opened a window on the right with a list of sources including AARP, T. Rowe Price, Fidelity, and Empower.
Perplexity offered links to cited sources throughout its summary after each section, which included Guardian, Schwab, Vanguard, and even Reddit.

For the “Beginner’s guide to saving for retirement” prompt:

ChatGPT had an overly simplistic list of steps with no cited sources.
Perplexity had a 5-step list with cited sources throughout (mainly the same ones cited the last time like Schwab and Fidelity).
Gemini had a 3-step list, a table of benchmarks, and a checklist, but the most interesting part for me was it specifically recommended several companies with which to open a Roth IRA. This is important for brands to note. With enough content on this topic, financial companies could be added to this list:

For the next query of “What are the different types of retirement accounts”:

ChatGPT gave a list with brief explanations and no citations again.
Perplexity detailed what each plan was and how it worked, citing a source at each line.
Gemini did the same citing many different sources:

For the sake of brevity, let’s go straight to “Common retirement savings mistakes to avoid” since this query is a different style – a serious prompt with negative implications on a person’s life, aka. Your Money or Your Life (YMYL) prompts:

ChatGPT offered a list of mistakes with a “why this hurts” and “do this instead” line for each.
Gemini gave a much more detailed list of mistakes with sources such as Discover, LPL Financial, T. Rowe Price, and Kiplinger. These sources differed from those that Gemini cited for the other prompts.
Perplexity cited 10 sources in their brief list of mistakes to avoid, this time with a few reputable news sources like the New York Times and The Harvard Gazette.

Since this prompt opened up the citations to new sources we hadn’t seen with the other prompts, it shows that organizations like Fidelity and Schwab don’t have a monopoly on citations for this topic. That means our financial business could possibly join the citations.

It also means that this type of prompt that’s focused on YMYL issues, such as making mistakes with your retirement savings, can impact the sources cited. Therefore, depending on their status in the industry, this may not be the right prompt for our financial company to focus on.

Let’s look at the results as a whole to see how our finance firm can fit in.

Reverse-Engineering Citations to Inform Content Creation

AI citations, derived from analyzing SERP features and LLM responses, help content planning by revealing the types of sources, formats, and channels necessary to gain visibility, especially in competitive areas like financial services.

AI Overviews and LLMs draw from a diverse ecosystem of content, which can be categorized by the credibility and nature of the source and the channels they utilize:

Source Type	Examples of Channels and Sources	Insight for Content Planning
Institutional/Financial Firms	Web pages, articles, and tools from established firms like Fidelity, Charles Schwab, T. Rowe Price, Vanguard, and LPL Financial.	These sources dominate citations for core financial topics, signaling the need for high-authority, expert-level content to compete.
Official/Educational	Articles and guides from government agencies and educational institutions, such as the U.S. Department of Labor, Trinity College, and AARP.	Citations favor unbiased, fact-checked authority, especially for complex or sensitive topics like retirement planning.
Reputable Media/News	Articles from established publishers and news sources like NerdWallet, Bankrate, Kiplinger, The New York Times, and The Harvard Gazette.	Targeting queries focused on specific angles, like “Common retirement savings mistakes to avoid” (a negative prompt), can broaden citation opportunities beyond traditional financial firms into reputable media sources.
Community/ User-Generated	Posts and threads from forums like Reddit and Quora.	An omnimedia strategy should recognize and target platforms where prospective customers are having exploratory, non-transactional conversations, as these are cited in both SERPs and LLMs.

The channels cited are primarily traditional web pages/articles, but an effective content strategy must be omnimedia, focusing on all channels where people search.

The type of content that receives citations is often determined by the specific query intent and is heavily favored by formats that are easily synthesized by AI. Cited assets tend to be highly structured and actionable:

List Articles (Listicles): Many SERP results and AI Overviews for queries like “mistakes to avoid” or “types of retirement accounts” heavily feature content formatted as lists.
Tables: AI Overviews frequently use tables to detail complex information, such as age-based benchmarks for saving or comparisons of different retirement accounts.
Interactive Tools and Calculators: For questions about specific quantities, such as “How much money should I save for retirement”, interactive tools and calculators from sources like Bankrate and NerdWallet are featured prominently in results and AI Overviews. Qforia also specifically recommends interactive tools for certain queries.
Actionable Formats: Both Qforia analysis and LLM outputs favor step-by-step lists and checklists for actionable queries like retirement strategies for someone in their 30s.
Videos: Videos are visible on SERPs across multiple exploratory queries, indicating the necessity of including video production in an omnimedia strategy.

Analyzing these citation patterns transforms competitor analysis into actionable content strategy, allowing a financial firm to focus its marketing efforts:

Prioritize Format Over Topic Saturation: Since many SERPs repeat listicles, opportunity may lie in creating different asset types, such as FAQ pages, comprehensive guides, or tables, that might capture more attention than yet another listicle.
Target Implicit Transactional Intent: Although the customer starts with exploratory questions like “What’s the best way to save for retirement”, AI models like Gemini are willing to suggest specific companies for opening accounts. This creates an opportunity for a financial business to position itself to be specifically recommended by AI.
Use Specific Query Styles to Bypass Citation Monopolies: While major firms like Fidelity and Schwab often dominate general retirement citations, queries focusing on specialized or negative topics, like “mistakes to avoid” (which touches on YMYL issues), open up citations to different types of high-authority sources, including news publications. This suggests either focusing resources on specific, highly authoritative content addressing nuanced concerns, or just letting this query go.
Adopt an Omnimedia/Multi-Channel Presence: The appearance of Reddit, Quora, and YouTube content in both SERPs and LLM citations shows that high-quality content planning must extend beyond official website articles. To fully compete, content must be tailored for, and distributed across, these community and video channels.

Now that we have an idea of how these results can inform content strategy, we’ll put together a generalized omnimedia strategy for our hypothetical finance business.

Building an Omnimedia Strategy

Here’s what we do at iPullRank:

Keyword Matrix: Establishes the groundwork for topical presence in AI Search by identifying synthetic keyword gaps, potential opportunities, and valuable terms at the passage level. It reveals which areas are strengthening or weakening your AI presence, enabling you to allocate resources toward terms that create competitive differentiation.
Omnimedia Content Audit: Comprehensive assessment of content spanning all formats and channels, covering owned, earned, and shared media properties. It uncovers what’s limiting your authority while pinpointing where focused resources will generate results.
Omnimedia Content Plan: Strategic blueprint for enhancing Content Resonance and expanding coverage across discovery channels, complete with content frameworks and governance structures. It provides a defined pathway to greater prominence in AI-generated responses.
AI Search Measurement Plan: Monitor input indicators, channel data, and performance benchmarks across AI Search environments. It identifies how AI Search influences growth trajectories, allowing you to connect spending with outcomes and refine your approach based on evidence.

Given that our fan-out research gave us the keywords we need and we already analyzed their presence in searches, let’s put together a plan for our financial company. This plan leverages the query fan-out analysis for retirement planning to create resonant, structured content across multiple channels.

Query Fan-out Prompt	Recommended Asset Type & Structure	Omnimedia Objective
“What’s the best way to save for retirement” (Core)	Pillar Guide / Comprehensive List: Create a definitive, long-form guide. Structure the article so it begins with the core answer or takeaway, followed by supporting detail and nuance.	Semantic Resonance: Ensure the title, description, and internal linking structure establish this as the canonical source for all related fan-out queries.
“Beginner’s guide to saving for retirement”	Actionable Checklist/Step-by-Step Guide: Develop a simplified, numbered list of steps for beginners. Content should be clear and simple.	Passage-Level Extraction: Optimize for easy extraction of the 3 or 5 essential steps by LLMs (like ChatGPT’s simplistic lists).
“What are the different types of retirement accounts?”	Comparison Table and FAQ Page: This requires highly specific definitions. Create a dedicated FAQ page featuring a comparison chart or table detailing account types and their features.	Structured Data: Ensure the data is provided in a clean, easily digestible format (table) that AI Overviews and LLMs already favor when detailing complex information.
“How much money should I save for retirement?”	Interactive Tool/Calculator: The primary format requested for this query is an interactive tool. This must be accompanied by structured data (tables) showing age-based benchmarks for saving.	High-Value Utility: Provide functional utility (the tool) and support the quantifiable answer with authoritative data (the table), increasing the chance of citation over static articles.
“Retirement savings strategies for someone in their 30s”	Personalized Checklist/Segmented Guide: Content should be highly specific and actionable for a segmented audience. Use the product grouping concept to show a progression_from or complementary_with path for financial products relevant to this age group.	Targeted Personalization: Customize the content to fit the behavior and specific needs of this persona, making the recommendations feel personalized, which is crucial for AI agents.
“Common retirement savings mistakes to avoid”	YMYL List Article with Solutions: Create a list detailing the mistakes, each paired with a solution. Must be accurate, with citations and data sourced from trusted research.	Trust and Authority: This content relates to YMYL issues. It must be highly detailed and authoritative to compete with reputable news sources and large financial institutions (otherwise don’t bother).

Because LLMs often prioritize user-generated content, the content plan must actively generate and integrate these signals across all channels:

Channel	Content Focus based on Retirement Queries	Strategic Goal
YouTube	How-To Videos & Guides: Create short, instructional videos for the “Beginner’s guide” and “Strategies for someone in their 30s” queries. Ensure video descriptions include relationship attributes to related website guides and link to the interactive tool.	Capture Exploratory Search: Videos are crucial for user education in the finance industry. Position the firm as an authority where consumers compare accounts and learn how they work.
Community Forums (Reddit/Quora)	Monitoring and Engagement: Focus resources on monitoring discussions related to “Common retirement savings mistakes to avoid” and “Beginner’s guide”, as these forums appear frequently in SERP and LLM citations.	Influence Citation Flow: While the firm cannot directly control these posts, understanding the conversation context allows for developing content that directly answers community-driven questions with clarity and context.
LinkedIn	Thought Leadership: Utilize LinkedIn Pulse for “Retirement savings strategies for someone in their 30s”. Publish educational, semi-formal posts that bridge technology and business to capture a broader professional audience.	Professional Authority: Establish individual and corporate credibility on a channel that ranks well for thought leadership.

By implementing this omnimedia plan, the financial company treats every content touchpoint from the interactive tool on its site to a checklist shared on LinkedIn as a signal that reinforces a clear, emotionally grounded, and contextually rich narrative about retirement planning to maximize visibility in SERPs and LLMs.

But what if your financial business is enterprise-sized with a massive library of webpages? How can you scale your content strategy? And how can you easily prune old, irrelevant content that is no longer serving your site?

Scaling Query Fan-Out and Omnimedia Planning

For a single topic or article, traditional content pruning involved manual work that was often time-consuming and subjective. This process required manually pulling traffic data, checking rankings, looking at backlinks, and skimming posts to make a “best guess” about relevance.

The content pruning methodology we’ve developed at iPullRank uses a Relevance Engineering framework that blends AI-driven semantic relevance analysis with SEO and content metadata to make data-driven decisions at scale. This will help ensure all the content on your site is relevant and identify what content should be removed.

The steps for executing this workflow involve defining your strategic benchmarks, quantifying meaning using vector embeddings, integrating performance data, and applying a final decision framework.

Here are the steps we follow to perform the content pruning:

Step 1: Define Strategic Focus

The entire analysis is grounded in business reality by defining what relevance means for the organization.

Work with the client to solidify their core solution areas or strategic pillars.
For each area, develop representative keyword portfolios reflecting user intent and product capabilities.
Draft a concise business relevance statement (a short paragraph capturing the ideal focus).

Step 2: Generate Topic & Business Relevance Centroids

This step translates the strategic pillars into quantitative benchmarks.

Using the chosen embedding model, generate an embedding for each individual keyword within a topic’s portfolio (developed in Step 1).
Calculate the average of all keyword embeddings within that cluster. This averaged vector becomes the topic centroid, serving as a robust mathematical representation of the topic’s semantic space.
Generate a single embedding for the overall business relevance statement.

Step 3: Generate Article Embeddings

The meaning of every post in the library is represented numerically.

Run the extracted article content through basic Python cleaning routines (removing excess whitespace or stray HTML tags).
Combine the Title text and the cleaned main body content for each article.
Generate a single embedding for each article using the same model used in Step 2 for consistency.
Store these individual article embeddings, typically in a NumPy array paired with their corresponding URLs.

Step 4: Calculate Similarity Scores

This step objectively measures how semantically aligned each article is with the strategic targets.

Using Scikit-learn in Python, calculate the cosine similarity between each article’s embedding (from Step 3) and each of the topic centroid embeddings (plus the business relevance embedding from Step 2).
The output is a matrix added to the spreadsheet, showing scores between -1 and 1, where scores closer to 1 indicate a stronger semantic alignment.

Step 5: Layer SEO Performance & Metadata

Real-world performance and freshness data are integrated with the relevance scores.

Pull standard SEO metrics, primarily trailing 3 to 6 months of organic clicks from Google Search Console, focusing on a recent window to avoid rewarding historical performance.
Pull Publish Date and Last Modified Date from the CMS or crawl data.
Join this performance and freshness data to the master spreadsheet containing the URLs and similarity scores.

Step 6: Apply the Decision Framework (Making Informed Choices)

The combined data points are translated into actionable categories to guide pruning and optimization efforts.

Establish data thresholds to categorize each article into three main actions:
- KILL: Candidates typically have low similarity scores across all core topics, low recent GSC clicks, and are old (more than 5 years with no significant updates).
- KEEP: Articles that qualify if they have high similarity to at least one core topic or have strong recent SEO performance.
- REVIEW/REVISE: Articles that are relevant but underperforming, outdated, or highly similar to other posts (potential consolidation targets).
Conduct manual review of edge cases, confirm decisions, and identify specific actions.

Reframing SEO with Query Fan-Out

For firms competing against giants on the SERPs and in LLMs, focusing on an omnimedia query fan-out approach to content strategy is essential. The citation analysis we walked through shows that specialized queries, especially those focused on specific concerns like “mistakes to avoid” or personalized scenarios like “strategies for someone in their 30s”, open up opportunities for smaller firms to break through with unique and truly valuable content.

You can’t out-budget the big players, but you can out-execute them on format diversity, omnimedia presence, and targeting the fan-out queries they’re ignoring. And when you scale this with the Relevance Engineering framework for content pruning, you’re optimizing your entire content library to align with how AI Search works.

We know that this can be overwhelming for a limited staff to achieve, but we can handle it for you. If you want to do this for your own business, talk to iPullRank about our AI Search Strategy Program with a full Omnimedia Content Audit and Content Plan, tied directly to a Keyword Portfolio Matrix, designed to improve visibility across traditional search, AI search, and third-party platforms. Let us help you be the first thing customers see (and trust) wherever they search.

Want to learn more about how we can help you with AI visibility?

Read about our AI Search Strategy Program

The post Query Fan-Out in Practice: Turning One Search into an Omnimedia Content Plan appeared first on iPullRank.

AI Search to Sale: What the Data Reveals About AI Search Ecommerce Behavior

Francine Monahan — Mon, 10 Nov 2025 12:00:00 +0000

From a soon to be published Profound survey of 2,300+ American shoppers, 58% reported using AI at least once a week to browse products or make purchases.

Does that surprise you? It’s an impressive AI adoption statistic only three years after the launch of ChatGPT. And being so close to Black Friday and the holiday shopping season, it’s an important strategy consideration for marketers.

Around 8% of the commerce world passes through an AI engine at some point in the shopping process, and Adobe expects AI traffic to rise by 520% YoY this holiday season. People of all demographics are researching products to buy on TikTok and Reddit, delivering over $33,000 in ecommerce sales every 10 seconds. There’s clearly a lot to prepare for in the future of ecommerce, but still a lot of uncertainty.

“We are in such a weird space where anything that we say today could go out the window tomorrow,” said iPullRank’s Director of Marketing Garrett Sussman. “Everyone’s search behavior has never been more complex.”

Let’s take a look at some data from Profound around AI search ecommerce behavior patterns and see if we can’t help you capitalize on a few opportunities this holiday season.

Buyer Journeys in Ecommerce

A 27-year-old hedge fund manager from Manhattan buying a smart watch online is going to be different than a 42-year-old runner in Austin. If they jump between a Google search, a YouTube video, a buyer’s guide, and seven other digital touch points, how are you going to account for the AI Search channels of that journey? Their prompts and outputs will look wildly different based on their context.

As Josh Blyskal, AI Strategist and Researcher at Profound said, “This is a volatile space. Everything is changing. This is the best information that exists right now.”

Over 78% of American customers do a mix of in-store and online shopping. And although Google still has most of the market when it comes to search, you still need to consider ChatGPT’s exponential growth. Its new shopping result population rate has become fairly consistent lately.

“We are just at the beginning of the way that user behavior is changing.”

- Garrett Sussman

Ecommerce is still growing overall, but if you look at the conversion rate patterns in this recent scholarly paper, ChatGPT referrals to ecommerce websites are trending upwards, while other channels remain fairly stagnant.

AI search isn’t the highest-converting channel yet, but a year from now, it could be.

“We’re moving into zero-click,” Josh said. “The user behavior’s going to dictate the way that this thing’s going to go and the user behavior is very clear.”

The strategy is changing, and it can be difficult for marketers or executives in various industries, from automotive to finance, to keep up. Every type of product, business, and consumer is different.

AI Ecommerce Strategies

To ensure visibility in every search channel, you will need to open up the floodgates, so to speak, with what product info you include.

Feature completeness is what Josh thinks should take focus.

“If someone says, ‘I want red shoes made out of leather with suede that are not super heavy and clunky,’ there are fields in that entire product feed that are going to dictate whether your product passes or doesn’t.” – Josh Blyskal

A lot of people don’t have that kind of structured information with their products. If you don’t, then you’re limiting who can find you. However, there is also the concern that scammers will try to add too many variations of their product to gain more visibility (like listing AirPods for teachers and AirPods for athletes for the same product).

OpenAI will have to be vigilant and responsible to address bad-faith players who attempt to manipulate the LLMs. That’s likely why they began doing a manual approval process via this form (which is fine for now, but they’ll need to come up with a better method in the future):

The OpenAI Bias

The challenge for marketers now is not knowing what will impact your business’s visibility short term vs. long term. It’s still unclear what biases LLMs operate under and what parameters ensure that certain products show up in results, but thanks to OpenAI’s new partnerships with multiple stores and outlets, visibility is affected.

For example, big box retailers like OpenAI’s new partner Walmart have consistent product feeds that work well on ChatGPT. But on the other side, there are many small Etsy or Shopify shops in its network that can actually win out against major competing companies because of their partnership with OpenAI.

Due to the lack of transparency, it’s difficult to determine how these partnerships may influence the recommendations.

Ecommerce in Google Lens, AI Overviews, and AI Mode

Omnimedia considerations for your AI Search strategy are necessary. Relevance Engineering requires marketers to pay attention to how their brand is represented in more visual search media.

Google Lens allows shoppers to photograph something with their phone and identify it, as well as search for more information on the product. Google is currently developing Project Astra, which incorporates capabilities like screen sharing and video understanding to make the process even easier.

Knowing this, online shopping brands need clean, consistent product photography and recognizable visual markets, or they will fail to match in real-world lookups.

AI Overviews continue to fall behind when it comes to providing fresh, timely content. As shown in this example below, when searching for Black Friday gifts, it references articles written last year:

Although AI Mode isn’t a huge improvement yet, it does continue to evolve and introduce new features, like the comparison feature launched in September, allowing you to select certain products and compare them during the shopping process:

High-quality content plays a big role in how helpful channels like AI Mode can become, and Garrett believes that there isn’t enough good content out there, and most humans aren’t able to produce it.

“It’s the SEOs and the marketers’ responsibility to produce that content,” Garrett said.

Google’s Shopping Graph takes information from retailer feeds, product pages, review data, videos, and more into a single product graph. If your product data is thin or inconsistent, you will weaken the entire Shopping Graph.

Overall, Garrett believes that until AI Mode becomes a default search engine, there probably won’t be any big changes to the industry.

“When it does, it’s going to disrupt all of SEO,” he said.

Black Friday Insights

A click isn’t the measure of a page’s success anymore. So, what is? Becoming a trusted source, being present in the answers, and your editorial presence in all channels.

Sure, AI search still isn’t the main way people search, but adoption is increasing, with the majority of prompts going to ChatGPT.

Looking at Personas

If you’re using a lot of persona data in a prompt, you won’t receive the same results as someone else, so tracking results can be nonspecific. However, persona data can help you see if you’re creating content in the right direction.

Profound provided Black Friday data by age and income, among other demographics, that may help you get an idea of your buyers and where they come from.

But, of course, the frequency at which AI search results change can make data difficult to parse. Using Black Friday footwear-related prompts as an example, you can see citations fluctuated over the past year for the top-cited websites.

“There’s nothing stable about this,” Garret said. “It’s all volatile.”

Real people shopping online are asking very specific questions these days, like if they should wait until Black Friday to buy a new phone, or what payment plans will be available for an Apple device. This means marketers will have to include as much detail as possible about their product to cover all possible queries (more on that next).

Optimizing Product Content for AI Search

When you’re creating content that you want to get drawn into LLMs, your content needs to be clear, concise, and easy to understand.

ChatGPT is pulling snippets of content, and ecommerce outlets have product description and product title limits (for example, Google Shopping limits product descriptions to 5,000 characters). So, you have a limited amount of space to make your point.

“It’s a density problem,” Josh said.

Some suggestions for ensuring your content has a better chance of getting picked up by LLMs include:

Be clear: Your brand voice and tone can’t interfere with the clarity of your content.
Break it up: Split your content into easily digestible chunks so it’s easy to pull.
Be direct: The fluffier your content is, the less helpful it is.
Use semantic triples: Your sentence structure should have a clear subject-predicate-object structure.
Use rich product descriptions: They should offer insight into the specific use cases, features and applications of the product.
Aim for field completion: Be sure to completely fill out all product detail fields.
Avoid ambiguity: Contextualize your content to ensure the right people find your product in the right place and reduce retrieval errors.
Provide exclusive insights: Unique content or proprietary data increases the likelihood of your page being retrieved.

For help with these strategies, iPullRank has its Qforia tool for query fanout simulations, as well as the Relevance Doctor tool that breaks your content into passages and scores each for semantic similarity.

Preparing for Ecommerce Changes

What works today with AI search might not work tomorrow, and the rules are still being written. The question now isn’t whether you should optimize for AI search, but how quickly you can adapt.

For example, one of the most cited sources in AI search right now is YouTube. However, we don’t know if the LLMs are pulling from video transcripts or metadata. Other factors may impact YouTube visibility and retrieval as well, such as influencers receiving an order from your shop and unboxing it in a video. How accessible are your product packages for videos? Are you making it easy for them to create interesting content with your product? Everything can play a part in visibility.

We’re engineering for context and creating content for AI engines. But we also must understand and prepare for the volatility and constant change. Nothing is certain yet, but we can work to prepare for the future.

“I don’t think anyone has the answers,” Josh said. “Anyone who says they have the answers is crazy or they just want to sell you something.”

How OpenAI’s Product Feed Redefines Commerce Data

The post AI Search to Sale: What the Data Reveals About AI Search Ecommerce Behavior appeared first on iPullRank.

AI Mode Impacts on Local Search: Researching Local Health Clinics

Francine Monahan — Fri, 03 Oct 2025 11:28:37 +0000

“This is kind of an AI Mode fail.”

That’s what an Oregon man said after trying to search for Kaiser urgent care clinics near him. Some of the results were not close to his home at all:

He then followed up with his zip code to make it more specific to his location, but one of the results provided was a medical office and not a clinic:

Further down the page, another result popped up that wasn’t a Kaiser facility either.

“That shouldn’t have come up,” he said.

Overall, the experience caused frustration for the man.

The majority of people in this study were looking for a quick answer in an easy-to-read list, preferably in order by distance so they could instantly decide where to go and with additional details so they didn’t have to click on a website. They wanted to see a map with a list of urgent care clinics close to their home, so they would often skip over the sponsored links and scroll directly to the “Places” area of the SERP.

But there were quite a few features people hoped to see in AI Mode (like hours of operation or an option to schedule an appointment), and were left disappointed.

However, AI Mode did succeed when it came to very specific searches, like finding a clinic that offered a particular service.

Reactions to AI Mode varied in our study, performed by Farrah Bostic of The Difference Engine, to learn the search habits of average people. She surveyed 100 participants, spoke one-on-one with 23 users, and watched them perform searches in real time.

Participants ranged in age from 18 to 75 with a balanced mix of ethnic backgrounds, genders, education levels, and locations across the country.

Each user performed five different queries:

Find out which credit cards offer the best loyalty programs.
Find out which kinds of breakfast cereals are healthiest for children.
Find out the top news or sports headlines in your local area.
Find a health clinic in your area.
Find a pair of shoes to wear indoors for under $75.

Watch the webinar replay:

Today, we’re looking deeper into the fourth search task where we asked participants to perform: finding a health clinic in their area. Will AI Mode have an impact on local search? If it becomes the default in the future, how will users be able to get what they need from it? Let’s take a look.

Overall Data from the AI Mode Study

Task #4 of our AI Mode study showed us some interesting info:

29% were shown AI Mode as an option
2% of those who were shown AI mode used AI Mode
3% engaged with AI Overviews and <1% unfurled them
Average number of links clicked: 1.09
Average time to complete the task: 56 seconds
Average score for ease: 4.49 out of 5
Average score for satisfaction: 4.19 out of 5

Here are some stats regarding search terms the participants used:

Search terms emphasized locality, so users tended to use words and phrases like “local” or “near me”, as well as using zip codes and city names to narrow the search.
Some chose to distinguish between different types of health clinics, including “health clinic”, “urgent care”, “general practitioner”, “health care providers”, “primary care”.
Some had specific use cases in mind, using terms like “Find nearest ENT doctor’s office”, or “women’s health clinic near me”.
And some had specific brands of health clinics in mind like City MD, Care Now, or Minute Clinic.

Overall, users wanted maps with locations, hours, reviews, and ways to book appointments. However, AI Mode results lacked specificity and buried the maps farther down the page (if they included them at all), so trust remained higher in Google Maps and direct clinic websites.

Some saw value in refining queries with additional questions, mostly on mobile with voice.

In general, most skipped AI Mode altogether, finding it unhelpful for local searches.

AI Mode User Search Patterns

“Since I’m searching for something specific and local, I would probably do a regular Google search.”

That’s how a 33-year-old from Idaho would search for an urgent care clinic in his area. He didn’t consider using AI Mode for this particular query.

When it comes to finding something local, he wanted to see a map and ratings right away at the top of the page:

AI Mode lacked those features, as it just sent him a list of locations, some of which weren’t that close to him, and many were the same as the Google search results.

“It looks like it’s giving me some relevant results, but at the same time, I’m seeing the same information,” he said.

AI Mode often provides more information than a person is looking for, with detailed summaries and sections featuring additional information and tips. This can be helpful in some cases, but when performing a local search, that’s not always needed.

“For me, I’m looking for a list,” said a 43-year-old woman from Fresno, CA. “I’m not looking for ‘the best’ healthcare clinic near me.”

Another user prioritized reviews. Aside from location, those were a top concern for her. When she tried AI Mode, she was disappointed that there were none in the summary:

A 41-year-old participant from the Bay Area in California also valued ratings above all else and began his search in Yelp.

“I use Yelp a lot for more than just restaurants,” he said.

One study participant from Minnesota added “in Minneapolis metro” to his search terms to narrow it down:

After trying it in AI Mode, he realized that he should have specified that he lives in northern Minneapolis because some of the options were a little far for him. But after comparing the AI Mode results to the traditional Google SERP, he found they were very similar aside from the order in which they presented the results and the location of the map (which is at the bottom in AI Mode).

When another participant searched for a walk-in clinic, there were no maps in the results, and he was one of the only participants to receive an AI Overview, which he liked:

When he tried AI Mode, he liked the map, even though it was located at the bottom of the result:

“That’s definitely helpful,” he said.

For a 74-year-old in Orlando, it didn’t occur to him to use AI Mode for this type of query, but he wasn’t too pleased with his traditional Google search result. There was no map when he searched with his zip code, and he didn’t like the options provided.

“Some of these I don’t recognize,” he said.

After trying AI Mode, he liked how the results were formatted in a concise list.

“This would’ve been a better way to do the search, to see it laid out like this,” he said.

Another study participant also used his zip code in his very specific prompt asking if any clinics accepted his particular insurance.

This is showing us that the more specific the query, the less likely you are to get Places and a map in the Google results. He even received an AI Overview:

AI Mode gave a similar list of results. But when he refined his search to confirm his copay, he was essentially told to check with his insurance. It’s also interesting to note that AI Mode still answered even though he spelled copay wrong.

More Specific Results from Conversational Search

AI shifting search to a more conversational format gives searchers the ability to refine their queries and go deep into specifics.

A 50-year-old North Carolina woman thought it would be helpful if AI Mode could tell you what each clinic specializes in so she could avoid wasting time.

“I’ll go sit in the lobby waiting to be seen and they don’t even do what I need help for,” she said. So, she performed a specific search regarding x-rays that she found helpful (and her preferred way to do that is through voice prompting, hence the addition of “oh”):

A 35-year-old man in Long Island also specified the service he needed in his prompt when using AI Mode, which is another example of conversational search providing more specific answers.

“For specific ones, I would definitely ask AI,” he said, like searching for help with back pain:

A woman from Boston said she would like to see a lot more information like wait time comparisons, or a comparison of services like blood work, scans, or things that are not offered everywhere.

“I might go a little bit deeper,” she said.

One 31-year-old user found her traditional Google search results for a CVS MinuteClinic “a little overwhelming” but liked the bar at the top that provided an “open now” filtering option:

She broadened her search to just “walk-in health clinics” and that seemed to help. AI Mode impressed her with how responsive it is to how she enters prompts, but she worried that could cause issues.

“I feel like that’s a pro and a con,” she said. “You have to be specific about what you’re putting in because the AI Mode will reflect EXACTLY what you put in.”

She entered a follow-up prompt asking about closing times, further proving that people want to see hours and other pertinent information directly in the results. AI Mode provided today’s date so she knew it was accurate, and even included hospitals and emergency rooms, which she actually found helpful (even though others didn’t like that):

A 35-year-old from Florida said his first stop was his insurance plan’s website to see which clinics were covered.

Because this topic is considered “more serious,” another participant said he didn’t necessarily trust Google for this.

“Something like this I’d probably want to get more of a recommendation from somebody first,” he said. Then he’d check to see if his insurance covered the facility. So when he did a Google search, he wasn’t impressed.

“This is not really doing much for me,” he said.

However, when he used AI Mode, he specified that he wanted clinics that were open on the weekend and liked the information provided. Conversational search gives users the opportunity for a more specific search result:

Negative Reactions to AI Mode

The main complaints from survey participants were that the results AI Mode provided weren’t close enough to their homes, and some of the results were hospitals and doctors’ offices rather than clinics.

One 42-year-old user from Virginia didn’t like the response she received from AI Mode.

“I’ve lived here for about 15 years,” she said. “It just pulled up ones that I have never heard of.”

Many of the options were actual doctors’ offices as well. She said that out of all the options given, she would only consider the one at the bottom of the list.

A 52-year-old Nevada man said he only has two clinics in his small town so he didn’t feel the need to use AI Mode, but tried it anyway when prompted.

However, the clinics were not listed in order of distance, which he didn’t like. He also thought some of them were not close enough for him to consider “near him.”

“Those are another 15 miles away,” he said.

Another participant said he “wouldn’t go to AI Mode for this” because Google gave him everything he needed, including an option to schedule an appointment:

A 54-year-old from Philadelphia didn’t use AI Mode either. Instead, she went straight to Google Maps to find a clinic (a technique that I frequently use):

A 29-year-old from Virginia thought the AI Mode responses were not timely or up to date.

“The thing with AI Mode that I see a lot is that it’s not necessarily as time relevant as I want it to be. It always feels like there’s some sort of delay,” he said.

He also agreed with many other study participants that things like phone number, address, hours, and reviews were all important and should be included in the AI Mode response:

AI Mode Considerations for Local Search

This study showed that when it comes to local health clinic searches, most users still prefer traditional Google features like Maps, Places, and direct links to clinics. While AI Mode occasionally provided helpful information like specific services and hours, it often fell short by surfacing results that were too far away, not clinics at all, or missing the details people care about most like reviews, ratings, and insurance acceptance.

At the same time, there were glimmers of potential in AI Mode. Some participants liked that it adapted to very specific queries, such as asking about weekend hours, x-ray availability, or insurance coverage, and they appreciated the conversational follow-up prompts. This suggests that if AI Mode can better integrate maps, filters, and clinic-specific data, it could eventually complement or even enhance local search experiences.

However, the majority of users still bypassed AI Mode altogether, relying instead on Google Maps, Yelp, or their insurance websites to get the most reliable information quickly.

Much of this speaks to the importance of the content on your Google Business profile. Ultimately, people expect all of the business attributes front and center. There’s a way for AI to do that effectively, but AI Mode actually makes the results worse. For people to choose AI Mode over Maps or traditional SERPs, it needs to create a better layout with the information that people want, presented in the way that they want it.

Read the previous articles in this series:

The post AI Mode Impacts on Local Search: Researching Local Health Clinics appeared first on iPullRank.

[Webinar Replay] How to Win Visibility in AI Search: The AI Search Manual Walkthrough

Francine Monahan — Tue, 30 Sep 2025 12:04:13 +0000

Is it all “just SEO”? Or are there new strategies, channels, or tactics you should be prioritizing?

You may not know how to measure or report on everything, but that hasn’t changed the demands from the C-Suite to deliver performance in the same data-driven methods that they’ve become accustomed to over the years.

An approach to AI Search requires education and flexibility, since it’s fundamentally different from the way the SEO industry has approached strategy and performance reporting.

Users never wanted 10 blue links in the first place. They wanted Star Trek technology that can do what we ask and answer questions or solve problems immediately. AI can eventually take us there if done correctly, and we need to be ready.

iPullRank’s own Mike King talked about the history and future of search, how we as an industry can prepare, and some helpful tips from our AI Search Manual in our recent webinar, “How to Win Visibility in AI Search: The AI Search Manual Walkthrough.”

“SEOs need to evolve,” he said. “There’s a lot more that we need to understand as a community.”

Here are some highlights from the webinar in case you missed it.

The Evolution of Search

When AI Overviews launched in May 2024, they had a huge impact on traffic and clicks. Fewer people were clicking over to websites because they now had a summary of information to answer their questions. However, this also led to more qualified leads and more conversions.

“Traffic from large language models or AI search is becoming more valuable,” Mike said.

With this change, we need a new way of strategizing, and that way is generative engine optimization (GEO), a name Mike says we should be using from now on because the publishing industry has already dubbed it that.

iPullRank has its own strategy called Relevance Engineering (r19g), which is the intersection of AI, information retrieval, digital PR, user experience, and content strategy. As users relearn how to search in a multimodal environment of voice, video, and image search, r19g will help us adapt.

“Users are realizing the more specific the information is in their query, the more specific the response will be.”

- Mike King

Mike emphasized that we need to rely mainly on “statistical rigor” and try to obtain as many data points as we can get our hands on. Facts, statistics, and accurate information have always been valuable, but in a world of growing AI slop they’re even more important.

Google Defends its Title

Google is still the most dominant force in search, and Mike thinks they will eventually win the AI battle.

Despite the results of the Department of Justice antitrust trial against Google, the giant still has the proprietary data advantage with an active user base of over one billion. And even though the remedies demand that they hand over that data to competitors, after they go through the inevitable appeals, Google may even change what that data looks like. There are many ways they could get out of this unscathed.

They also have access to first-party chip manufacturing and don’t need to rely on NVIDIA, which gives them another advantage.

The Problem with AI

According to a study done by Ahrefs, 86.5% of top-ranking pages now include some form of AI assistance. And this AI usage does not impact rankings.

You read that right. Just because content is AI-generated does not mean it won’t rank well.

Hallucinations are still not solved, though, so this doesn’t bode well for the future of content unless they can figure out how to solve that.

A Columbia Journalism Review article found that for a controlled information retrieval task, chatbots collectively provided inaccurate or misleading answers more than 60% of the time, nearly always without acknowledging uncertainty.

As Mike said during the webinar, we need to reconsider what we’re doing and identify all of the gaps.

“Now is the time to shed all the things that no longer serve us that we inherited in the SEO industry.”

- Mike King

The Rise of AI Overviews and Semantic Search

Google’s launch of AI Overviews and AI Mode marks a shift from deterministic rankings, where content is surfaced largely as-is based on predefined scoring signals, to probabilistic rankings driven by large language models that interpret, synthesize, and reason across multiple sources.

At the heart of most AI search platforms is retrieval-augmented generation (RAG). By grounding in fresh, externally retrieved data, RAG addresses the fundamental weaknesses of large language models: hallucinations and knowledge cutoffs. RAG systems can deliver answers that are both fluent and factual.

Search has also moved from lexical to semantic. Lexical search is driven by the distribution of words, while semantic is driven by a representation of meaning.

Hybrid retrieval combines both lexical and semantic search then re-rank the documents using methods like reciprocal rank fusion (RRF). However, 95% of SEO tools still only perform lexical analysis.

Google’s next leap was to embed everything it cared about in the search ecosystem to create a unified semantic framework where any object, website, author, entity, or user profile could be compared to any other in the same high-dimensional space.

These embeddings allow Google to “remember” patterns and tailor results in real time, shaping which sources are selected and how answers are framed, making search more personalized.

AI Overviews gave us one of our first tastes of semantic search. They generate an answer and look for documents that can corroborate what was said in the answer.

AI Mode is similar, but its query fanout uses a longer pipeline of the same idea. There are more reasoning stages that go into it, and more passages and pages considered for more queries.

Unlike traditional search engines like Google and Bing, LLMs like ChatGPT and Perplexity don’t “crawl,” they request, and they don’t index content or cache the pages so they are requesting in real time.

The query process for LLMs is a five-step process:

Expansion: This is the stage where the system broadens the scope of what it is looking for, aiming to cover both the explicit and implicit needs behind the request.
Routing: This is where the fan-out map becomes operational. Each sub-query is now a small task in its own right, and the system must decide which source or sources can best satisfy it, which modality is most appropriate for the answer, and which retrieval strategy will be used to get it.
Retrieval: Content must match the expected modality or it won’t be retrieved. Ensure multi-modal parity (text, structured data, transcripts, etc.), place content where the routing logic looks (e.g. API-friendly formats, transcripts for procedural content), and align content with routing profiles to increase retrieval across fan-out branches.
Selection: This often applies harm and safety filters that draw on both explicit policies and learned patterns from training data.
Synthesis: High-quality content can be excluded from synthesis if it isn’t easily extractable. Interactive designs that aren’t crawlable or long-form narratives that bury key facts risk being skipped in favor of denser, more accessible material.

In this new retrieval-and-synthesis pipeline, the query you type is not the query the system uses to gather information. Instead, the initial input is treated as a high-level prompt that sets off a much broader exploration of related questions and possible user needs. The system decomposes the query, rewrites it in multiple forms, generates speculative follow-ups, and routes each variant to different sources.

Getting Visibility in AI Search

The fight for visibility in AI-driven search is about making content easy to extract, interpret, and reuse. If your content is buried in long, unstructured paragraphs, chances are it won’t surface. Clear headings, concise paragraphs, and scannable lists dramatically increase the odds of being pulled into an AI-generated response.

But visibility goes beyond your website. AI systems pull from the full content ecosystem:

Audio clips
Video transcripts
Social media
User-generated content
Digital PR mentions

If your strategy is confined to a single channel, you’re basically accepting “a one-in-four chance” of surfacing. Omnimedia strategies expand your footprint so that wherever the AI looks, it finds you.

Our approach to securing that visibility at iPullRank starts with a content audit, followed by semantic and latent intent research to ensure alignment with how people search and how AI interprets meaning. We then structure and augment content that’s entity-rich, embedding-friendly, and supported by qualifiers, locations, and related entities.

Other semantic practices can also help, including:

Structured data
Layout-aware HTML chunking
Writing for synthesis
Using semantic triples
Strategic use of numbers and data in your content

It’s more of a challenge to measure KPIs in the AI age, so we need to rethink what we’re measuring and how. Measurement now happens across three buckets:

Input metrics: How much high-quality content is produced and distributed
Channel metrics: How well content performs on each platform
Performance metrics: The ultimate business outcomes

Continuous testing and iteration ensure the strategy stays effective as search evolves.

And now that we have the embeddings that OpenAI and Gemini use, we can simulate rankings based on the changes made (like in our proprietary program Qforia).

“We can effectively simulate what a change is going to do from how they’re measuring relevance against these prompts and queries,” Mike said.

It’s Not Just SEO

AI search forced a big change in how information is retrieved, interpreted, and surfaced. We need to adapt quickly, structure content so it’s easily extractable, and show up across the full ecosystem where AI is pulling answers. That means embracing GEO, expanding your team in new ways, applying semantic practices, and treating visibility as omnimedia.

The industry is at a point where old tactics no longer serve us. We can’t rely on rankings alone, and we can’t expect AI systems to do the work of parsing through messy or inaccessible formats. Clean layouts, entity-rich language, structured data, and supporting assets across video, audio, and PR are what make content usable.

As Mike King pointed out, the real opportunity is to combine statistical rigor with creative execution. We have the tools now to simulate rankings against LLMs, test strategies before deploying them, and iterate in a way that matches the speed of search innovation. That’s how we’ll stay ahead.

But until everyone gets to that point, Mike believes we’ll remain stuck:

“Until we see our software catch up, what everyone is talking about is an idealistic form of SEO that most people aren’t doing.”

Want to learn more about AI visibility?

Read the AI Search Manual

The post [Webinar Replay] How to Win Visibility in AI Search: The AI Search Manual Walkthrough appeared first on iPullRank.