How To Analyze Google Discover

TL;DR

1. To generate the most value from Discover, view it through an entity-focused lens. People, places, organisations, teams, et al.
2. Your best chance of success in Discover with an individual article is to make sure it outperforms its expected performance early. So share, share, share.
3. Then analyze the type of content you create. What makes it clickable? What resonates? What headline and image combination works?
4. High CTR is key for success, but “curiosity gap” headlines that fail to deliver kill long-term credibility. User satisfaction trumps clickiness over time.

Discover isn’t a completely black box. We have a decent idea of how it works and can reverse engineer more value with some smart analysis.

Yes, there’s always going to be some surprises. It’s a bit mental at times. But we can make the most of the platform without destroying our credibility by publishing articles about vitamin B12 titled:

“Outlive your children with this one secret trick the government don’t want you to know about.”

Key Tenets Of Discover

Before diving in headfirst, let’s check the depth of the pool.

“Sustained presence on search helps maintain your status as a trustworthy publisher.”

• Discover feeds off fresh content. While evergreen content pops up, it is very closely related to the news.
• More lifestyle-y, engaging content tends to thrive on the clickless platform.
• Just like news, Discover is very entity, click, and early engagement driven.
• The personalized platform groups cohorts of people together. If you satiate one, more of that cohort will likely follow.
• If your content outperforms its predicted early-stage performance, it is more likely to be boosted.
• Once the groups of potentially interested doomscrollers have been saturated, content performance naturally drops off.
• Google is empowering our ability to find individual creators and video content on the platform, because people trust people and like watching stuff. Stunned.

Obviously, loads of people know how to game the system and have become pretty rich by doing so. If you want to laugh and cry in equal measure, see the state of Google’s spam problems here.

Most algorithms follow the Golden Hour Rule. Not to be confused with the golden shower rule, it means the first 60 minutes after posting determine whether algorithms will amplify or bury your content.

If you want to go viral, your best bet is to drive early stage engagement.

What Data Points Should You Analyze?

This is focused more on how you, as an SEO or analyst, can get more value out of the platform. So, let’s take conversions and click/impression data as read. We’re going deeper. This isn’t amateur hour.

I think you need to track the below and I’ll explain why.

• CTR.
• Entities.
• Subfolders.
• Authorship.
• Headlines and images.
• Content type (just a simple breakdown of news, how-tos, interviews, evergreen guides, etc.).
• Publishing performance.

You need to already get traffic from Discover to generate value from this analysis. If you don’t, revert back to creating high-quality, unique content in your niche(s) and push it out to the wider world.

Create great content and get the right people sharing it.

Worth noting you can’t accurately identify Discover traffic in analytics platforms. You have to accept some of it will be mis-attributed. Most companies make an educated guess of sorts, using a combination of Google and mobile/android to group it together.

CTR

CTR is one of the foundational metrics of news SEO, Top Stories, Discover, and almost any form of real-time SEO. It is far more prevalent in news than traditional SEO because the algorithm is making decisions about what content should be promoted in almost real time.

Evergreen results are altered continuously, based on much longer-term engagement.

This is weighted alongside some kind of traditional Navboost engagement data – clicks, on-page interactions, session duration, et al. – to associate a clickable headline and image with content that serves the user effectively.

It’s also one of the reasons why clickbait has (broadly) started to die a death. Like rampant AI slop, even the mouth breathers will tire of it eventually.

To get the most out of CTR, you need to combine it with:

• Image type.
• Headline type (content type too).
• And entity analysis.

Entity Analysis

Entities are more important in news than any other part of SEO. While entity SEO has been growing in popularity for years, news sites have been obsessed with entities (arguably without knowing it), for years.

While it isn’t as easy to just frontload headlines with relevant entities to get traffic anymore, there’s still a real value in analyzing performance at an entity level.

Particularly in Discover.

You want to know what people, places, and organizations (arguably, these three make up 85%+ of all entities you need to care about) drive value for you and users in Discover.

To run proper entity analysis you cannot do this manually. At least not well or at scale.

My advice is to use a combination of your LLM of choice, an NER (Named Entity Recognition) tool and either Google’s Knowledge Graph or WikiData.

You can then extract the entity from the page in question (the title), disambiguate using the on page content (this helps you assess whether ‘apple’ is the computing company, the fruit or an idiotic celebrities daughter) and confirm it with WikiData or Google’s Knowledge Graph.

Subfolder

Relatively straightforward, but you want to know which subfolders tend to generate more impressions and clicks on average in Discover. This is particularly valuable if you work on larger sites with a lot of subfolders and high content production.

You want to make sure that everything you do maximizes value.

This becomes far more valuable when you combine this data with the type of headline and entities. If you begin to understand the type of headline (and content) that works for specific subfolders, you can help commissioners and writers make smarter decisions.

Subfolders that tend to perform better in Discover give individual articles a better chance of success.

Generate a list of all of your subfolders (or topics if your site isn’t setup particularly effectively) and tracking clicks, impressions and CTR over time. I’d use total clicks, impressions and CTR and an average per article as a starting point.

Authorship

Google tracks authorship in search. No ifs, no buts. The person who writes the content has significance when it comes to E-E-A-T, and good, reliable authorship makes a difference.

How much significance, I don’t know. And neither do you.

In breaking down all metrics from the leak that mention the word “author,” the below is how Google perceives and values authorship. As always, this is an imperfect science, but it’s interesting to note that of the 35 categories I reviewed, almost half are related just to identifying the author.

Disambiguation is one of the most important components of modern-day search. Semantic SEO. Knowledge graphs. Structured data. E-E-A-T. A huge amount of this is designed to counter false documents, AI slop, and misinformation.

So, it’s really important for search (and Discover) that you provide undeniable clarity.

For Discover specifically, you should see authors through the prism of:

• How many articles have they written that make it onto Discover (and that perform in Search)?
• What topic/entities do they perform best with?
• Ditto headline type.

Headline Type

This is a really good way of viewing the type of content that tends to perform for you. For example, you want to know whether curiosity gap headlines work well for you and whether headlines with numbers have a higher or lower CTR on average.

• Do headlines with celebrities in the headline work well for you?
• Does this differ by subfolder?
• Do first-person headlines have a higher CTR in Money than in News?

These are all questions and hypotheses that you should be asking. Although you can’t scrape Discover directly (trust me, I’ve tried), you can hypothesize which H1, page title, and OG title is the clickiest.

What’s interesting in this example is that “how-to” headlines are not portrayed as very Discover-friendly. But it’s the concept that sells it. It’s different.

Start by defining all the types of headlines you use – curiosity gap, localized, numbered lists, questions, how-to or utility type, emotional trigger, first person, et al. – and analyze how effective each one is.

Use a machine learning model (you can absolutely use ChatGPT’s API) to categorize each headline.

• Train the model to identify place names, numbers, questions, and first-person style patterns.
• Verify the quality of the categorization.
• Break this down by subfolder, author, entity, or anything else you choose.

Worth noting that there are five different headlines you and Google can and should be using to determine how content is perceived. Discover is known to use the OG title more frequently than traditional search.

It’s an opportunity to create a “clickier” headline than you would typically use in the H1 or page title.

Images

Images fall into a similar category as headlines. They’re crucial. You can’t definitively prove which image gets pulled through into Discover. But as long as your featured image is 1200 px wide, it’s safe-ish to assume this is the one that’s used.

CTR is arguably the single biggest factor in determining early success. Continued success, I believe, is more Navboost-related – more traditional-ish engagement.

And CTR in Discover is determined by two things:

1. The headline.
2. The image.

Well, two things in your control. You could be pedantic and say, “Ooo, your brand is an important factor in CTR, actually. Psychologically, people always click on…”

And I’d tell you to bore off. We’re talking about an individual article. We’ve done a significant amount of image testing and know that in straight news, people like seeing people looking sad. They like real-ness.

In money, they like people looking at the camera, looking happy. It makes them feel safe in a financial decision.

Stupid, I know. But we’re not an intelligent race. Sure, there are a few outliers. Galileo. Einstein. Noel Edmonds. But the rest of us are just trying not to throw stuff at each other outside Yates’s on a Friday night.

It is actually why clickbait headlines have worked for years. It works until it doesn’t.

You’ll need to upload a set of images to help train the model, and please don’t take it as gospel. Check the outputs. For the basics – whether people are present, where they’re looking, color schemes, etc. – great. For more nuanced decisions like trustworthiness or emotional meaning, you’ll need to do that yourself.

Worth noting that lots of publishers trial badges and logos on images. And for good reason. Images with logos consistently click higher for larger brands (to the best of my knowledge), and if you’re a paywalled site, but have set live blogs to free, it’s worth telling people.

You should breakdown this image analysis into:

• Human presence and gaze.
• Facial expression.
• Emotional resonance.
• Composition and framing.
• Colour schemes.
• Photo-type.

Then you can use machine learning to bucket photos into groups to help determine CTR. For example, people directly looking at a camera + smiling could be one bucket. Not looking at a camera + scowling.

Publishing Performance

The more you publish, the more this matters.

Large newsrooms run analysis on publishing volumes, times, and content freshness fairly consistently and at a desk-level. If you only have 50 or fewer articles per month making it into Discover, you probably don’t need to do this.

But if we’re talking about hundreds or thousands of articles, these insights can be really useful to commissioners.

I would focus on:

• Publishing days.
• Publishing times.
• Content freshness.
• Republishing vs. publishing.

Your output should give really clear guidance to desks, commissioners, and publishers around when is best to publish for peak Discover performance.

We never make direct recommendations solely for Discover for a number of reasons. Discover is a highly volatile platform and one that does reward nonsense. It can lead you down the garden path with all sorts of thin, curiousity gap style content if you just follow the numbers.

And it has limited direct impact on your bottom line.

How Do You Tie This All Together?

You need a clear set of goals. Goals that help you deliver analysis that directly impacts the value of your content in Discover. When you set your analysis, focus on elements you have more control over.

For example, you might not be able to control what commissioners choose to publish, but you can change the headline (H1, title, and/or OG) and image prior to publish.

1. Set a clear goal around conversions and traffic.
2. Understand what you have more control over.
3. Deliver insights at a desk or subfolder level.

Understanding whether your role is more strategic or tactical is crucial. Strategic roles are more advisory in nature. You can offer some thoughts and advice on the type of headlines and entities to avoid or choose, but you may not be able to change them.

Tactical roles mean you have more say in the implementation of change. Headlines, publish times, entity targeting, etc.

Simple.

More Resources:

This post was originally published on Leadership in SEO.

Featured Image: Master1305/Shutterstock

AI Search in 2026: The 5 Article GEO & SEO Playbook For Modern Visibility via @sejournal, @contentful

In the SEO world, when we talk about how to structure content for AI search, we often default to structured data – Schema.org, JSON-LD, rich results, knowledge graph eligibility – the whole shooting match.

While that layer of markup is still useful in many scenarios, this isn’t another article about how to wrap your content in tags.

Structuring content isn’t the same as structured data

Instead, we’re going deeper into something more fundamental and arguably more important in the age of generative AI: How your content is actually structured on the page and how that influences what large language models (LLMs) extract, understand, and surface in AI-powered search results.

Structured data is optional. Structured writing and formatting are not.

If you want your content to show up in AI Overviews, Perplexity summaries, ChatGPT citations, or any of the increasingly common “direct answer” features driven by LLMs, the architecture of your content matters: Headings. Paragraphs. Lists. Order. Clarity. Consistency.

In this article, I’m unpacking how LLMs interpret content — and what you can do to make sure your message is not just crawled, but understood.

How LLMs Actually Interpret Web Content

Let’s start with the basics.

Unlike traditional search engine crawlers that rely heavily on markup, metadata, and link structures, LLMs interpret content differently.

They don’t scan a page the way a bot does. They ingest it, break it into tokens, and analyze the relationships between words, sentences, and concepts using attention mechanisms.

They’re not looking for a tag or a JSON-LD snippet to tell them what a page is about. They’re looking for semantic clarity: Does this content express a clear idea? Is it coherent? Does it answer a question directly?

LLMs like GPT-4 or Gemini analyze:

• The order in which information is presented.
• The hierarchy of concepts (which is why headings still matter).
• Formatting cues like bullet points, tables, bolded summaries.
• Redundancy and reinforcement, which help models determine what’s most important.

This is why poorly structured content – even if it’s keyword-rich and marked up with schema – can fail to show up in AI summaries, while a clear, well-formatted blog post without a single line of JSON-LD might get cited or paraphrased directly.

Why Structure Matters More Than Ever In AI Search

Traditional search was about ranking; AI search is about representation.

When a language model generates a response to a query, it’s pulling from many sources – often sentence by sentence, paragraph by paragraph.

It’s not retrieving a whole page and showing it. It’s building a new answer based on what it can understand.

What gets understood most reliably?

Content that is:

• Segmented logically, so each part expresses one idea.
• Consistent in tone and terminology.
• Presented in a format that lends itself to quick parsing (think FAQs, how-to steps, definition-style intros).
• Written with clarity, not cleverness.

AI search engines don’t need schema to pull a step-by-step answer from a blog post.

But, they do need you to label your steps clearly, keep them together, and not bury them in long-winded prose or interrupt them with calls to action, pop-ups, or unrelated tangents.

Clean structure is now a ranking factor – not in the traditional SEO sense, but in the AI citation economy we’re entering.

What LLMs Look For When Parsing Content

Here’s what I’ve observed (both anecdotally and through testing across tools like Perplexity, ChatGPT Browse, Bing Copilot, and Google’s AI Overviews):

• Clear Headings And Subheadings: LLMs use heading structure to understand hierarchy. Pages with proper H1–H2–H3 nesting are easier to parse than walls of text or div-heavy templates.
• Short, Focused Paragraphs: Long paragraphs bury the lede. LLMs favor self-contained thoughts. Think one idea per paragraph.
• Structured Formats (Lists, Tables, FAQs): If you want to get quoted, make it easy to lift your content. Bullets, tables, and Q&A formats are goldmines for answer engines.
• Defined Topic Scope At The Top: Put your TL;DR early. Don’t make the model (or the user) scroll through 600 words of brand story before getting to the meat.
• Semantic Cues In The Body: Words like “in summary,” “the most important,” “step 1,” and “common mistake” help LLMs identify relevance and structure. There’s a reason so much AI-generated content uses those “giveaway” phrases. It’s not because the model is lazy or formulaic. It’s because it actually knows how to structure information in a way that’s clear, digestible, and effective, which, frankly, is more than can be said for a lot of human writers.

A Real-World Example: Why My Own Article Didn’t Show Up

In December 2024, I wrote a piece about the relevance of schema in AI-first search.

It was structured for clarity, timeliness, and was highly relevant to this conversation, but didn’t show up in my research queries for this article (the one you are presently reading). The reason? I didn’t use the term “LLM” in the title or slug.

All of the articles returned in my search had “LLM” in the title. Mine said “AI Search” but didn’t mention LLMs explicitly.

You might assume that a large language model would understand “AI search” and “LLMs” are conceptually related – and it probably does – but understanding that two things are related and choosing what to return based on the prompt are two different things.

Where does the model get its retrieval logic? From the prompt. It interprets your question literally.

If you say, “Show me articles about LLMs using schema,” it will surface content that directly includes “LLMs” and “schema” – not necessarily content that’s adjacent, related, or semantically similar, especially when it has plenty to choose from that contains the words in the query (a.k.a. the prompt).

So, even though LLMs are smarter than traditional crawlers, retrieval is still rooted in surface-level cues.

This might sound suspiciously like keyword research still matters – and yes, it absolutely does. Not because LLMs are dumb, but because search behavior (even AI search) still depends on how humans phrase things.

The retrieval layer – the layer that decides what’s eligible to be summarized or cited – is still driven by surface-level language cues.

What Research Tells Us About Retrieval

Even recent academic work supports this layered view of retrieval.

A 2023 research paper by Doostmohammadi et al. found that simpler, keyword-matching techniques, like a method called BM25, often led to better results than approaches focused solely on semantic understanding.

The improvement was measured through a drop in perplexity, which tells us how confident or uncertain a language model is when predicting the next word.

In plain terms: Even in systems designed to be smart, clear and literal phrasing still made the answers better.

So, the lesson isn’t just to use the language they’ve been trained to recognize. The real lesson is: If you want your content to be found, understand how AI search works as a system – a chain of prompts, retrieval, and synthesis. Plus, make sure you’re aligned at the retrieval layer.

This isn’t about the limits of AI comprehension. It’s about the precision of retrieval.

Language models are incredibly capable of interpreting nuanced content, but when they’re acting as search agents, they still rely on the specificity of the queries they’re given.

That makes terminology, not just structure, a key part of being found.

How To Structure Content For AI Search

If you want to increase your odds of being cited, summarized, or quoted by AI-driven search engines, it’s time to think less like a writer and more like an information architect – and structure content for AI search accordingly.

That doesn’t mean sacrificing voice or insight, but it does mean presenting ideas in a format that makes them easy to extract, interpret, and reassemble.

Core Techniques For Structuring AI-Friendly Content

Here are some of the most effective structural tactics I recommend:

Use A Logical Heading Hierarchy

Structure your pages with a single clear H1 that sets the context, followed by H2s and H3s that nest logically beneath it.

LLMs, like human readers, rely on this hierarchy to understand the flow and relationship between concepts.

If every heading on your page is an H1, you’re signaling that everything is equally important, which means nothing stands out.

Good heading structure is not just semantic hygiene; it’s a blueprint for comprehension.

Keep Paragraphs Short And Self-Contained

Every paragraph should communicate one idea clearly.

Walls of text don’t just intimidate human readers; they also increase the likelihood that an AI model will extract the wrong part of the answer or skip your content altogether.

This is closely tied to readability metrics like the Flesch Reading Ease score, which rewards shorter sentences and simpler phrasing.

While it may pain those of us who enjoy a good, long, meandering sentence (myself included), clarity and segmentation help both humans and LLMs follow your train of thought without derailing.

Use Lists, Tables, And Predictable Formats

If your content can be turned into a step-by-step guide, numbered list, comparison table, or bulleted breakdown, do it. AI summarizers love structure, so do users.

Frontload Key Insights

Don’t save your best advice or most important definitions for the end.

LLMs tend to prioritize what appears early in the content. Give your thesis, definition, or takeaway up top, then expand on it.

Use Semantic Cues

Signal structure with phrasing like “Step 1,” “In summary,” “Key takeaway,” “Most common mistake,” and “To compare.”

These phrases help LLMs (and readers) identify the role each passage plays.

Avoid Noise

Interruptive pop-ups, modal windows, endless calls-to-action (CTAs), and disjointed carousels can pollute your content.

Even if the user closes them, they’re often still present in the Document Object Model (DOM), and they dilute what the LLM sees.

Think of your content like a transcript: What would it sound like if read aloud? If it’s hard to follow in that format, it might be hard for an LLM to follow, too.

The Role Of Schema: Still Useful, But Not A Magic Bullet

Let’s be clear: Structured data still has value. It helps search engines understand content, populate rich results, and disambiguate similar topics.

However, LLMs don’t require it to understand your content.

If your site is a semantic dumpster fire, schema might save you, but wouldn’t it be better to avoid building a dumpster fire in the first place?

Schema is a helpful boost, not a magic bullet. Prioritize clear structure and communication first, and use markup to reinforce – not rescue – your content.

How Schema Still Supports AI Understanding

That said, Google has recently confirmed at Search Central Live in Madrid that its LLM (Gemini), which powers AI Overviews, does leverage structured data to help understand content more effectively.

In fact, at the event, John Mueller recommends to use structured data because it gives models clearer signals about intent and structure.

That doesn’t contradict the point; it reinforces it. If your content isn’t already structured and understandable, schema can help fill the gaps. It’s a crutch, not a cure.

Schema is a helpful boost, but not a substitute, for structure and clarity.

In AI-driven search environments, we’re seeing content without any structured data show up in citations and summaries because the core content was well-organized, well-written, and easily parsed.

In short:

• Use schema when it helps clarify the intent or context.
• Don’t rely on it to fix bad content or a disorganized layout.
• Prioritize content quality and layout before markup.

The future of content visibility is built on how well you communicate, not just how well you tag.

Conclusion: Structure For Meaning, Not Just For Machines

Optimizing for LLMs doesn’t mean chasing new tools or hacks. It means doubling down on what good communication has always required: clarity, coherence, and structure.

If you want to stay competitive, you’ll need to structure content for AI search just as carefully as you structure it for human readers.

The best-performing content in AI search isn’t necessarily the most optimized. It’s the most understandable. That means:

• Anticipating how content will be interpreted, not just indexed.
• Giving AI the framework it needs to extract your ideas.
• Structuring pages for comprehension, not just compliance.
• Anticipating and using the language your audience uses, because LLMs respond literally to prompts and retrieval depends on those exact terms being present.

As search shifts from links to language, we’re entering a new era of content design. One where meaning rises to the top, and the brands that structure for comprehension will rise right along with it.

More Resources:

Featured Image: Igor Link/Shutterstock

SEO Is No Longer A Single Discipline via @sejournal, @DuaneForrester

Most people have a favorite coffee mug. You reach for it without thinking. It fits your hand. It does its job. For a long time, SEO felt like that mug. A defined craft, a repeatable routine, a discipline you could explain in a sentence. Crawl the site. Optimize the pages. Earn visibility. Somewhere along the way, that single mug turned into a cabinet full of cups. Each one different. Each one required – none of them optional anymore.

That shift did not happen because SEO got bloated or unfocused. It happened because discovery changed shape.

SEO did not become complex on its own. The environment around it fractured, multiplied, and layered itself. SEO stretched to meet it.

The SEO Core Still Exists

Despite everything that has changed, SEO still has a core. It is smaller than many people remember, but it is still essential.

This core is about access, clarity, and measurement. Search engines must be able to crawl content, understand it, and present it in a usable way. Google’s own SEO Starter Guide still frames these fundamentals clearly.

Crawl and indexing remain foundational. If content cannot be accessed or stored, nothing else matters. Robots.txt governance follows a formal standard, RFC 9309, which defines how crawlers interpret exclusion rules. This matters because robots.txt is guidance, not enforcement. Misuse can create accidental invisibility.

Page experience is no longer optional. Core Web Vitals represent measurable user experience signals that Google incorporates into Search. The broader framework and measurement approach are documented on Web.dev.

Content architecture still matters. Pages must map cleanly to intent. Headings must signal structure. Internal links must express relationships. Structured data still plays a role in helping machines interpret content and enable eligible rich results today.

Measurement and diagnostics remain part of the job. Search Console, analytics, and validation tools still anchor decision-making for traditional search.

That is the SEO core. It is real work, and it is not shrinking. It is, however, no longer sufficient on its own.

This first ring out from the core is where SEO stops being a single lane.

Once the core is in place, modern SEO immediately runs into systems it does not fully control. This is where the real complexity starts to expand.

AI Search And Answer Engines

AI systems now sit between content and audience. They do not behave like traditional search engines. They summarize, recommend, and sometimes cite. Critically, they do not agree with each other.

In mid-2025, BrightEdge analyzed brand recommendations across ChatGPT, Google AI experiences, and other AI-driven interfaces and found that they disagreed on brand recommendations for 62% of queries. Search Engine Land covered the same analysis and framed it as a warning for marketers assuming consistency across AI search experiences.

This introduces a new kind of SEO work. Rankings alone no longer describe visibility. Practitioners now track whether their brand appears in answers, which pages are cited when citations exist, and how often competitors are recommended instead.

This is not arbitrary. Retrieval-augmented generation exists precisely to ground AI responses in external sources and improve factual reliability. The original RAG paper outlines this architecture clearly.

That architectural choice pulls SEO into new territory. Content must be written so it can be extracted without losing meaning. Ambiguity becomes a liability. Sections must stand alone.

Chunk-Level Content Architecture

Pages are no longer the smallest competitive unit. Passages are. And despite being told we shouldn’t focus on chunks for traditional search, when you look outside of traditional search, you need to understand the role chunks play. And traditional search isn’t the only game in town now.

Modern retrieval systems often pull fragments of content, not entire documents. That forces SEOs to think in chunks. Each section needs a single job. Each answer needs to survive without surrounding context.

This changes how long-form content is written. It does not eliminate depth. It demands structure. We now live in a hybrid world where both layers of the system must be served. It means more work, but selecting one over the other? That’s a mistake at this point.

Visual Search

Discovery increasingly starts with cameras. Google Lens allows users to search what they see, using images as queries. Pinterest Lens and other visual tools follow the same model.

This forces new responsibilities. Image libraries become strategic assets. Alt text stops being a compliance task and becomes a retrieval signal. Product imagery must support recognition, not just aesthetics.

Google’s product structured data documentation explicitly notes that product information can surface across Search, Images, and Lens experiences.

Audio And Conversational Search

Voice changes how people ask questions and what kind of answers they accept. Queries become more conversational, more situational, and more task-focused.

Industry research compiled by Marketing LTB shows that a meaningful portion of users now rely on voice input, with multiple surveys indicating that roughly one in four to one in three people use voice search, particularly on mobile devices and smart assistants.

That matters less as a headline number and more for what it does to query shape. Spoken queries tend to be longer, more natural, and framed as requests rather than keywords. Users expect direct, complete answers, not a list of links.

And the biggest search platform is reinforcing this behavior. Google has begun rolling out conversational voice experiences directly inside Search, allowing users to ask follow-up questions in real time using speech. The Verge covered Google’s launch of Search Live, which turns search into an ongoing dialogue rather than a single query-response interaction.

For SEO practitioners, this expands the work. It pulls them into spoken-language modeling, answer-first content construction, and situational phrasing that works when read aloud. Pages that perform well in voice and conversational contexts tend to be clear, concise, and structurally explicit, because ambiguity collapses quickly when an answer is spoken rather than scanned. Still think traditional SEO approaches are all you need?

Personalization And Context

There is no single SERP. Google explains that search results vary based on factors including personalization, language, and location.

For practitioners, this means rankings become samples, not truths. Monitoring shifts toward trends, segments, and outcome-based signals rather than position reports.

The third ring is where complexity becomes really visible.

These are not just SEO tasks. The things in this layer are entire disciplines that SEO now interfaces with.

Brand Protection And Retrieval In An LLM World

Brand protection used to be a communications problem. Today, it is also a retrieval problem.

Large language models do not simply repeat press releases or corporate messaging. They retrieve information from a mixture of training data, indexed content, and real-time sources, then synthesize an answer that feels authoritative, whether it is accurate or not.

This creates a new class of risk. A brand can be well-known, well-funded, and well-covered by media, yet still be misrepresented, outdated, or absent in AI-generated answers.

Unlike traditional search, there is no single ranking to defend. Different AI systems can surface different descriptions, different competitors, or different recommendations for the same intent. That BrightEdge analysis showing 62% disagreement in brand recommendations across AI platforms illustrates how unstable this layer can be.

This is where SEO is pulled into brand protection work.

SEO practitioners already operate at the intersection of machine interpretation and human intent. In an LLM environment, that skill set extends naturally into brand retrieval monitoring. This includes tracking whether a brand appears in AI answers, how it is described, which sources are cited when citations exist, and whether outdated or incorrect narratives persist.

PR and brand teams are not historically equipped to do this work. Media monitoring tools track mentions, sentiment, and coverage. They do not track how an AI model synthesizes a brand narrative, nor how retrieval changes over time.

As a result, SEO increasingly becomes the connective tissue between brand, PR, and the machine layer.

This does not mean SEO owns brand. It means SEO helps ensure that the content machines retrieve about a brand is accurate, current, and structured in ways retrieval systems can use. It means working with brand teams to align authoritative sources, consistent terminology, and verifiable claims. It means working with PR teams to understand which coverage reinforces trust signals that machines recognize, not just headlines humans read.

In practice, brand protection in AI search becomes a shared responsibility, with SEO providing the technical and retrieval lens that brand and PR teams lack, and brand and PR providing the narrative discipline SEO cannot manufacture alone.

This is not optional work. As AI systems increasingly act as intermediaries between brands and audiences, the question is no longer “how do we rank?” It is “how are we being represented when no one clicks at all?”

Branding And Narrative Systems

Branding is not a subset of SEO. It is a discipline that includes voice, identity, reputation, executive presence, and crisis response.

SEO intersects with branding because AI systems increasingly behave like advisors, recommending, summarizing, and implicitly judging.

Trust matters more in that environment. The Edelman Trust Barometer documents declining trust across institutions and brands, reinforcing why authority can no longer be assumed. Trust diminishes, and consumer behavior changes. The equation is no longer brand = X, therefore X = brand.

SEO practitioners now care about sourcing, claims, and consistency because brand perception can now influence whether content is surfaced or ignored.

UX And Task Completion

Clicks are no longer the win. Completion is.

Though old, these remain applicable. Nielsen Norman Group defines success rate as a core usability metric, measuring whether users can complete tasks. They also outline usability metrics tied directly to task efficiency and error reduction.

When AI and zero-click experiences compress opportunities, the pages that do earn attention must deliver. SEO now has a stake in friction reduction, clarity, and task flow. CRO (conversion rate optimization) has never been more important, but how you define “conversion” has also never been broader.

Paid Media, Lifecycle, And Attribution

Discovery spans organic, AI answers, video feeds, and paid placements. Measurement follows the same fragmentation.

Google Analytics defines attribution as assigning credit across touchpoints in the path to conversion.

SEO practitioners are pulled into cross-channel conversations not because they want to own them, but because outcomes are shared. Organic assists paid. Email creates branded demand. Paid fills gaps while organic matures.

Generational And Situational Behavior

Audience behavior is not uniform. Pew Research Center’s 2025 research on teens, social media, and AI chatbots shows how discovery and engagement increasingly differ across age groups, platforms, and interaction modes, including traditional search, social feeds, and AI interfaces.

This shapes format expectations. Discovery may happen in video-first environments. Conversion may happen on the web. Sometimes the web is skipped entirely.

What This Means For SEO Practitioners

SEO did not become more complex because practitioners lost discipline or focus; it became more complex because discovery fractured. The work expanded because the interfaces expanded. The inputs multiplied. The outputs stopped behaving consistently.

In that environment, SEO stopped being a function you execute and became a role you play inside a system you do not fully control, and that distinction matters.

Much of the anxiety practitioners feel right now comes from being evaluated as if SEO were still a closed loop. Rankings up or down. Traffic in or out. Conversions attributed cleanly. Those models assume a world where discovery happens in one place and outcomes follow a predictable path.

That is no longer the world we’re operating in.

Today, a user might encounter a brand inside an AI answer, validate it through a video platform, compare it through reviews surfaced in search, and convert days later through a branded query or a direct visit. In many cases, no single click tells the story. In others, there is no click at all.

This is why SEO keeps getting pulled into UX conversations, brand discussions, PR alignment, attribution debates, and content format decisions. Not because SEO owns those disciplines, but because SEO sits closest to the fault lines where discovery breaks or holds.

This is also why trying to “draw a box” around SEO keeps failing.

You can still define an SEO core, and you should. Crawlability, performance, content architecture, structured data, and measurement remain non-negotiable. But pretending the job ends there creates a gap between responsibility and reality. When visibility drops, or when AI answers misrepresent a brand, or when traffic declines despite strong fundamentals, that gap becomes painfully visible.

What’s changed is not the importance of SEO, but the nature of its influence.

Modern SEO operates as an integration discipline. It connects systems that were never designed to work together. It translates between machines and humans, between intent and interface, between brand narrative and retrieval logic. It absorbs volatility from platforms so organizations don’t have to feel it all at once.

That does not mean every SEO must take on every cup in the cabinet. It does mean understanding what those cups contain, which ones you own, which ones you influence, and which ones you simply need to account for when explaining outcomes.

The cabinet is already there, and you can choose to keep reaching for a single familiar mug and accept increasing unpredictability. Or you can open the cabinet deliberately, understand what’s inside, and decide how much of the expanded role you’re willing to take on.

Either choice is valid, but pretending everything still fits in one cup is no longer an option.

More Resources:

This post was originally published on Duane Forrester Decodes.

Featured Image: Master1305/Shutterstock