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.

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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

Search Marketing’s Insight Gap: When Automation Replaces Understanding via @sejournal, @coreydmorris

Automation is a part of our daily lives in marketing. If you’re in a leadership role or oversee it in some capacity, you’re hearing about it from your team doing the day-to-day work, from those within your industry, or you’re doing your own exploration.

Within search marketing, it has helped to greatly scale efforts as well as to bring new efficiencies, whether those are in our own processes or built into the platforms we use.

In just a few short years, automated bidding strategies, AI-generated content, AI-driven research, and platform-generated “insights” have changed the way we work, including the tools we use, and many of our expectations for how we do search marketing and digital marketing in a broader sense.

With all of this automation and new ways of getting things done, a gap has emerged. I’ll call it an “insights gap.” I contend that teams can see performance changes, but struggle to explain why. This can be serious and, for marketing leaders, can result in a loss of confidence in decision-making due to outcomes not being what was planned, projected, or desired.

No one at a leadership or implementation level likes to have a non-answer or mystery that can’t be solved when real leads or sales dollars are at stake.

Here’s the problem. It is a leadership challenge at this point. It isn’t a technology issue. Automation itself isn’t the problem; the lack of strategic interpretation is.

Now, yes, search volatility is involved. It amplifies the problem with algorithm updates, SERP changes, AI Overviews, and how user behavior changes. Automated systems we have react, but they don’t necessarily contextualize.

Combined with stakeholder expectations rising, we can’t get by with just charts and graphs and data tables. We have to find the insights, contextualize them, and demonstrate value. This is the impact versus activity contrast that has been around forever, but is amplified with automation.

If we go too far into reliance on automation and AI and don’t get the expected marketing and business outcomes, we likely have weaker strategic muscles and an over-dependence on AI and automation tools and platforms. Connecting all knowledge back to being institutional versus platform-specific (and in the AI “brains”) is a key to fixing the problem.

How Marketing Leaders Can Close The Insight Gap

1. Reinforce Strategy In Search Marketing Campaigns & Efforts

Efficiencies gained in execution should be celebrated. Tasks that were manual, done with expensive software, or not done at all just a few years ago can be done in an instant now. The hard and soft cost savings shouldn’t be overlooked.

However, we need to be clear in separating the executional efficiencies from strategic aspects and intent.

Every automated system and process needs to support a documented objective so we’re not just “doing” things, but we’re quantifying them, and they are connected to our overall strategy.

2. Build Human Review Into Automated Systems & Processes

A longstanding challenge with search marketing is that it often doesn’t have a clearly defined ending point. It is ongoing and includes iterative optimization processes. We look to the past to inform decisions for now and going forward, but we often don’t turn it all off, blow it up, and start over (and I’m not advocating for that).

Scheduling structured reviews of AI-driven decisions is important to ensure that we don’t have an insights gap.

In those reviews, even simply asking “why did this change?” before moving on to “what do we do next?” adds an intentional moment to ensure we’re not on autopilot with systems that are not connected deeply enough to our strategy.

3. Train Teams To Interpret, Not Just Monitor, Search Data

We all have dashboards and data coming to us. Or, we have go-to reports in Google Analytics 4 or our web analytics suite that we’re comfortable with. Those are important to have, and any alerts coming our way are great for tracking real-time progress.

Maintaining (or developing) analysts and strategists who can translate data, patterns, and observations into insights is important. Yes, you can create AI agents to do this, but ensure that you have oversight of the agents and that there’s enough cross-checking to ensure that business outcomes aren’t negatively impacted by assumptions that go on for too long in an automated way.

4. Treat AI Outputs As Inputs (For Humans), Not Answers

Being careful with my wording of “inputs” and “outputs” here, calling attention to what AI gives us, we should treat that as output. But, it shouldn’t stop there. The AI output should become “input” for humans.

Even the seemingly smartest ideas from AI should be taken as an output, for human input, and not a definitive (a favorite AI word, by the way) answer.

Just like when humans are owning the full process, with whatever level of AI and automation we have involved, we should maintain a healthy skepticism and validation.

5. Protect Institutional Knowledge In Search Marketing

The more automation we have, likely the more scattered we are with documentation. It probably lives in many places, within platforms, or may be lacking overall. As we get smarter and more efficient with our tech stacks and use, we can’t lose critical institutional knowledge in search marketing.

That means we need to document learnings from tests, optimization, campaigns, and changes. We don’t want to repeat mistakes when platforms, vendors, or other variables change.

6. Align Automation With Business Outcomes, Not Platform Metrics

This is not a new recommendation or news to anyone who has been in marketing leadership. However, I point it out as a word of caution, as the deeper we get in turning things over to automation, the more we’re at risk of getting into the weeds and not being able to connect actions, activities, tactics, and work being done back to an ultimate marketing-driven business outcome.

We need the platform metrics. But, we still need to be able to translate metrics at every depth level back to something higher in the marketing and business ROI equation. Being able to automate and scale something without context can lead us to just doing more of something, doing it faster, or cheaper, but not necessarily moving the needle for ROI.

7. Reintroduce Strategic Review Into Search Marketing Cadence

I mentioned asking questions with human review earlier. More broadly, ensuring that strategic review is integrated into your search marketing cadence is important. My team has been challenging our own client reporting meetings, metrics, and flow recently.

Whether you already have a monthly or quarterly strategic review process or not, this is an opportunity to challenge what automation and AI are doing in the mix. What is it helping, hiding, or potentially distorting? How can we include this in strategic review and go beyond just the data, reports, and activity?

8. Elevate Search Reporting For Executive Audiences

At the heart of any talk about insights, we know we have to translate performance into narrative. With more automation, we need to have more translation. What we are doing matters. However, our executive peers and audiences are a degree (or more) further removed from what we do, and with new tech, are probably even less connected (no offense to the super high-tech execs I know and love).

We still must connect search behavior to customer intent and business priorities. That hasn’t changed, even if we need to layer in more or mine it out of the automation we have in place.

Wrap Up

Automation is essential, and for most, it is a big part of how our teams are scaling digital marketing and search marketing work. Plus, we’re leveraging the functions (whether by choice or not) in platforms and channels that we’re doing our work in.

Automation is incomplete, though, without insight. Strategic understanding is not just necessary, but can be a competitive advantage in search. When everyone is automating, getting above and beyond with strategic insights and leveraging them can be a difference-maker.

The goal here isn’t to slow automation. It is to advance your team’s ability to think critically while scaling implementation and execution.

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Featured Image: Anton Vierietin/Shutterstock

SEO Pulse: Core Update Favors Niche Expertise, AIO Health Inaccuracies & AI Slop via @sejournal, @MattGSouthern

Welcome to this week’s Pulse: updates on rankings from December’s core update, platform responses to AI quality issues, and disputes that reveal tensions in AI-generated health information.

Early analysis of Google’s December core update suggests specialized sites gained visibility in several shared examples. Microsoft and Google executives reframed criticism of AI quality. The Guardian reported concerns about health-related AI Overviews, and Google pushed back on aspects of the testing.

Here’s what matters for you and your work.

December Core Update Favors Specialists Over Generalists

Early analysis of Google’s December core update suggests specialized sites gained visibility in examples shared across publishing, ecommerce, and SaaS.

Key facts: Aleyda Solís’s analysis found sites with narrower, category-specific strength appear to be gaining ground on “best of” and mid-funnel product terms.

Some publisher sites appeared to lose visibility on broader, top-of-funnel queries in examples shared after the rollout. In examples shared after the December 11-29 rollout, ecommerce and SaaS brands with direct category expertise appeared to outperform broader review sites and affiliate aggregators.

Why SEOs Should Pay Attention

This update highlights a trend where generalist sites face ranking pressure, especially on queries with commercial intent or specific domain knowledge. Sites covering multiple categories are affected by competition from dedicated category sites.

Google says improvements can take time to show up. Some changes can take effect in a few days, but it can take several months for its systems to confirm longer-term improvement. Google also says it makes smaller, unannounced core updates that it doesn’t typically announce.

In the examples shared so far, specialization appears to outperform breadth when queries have specific intent.

What SEO Professionals Are Saying

Luke R., founder at Adexa.io, commented on LinkedIn:

“Specialists rise when search stops guessing and starts serving intent. These shifts reward brands that live one problem, one buyer.”

AYESHA ASIF, social media manager and content strategist, wrote:

“Generalist pages used to win on authority, but now depth matters more than domain size.”

Thanos Lappas, founder at Datafunc, added:

“This feels like the beginning of a long-anticipated transition in how search evaluates relevance and expertise.”

In that thread, several commenters argued the update favors deep, category-specific content over broad coverage. Several commenters suggested domain authority mattered less than focused expertise in the examples being discussed.

Read our full coverage: December Core Update: More Brands Win “Best Of” Queries

Guardian Investigation Claims AI Overview Health Inaccuracies

The Guardian reported that health organizations and experts reviewed examples of AI Overviews for medical queries and raised concerns about inaccuracies. A Google spokesperson said many examples were “incomplete screenshots.” The spokesperson also said the vast majority of AI Overviews are factual and helpful, and that Google continuously makes quality improvements.

Key facts: The Guardian said it tested health queries and shared AI Overview responses with health groups and experts for review. A Google spokesperson said many examples were “incomplete screenshots,” but added that the results linked “to well-known, reputable sources” and recommended seeking out expert advice.

Why SEOs Should Pay Attention

AI Overviews can appear at the top of results. When the topic is health, errors carry more weight. The Guardian’s reporting also highlights a practical problem. One charity leader told The Guardian the AI summary changed when repeating the same search, pulling from different sources. That can make verification harder.

Publishers have spent years investing in documented medical expertise to meet Google’s expectations around health content. This investigation puts the same spotlight on Google’s own summaries when they appear at the top of results.

What Health Organizations Are Saying

Sophie Randall, director of the Patient Information Forum, told The Guardian:

 “Google’s AI Overviews can put inaccurate health information at the top of online searches, presenting a risk to people’s health”

Anna Jewell, director of support, research, and influencing at Pancreatic Cancer UK, stated:

“If someone followed what the search result told them, they might not take in enough calories … and be unable to tolerate either chemotherapy or potentially life-saving surgery.”

The reactions reveal two concerns. First, that even when AI Overviews link to trusted sources, the summary itself can override that trust by presenting confident but incorrect guidance. Second, some reactions framed Google’s response as addressing individual examples without explaining how these errors happen or how often they occur.

Read our full coverage: Guardian Investigation: AI Overviews Health Accuracy

Microsoft CEO And Google Engineer Reframe AI Quality Criticism

Within one week, Microsoft CEO Satya Nadella published a blog post asking the industry to “get beyond the arguments of slop vs. sophistication,” while Google Principal Engineer Jaana Dogan posted that people are “only anti new tech when they are burned out from trying new tech.”

Key facts: Nadella’s blog post characterized AI as “cognitive amplifier tools” and called for “a new equilibrium” that accounts for humans having these tools. Dogan’s X post framed anti-AI sentiment as burnout from trying new technology. In replies, some people pointed to forced integrations, costs, privacy concerns, and tools that feel less reliable in day-to-day workflows. The timing follows Merriam-Webster naming “slop” its 2025 Word of the Year.

Why SEOs Should Pay Attention

Some readers may interpret these statements as an attempt to move the conversation away from output quality and toward user expectations. When people are urged to move past “slop vs. sophistication” or describe criticism as burnout, the conversation can drift away from accuracy, reliability, and the economic impact on publishers.

The practical concern is how these companies respond to user feedback versus how they frame criticism. Keep an eye out for more messaging that frames AI criticism as a user issue rather than a product- and economics-related one.

What Industry Observers Are Saying

Jez Corden, managing editor at Windows Central, wrote that Nadella’s framing of AI as a “scaffolding for human potential” felt “either naively utopic, or at worse, wilfully dishonest.”

Tom Warren, senior editor at The Verge, wrote on Bluesky that Nadella wants everyone to move beyond the arguments about AI slop, calling 2026 a “pivotal year for AI.”

The commentary reveals a gap between executive messaging about AI as a transformative technology and the user experience of AI products, which feels inconsistent or forced. Some reactions suggested the request drew more attention to the term.

Read our full coverage: Microsoft CEO, Google Engineer Deflect AI Quality Complaints

Theme Of The Week: Competing Standards

Each story this week reveals a tension between the quality standards applied to publishers and those applied to platforms’ own AI systems.

The December core update appears to put more weight on category expertise than broad coverage in the examples highlighted. The Guardian investigation questions whether AI Overviews meet the accuracy bar Google sets for health content. The Nadella messaging attempts to reframe quality concerns as user adjustment problems rather than product issues.

The week highlights a tension between the standards applied to websites and the way platforms defend their own AI summaries when accuracy is questioned.

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