AI agents don’t read content the way humans do. They operate inside strict token budgets — fixed limits on how much text they can process at once. When your content exceeds that budget, the agent doesn’t skim. It cuts. Understanding where those cuts happen, and why, is the actual foundation of AI content strategy right now.

The optimization community has spent two years talking about “writing for AI” without confronting this constraint directly. Token limits aren’t a technical footnote. They’re the architectural fact that determines whether your content gets cited, summarized, or silently discarded.

Context Windows Don’t Determine What Agents Actually Read

Modern language models advertise context windows measured in hundreds of thousands of tokens. GPT-4o handles 128,000. Claude 3.5 handles 200,000. It’s tempting to assume that means an AI agent will happily consume an entire website and synthesize it. That’s not how deployed agents work in practice.

Most AI systems that retrieve web content use a retrieval-augmented generation (RAG) architecture. The agent doesn’t read your page from top to bottom. It queries a vector database, pulls the passages most semantically relevant to the query, and feeds only those passages into the model’s active context. The effective reading window for any single passage runs between 375 and 1,500 words.

Your content competes passage by passage, not page by page.

The agent isn’t evaluating whether your article is good. It’s evaluating whether a specific block answers the query it’s trying to resolve.

Sequential Content Architecture Fails at Passage-Level Extraction

Oomph’s GEO audit work across clients in multiple verticals has surfaced one consistent pattern: the passages that earn AI citations contain a complete unit of information within 150 to 300 words, with claim, evidence, and implication all present. Passages that require surrounding context get retrieved less often, and cited almost never.

The explanation is structural. Most web content is written to be read in order. Context builds across sections. Arguments develop over paragraphs. Evidence appears after setup. Sequential structure serves readers who move through an article from beginning to end. AI retrieval systems pull individual passages without surrounding context, which means content that relies on sequential reading will fail at the extraction stage.

When a RAG system pulls a passage from your article, it gets that passage without surrounding content. If your best insight sits in paragraph four of section three, after two paragraphs of setup and a transition, the retrieved passage is incomplete. The agent gets the insight without the framing that makes it intelligible. It can’t cite what it can’t understand in isolation.

Token-Aware Content Architecture Prioritizes Information Density Over Narrative Flow

SEO-first content prioritizes keyword density, internal linking, and time-on-page signals. Token-aware content organizes around a different variable: how much answerable information exists per unit of text, and whether each block can stand alone.

The practical difference shows up in four places.

Opening sentences carry the full answer. AI retrieval systems, including those powering Perplexity, ChatGPT search, and Google’s AI Overviews, are trained to extract the first one to two sentences of a passage as the primary answer candidate. If your opening sentence is context-setting (“The world of digital marketing has changed dramatically…”), that slot is wasted. If it’s answer-first (“Brands that structure content for passage-level extraction appear more frequently in AI-generated responses across the major platforms”), the agent has something to pull.

Headers state findings, not topics. “Content Strategy Best Practices” tells an AI agent nothing about whether this section answers its query. “Passage-Dense Content Gets Retrieved More Often Than Narrative-First Content” gives the agent a decision signal before it reads the body text. Header specificity is a retrieval signal, not just a UX preference.

Paragraph length maps to token chunks. Most RAG implementations chunk content at natural paragraph breaks. A 600-word paragraph becomes a single chunk that may or may not surface as a coherent answer. Five 120-word paragraphs, each containing a discrete claim with evidence, become five distinct retrieval candidates that an agent can evaluate independently.

Lists and tables survive extraction better than prose. Structured data holds up under chunking because each list item or table row is a self-contained unit. Narrative that relies on transitional connectives (“building on that point,” “as we saw above”) breaks when extracted from context.

None of these principles require abandoning good writing. They require front-loading the substance. The writer who saves the insight for the closing paragraph is writing for suspense. The content that gets cited leads with the answer.

Technical Signals Tell Agents Where to Look and What to Trust

Content structure gets you into the retrieval pool. Technical signals affect whether you’re weighted toward the top of it.

The llms.txt standard is the clearest example of a technical signal designed specifically for AI agents. A file placed at your domain root tells AI crawlers which content is authoritative, which is supplementary, and which sections are meant to inform rather than be cited. Oomph has implemented llms.txt across multiple client properties. The consistent finding is that agents using this signal weight the flagged authoritative content over other content on the same domain that isn’t marked up.

Structured data functions as a secondary retrieval signal. An FAQ schema turns a list of questions into machine-readable answer pairs. An Article schema with explicit author attribution, publication date, and about markup gives an AI agent metadata that affects both retrieval ranking and citation confidence. Agents are more likely to cite content when they can verify its provenance without inference.

Robots.txt deserves specific attention here. Blocking AI crawlers with a broad disallow rule does more than limit indexing. It determines whether any AI system trained on web crawl data ever incorporates your content into its model weights. Companies that blocked AI crawlers in 2023 and 2024, reasoning that they didn’t want their content used for training, may now find themselves underrepresented in AI responses across platforms they didn’t anticipate. The decision to block or allow specific crawlers (GPTBot, ClaudeBot, Anthropic-ai, PerplexityBot) affects citation share of voice, not just training data.

A Token-Aware Content Audit Finds Three Failure Modes Every Time

Running a token-aware audit on an existing content library typically surfaces the same problems across clients and verticals.

The first is setup debt. A significant portion of most articles’ opening sections contains no retrievable information: context-setting, background, and framing that made sense in a sequential reading model. An audit quantifies this debt and flags it for rewrite priority.

The second is information burial. High-value claims, the specific sourced insights that AI agents want to cite, frequently appear in the middle or end of articles. This is a holdover from the long-form content era of 2012–2016, when longer articles ranked better and writers front-loaded engagement hooks rather than answers. An audit maps where citable claims live relative to passage boundaries.

The third is structural mismatch. Social sharing content follows emotional arcs: story, tension, release, punchline. That pattern performs poorly under AI retrieval. An audit distinguishes between content that should keep its social-sharing structure and content that should be restructured for agent consumption, and flags which pieces warrant investment in both.

The Gap Still Favors Brands That Restructure First

The signals that drove content strategy for the past decade (keyword rankings, time-on-page, backlink profiles) don’t disappear. A new constraint joins them, one that’s structurally different from anything in traditional SEO: can an AI agent extract a complete, citable unit of information from your content without reading the whole article?

That question has a concrete answer for every piece of content on your site. Each passage either holds up in isolation or it doesn’t. The same binary applies to every header and every technical signal on the page.

The brands showing up in AI-generated responses right now aren’t necessarily the ones with the best content. They’re the ones whose content happens to be structured the way AI agents retrieve it. The gap between those groups is still wide enough that structural changes move fast. It won’t stay that way.

Ready to find out how your content holds up under AI retrieval? Oomph’s GEO audit process maps exactly where your content gets cut, buried, or missed, and what to restructure first. Get in touch with our team to start with a token-aware content audit.

Brands with strong traditional SEO rankings are getting skipped by ChatGPT, Perplexity, and Google’s AI Overviews. Not because their content is bad, but because it’s structured for a retrieval system that AI pipelines don’t use. Four days at SEO Week NYC 2026 laid out exactly what’s broken and what fixes it.

You’ve likely watched this play out already: rankings hold, but traffic from AI-generated answers goes to competitors. Or a prospect mentions they “looked it up” before calling, more recently meaning they asked an AI. The problem isn’t your SEO. It’s that the signals AI systems use to decide what to cite are often different than the signals that determine traditional rankings, and most brand sites haven’t been built for them yet.

AI Systems Filter Out Most Content Before They Ever Read It

AI retrieval pipelines evaluate content through a series of eligibility checks before a model ever sees it. Content that fails those checks can’t be cited, regardless of its quality or ranking. Krishna Madhavan, Principal Product Manager at Microsoft AI, opened the conference by describing what he called the “invisible, converged web”: a layer of grounding confidence scores, safety filters, publisher controls, and attribution signals that sits between your content and the AI systems your audience is using. If your content doesn’t carry the right signals, it gets filtered out at that layer. It never reaches the model.

This is the structural gap most brands don’t know they have. A page can rank on the first page of Google and be completely absent from AI-generated answers on the same topic, because ranking signals and retrieval eligibility signals are different. Google evaluates pages. AI pipelines evaluate whether individual content blocks are structured, sourced, and verified enough to ground a response. Madhavan’s framing was precise: modern SEO and GEO now feel less like ranking tactics and more like a distributed systems challenge, where the goal is coordinating the signals that let AI pipelines trust and reuse your content.

In every GEO audit we run at Oomph, most brand sites are missing at least two of those signals. The most common gaps are invisible in standard SEO tooling, which is exactly why brands with strong traditional performance are still surprised when their AI citation share is near zero.

Traffic Metrics Will Lie to You About Your AI Search Performance

AI Overviews, ChatGPT, and Perplexity are answering questions in your category and not sending traffic to your site, but your analytics won’t show you that as a problem, because there’s no traffic to track. Jori Ford, Chief Marketing and Product Officer at FoodBoss, introduced the HEO (Hybrid Engine Optimization) framework at SEO Week specifically to address this measurement gap. Her Hybrid Engine Score is a weekly composite that tracks both traditional ranking performance and AI citation performance in a single number. Measuring them separately, or measuring only one, gives you an incomplete and often misleading picture of your actual search visibility.

Dale Bertrand of Fire&Spark extended this into the CFO conversation that most marketing leaders are currently losing. If your traffic is down but AI-influenced conversions are up, you’re actually winning. GA4 misses most AI-driven attribution, so you look like you’re failing. Bertrand’s work with global brands showed that revenue-focused GEO consistently produces stronger business outcomes than traffic-focused SEO when you measure far enough downstream. The brands building that measurement and implementation frameworks now are the ones who’ll be able to defend AI search investment in 12 months, when leadership starts asking why organic traffic hasn’t recovered.

“Revenue-focused GEO consistently produces stronger business outcomes than traffic-focused SEO.”

A Weak Paragraph Loses to a Strong One Every Time

AI systems retrieve at the paragraph level, not the page level, which means every paragraph on your site now competes independently to be cited. Mike King’s session was the most direct of the conference on this point. His framing: Google has been operating semantically for over a decade, most SEO tooling still does keyword math, and the gap between what tools measure and what AI systems actually evaluate has become the opportunity for brands willing to close it. A well-structured, well-evidenced paragraph on a thinner site gets cited ahead of a buried, unfocused paragraph on a high-authority domain.

The practical consequence for your content team is specific. Paragraphs need to open with their conclusion, not build toward it. Each paragraph should address one provable idea with enough context that it can be extracted and stand alone. Sourcing needs to be explicit and named. An unnamed statistic is an assertion an AI system can’t ground.

The brands we work with who’ve rebuilt their content architecture around these requirements are seeing measurable improvement in AI citation rates within 60–90 days. That improvement shows up in AI Overview appearances and third-party platform citations before it shows up in traffic numbers.

Four Technical Requirements Now Sit Between Your Content and AI Citation

Structured, sourced, crawlable, and machine-readable content gets cited by AI systems. Content missing any one of those properties gets filtered before retrieval. Andrea Volpini, CEO of WordLift, described the staged retrieval process AI systems use: models don’t consume your site whole, they pull from a pre-filtered subset of content that met a minimum bar for structure and verifiability. Content that isn’t structured, connected, and verifiable gets excluded from that subset, regardless of how good it is as writing.

The four technical requirements that determine whether your content clears that bar are: AI crawler access confirmed in your robots.txt; schema markup that is complete, accurate, and present on key pages; an llms.txt file that correctly tells AI agents what they can use from your site; and content blocks written so individual paragraphs can stand alone as cited answers. In every GEO audit we run at Oomph, most brand sites are missing at least two. None of these are advanced configurations. They’re the new baseline for being retrievable. Platforms like Scrunch can show you exactly where you stand across ChatGPT, Perplexity, Gemini, and Google AI Overviews: which prompts surface your brand, which source pages are driving citations, and where competitors are getting cited in your place.

The Brands That Close This Gap First Will Be Significantly Harder to Displace

AI citation compounds the same way that traditional search authority compounds. Brands that establish consistent citation history build a signal advantage that takes competitors real time to close. The difference from traditional SEO is that the window to build that advantage early is shorter, because the field is moving fast and the gap between brands actively building for AI retrieval and brands waiting to see how it develops is widening every month.

The sequencing for closing that gap is straightforward. Technical access for AI crawlers comes first, because you can’t be cited if you can’t be read. Schema markup and structured data come next, because they’re the verification signals AI systems use to trust your content.

Passage-level content architecture follows. Reformatting existing strong content for standalone paragraph retrieval is often faster than creating new content. Third-party brand presence on the platforms AI models train on comes last, because it’s the authority signal that determines whether an AI system treats your content as a credible source or skips it in favor of one it recognizes.

At Oomph, we run GEO audits that score your site across all four of these dimensions and return a prioritized 30-day action plan. If you’re not sure where your brand stands on AI visibility, the audit tells you exactly which gaps are costing you citations right now. Talk to us about a GEO audit.

Healthcare organizations that don’t structure their content for AI retrieval are already losing patients before the first visit. Tools like ChatGPT, Perplexity, and Google’s AI Overviews have become a first stop for health questions. They pull answers directly from web content without sending users to a website. If your organization’s content isn’t structured to show up in those answers, you’re invisible at the moment patients and caregivers are most actively searching.

Answer Engine Optimization (AEO) is the practice of structuring content so AI systems can find it, understand it, and cite it. It’s distinct from traditional SEO, though the two aren’t in conflict. Understanding the difference matters for every healthcare communicator making content decisions right now.

AI Search Engines Retrieve and Synthesize, They Don’t Rank and Link

Traditional SEO optimized full pages for rankings. A page with strong domain authority, good keyword coverage, and solid backlinks would surface near the top of a results page. Users would click through to read it. That model still works for many queries, but it’s no longer the whole picture.

If you’re weighing how SEO and generative engine optimization fit together, the distinction is worth understanding clearly.

AI answer engines don’t rank pages. They retrieve specific passages from across the web, synthesize an answer, and present it directly to the user. The user often never clicks through to the source. According to SparkToro’s 2024 zero-click search study, nearly 60% of Google searches end without a click. For healthcare communicators, that means a significant portion of your potential audience is forming opinions about their health, their options, and their providers without ever landing on your site. AI-generated answers accelerate that trend.

Content strategy decisions right now should account for whether your content is structured so AI systems can extract a clear, direct answer from it, not just whether it ranks.

Healthcare Authority Helps, But Structure Is What Gets You Cited

Health information is a high-stakes category for AI systems. Google classifies health, finance, and legal content as YMYL (Your Money or Your Life) because inaccurate answers carry real consequences. AI systems tend to be more selective about which sources they retrieve and cite in these categories.

That selectivity works in favor of established healthcare organizations. Hospitals, health systems, and credentialed clinics carry demonstrated authority, and that matters more in YMYL retrieval than in general content. But authority alone isn’t enough. Content still has to be structured correctly to be extracted. A well-credentialed source with poorly structured content will lose to a less-credentialed source that’s written in a way AI systems can parse.

Most healthcare organizations already have the credibility AI systems favor. That means the path to better retrieval runs through content structure, not authority-building.

What AI Systems Actually Look For in Content

AI retrieval systems evaluate each paragraph independently, treating it as a standalone candidate for citation. A page with a strong introduction and weak middle sections will have the strong introduction cited and the rest ignored. This changes how content needs to be written.

Passages that get retrieved share a common structure. They open with a direct, declarative answer to a specific question. They use plain language rather than jargon. And they don’t require surrounding context to make sense.

A paragraph that opens with “There are many factors to consider when evaluating treatment options” is hard for an AI system to use. A paragraph that opens with “Most patients with early-stage [condition] have three primary treatment options” gives the system something it can extract and cite directly. That’s the foundation of citation-ready content architecture, and it’s the standard healthcare organizations should be building toward.

Schema markup also plays a meaningful role. Structured data signals to AI systems how to categorize and use your content. Three schema types matter most for healthcare organizations: FAQ schema for patient question pages, MedicalCondition schema for clinical content, and HowTo schema for procedural or instructional pages. Organizations that have implemented structured data on their clinical and service pages have a measurable advantage in AI retrieval over those that haven’t.

The Patient Journey Now Runs Through AI Before It Reaches You

Patients and caregivers typically begin with a question typed into an AI tool or search engine, well before they consider visiting a specific organization’s website. By the time they reach your site, they’ve already formed an understanding of their condition, their options, and what they’re looking for based on whatever content those tools surfaced.

Whether your organization is part of that pre-visit understanding depends entirely on whether your content was present in the AI’s answer. If it wasn’t, a competitor’s content filled that space instead.

For healthcare marketers, showing up in AI answers is about whether your organization is part of the conversation patients are having before they ever contact you. That matters well beyond traffic metrics.

Where to Start: Four Practical Priorities

Most healthcare organizations don’t need to rebuild their content from scratch. They need to identify where their existing content is close to being retrievable and close the gap. Four areas consistently make the biggest difference.

Audit your highest-traffic clinical and service pages for passage structure. Read the first sentence of every paragraph on each page. If those sentences don’t directly state the main point of that paragraph, the content isn’t structured for AI retrieval. Rewriting opening sentences to lead with the conclusion is often the fastest improvement available.

Build out FAQ content with direct, complete answers. FAQ pages are one of the most reliably retrieved content formats in AI search because they’re structured around specific questions with discrete answers. Healthcare organizations that publish clear FAQs on common patient questions, symptoms, procedures, recovery, cost expectations, give AI systems exactly the format they’re looking for.

Implement structured data on clinical pages. If your web team hasn’t added schema markup to your clinical and service pages, that’s a near-term technical priority. The implementation isn’t complex, but it requires coordination between your content team and whoever manages your CMS.

Prioritize topical depth over topical breadth. AI systems favor sources that demonstrate consistent depth on a topic over sources that cover many topics superficially. For healthcare communicators, this means investing in comprehensive content on your core service lines rather than spreading thin across every health topic your organization touches.

The same characteristics that make content useful for AI retrieval, clear structure, direct answers, demonstrated depth, make content better for human readers too. Raising the standard in one area raises it across the board.

Oomph will be at NESHCo May 27–29 in Burlington. If you’re headed there too, we hope to see you.

Summary

Most organizations are treating SEO and Generative Engine Optimization as two separate disciplines – and wasting resources in the process. The real strategic question is not which channel to optimize for but whether your content is built to be reused: extracted, synthesized, and cited by both search algorithms and AI answer engines. We call this Citation-Ready Content Architecture – a unified approach where structure, authority, and specificity make content perform across every discovery surface simultaneously. Organizations in regulated industries face compressed timelines: healthcare queries already trigger AI Overviews on nearly half of all searches.

Sixty percent of Google searches now end without a click. That number is not a forecast – it is a 2025 finding from Bain & Company. Meanwhile, Gartner predicts traditional search volume will drop 25% by the end of 2026 as users migrate to AI-powered answer engines. And here is the statistic that should change how you think about your content strategy: according to Ahrefs, 80% of URLs cited by ChatGPT, Perplexity, and Copilot do not rank in Google’s top 100 results for the original query.

That last data point is the one most SEO-vs.-GEO articles ignore. If the overlap between traditional rankings and AI citations were nearly complete, you could optimize for one and trust the other to follow. It is not. The two discovery channels draw from overlapping but meaningfully different content signals. Treating them as a single problem or two separate problems are both the wrong framing.

Why Is the “SEO vs. GEO” Framing Wrong?

Because it implies a choice between two competing strategies, when what actually matters is a single architectural principle applied across both.

SEO optimizes content for ranking position – getting your page onto a results list a human scans and clicks. GEO – Generative Engine Optimization, a term formalized by researchers at Princeton, Georgia Tech, and IIT Delhi in 2024 – optimizes content so AI systems can retrieve, synthesize, and cite it when generating answers. The Princeton study demonstrated that GEO techniques can boost content visibility in AI-generated responses by up to 40%, and that the most effective strategies vary by domain.

The difference is real. But the industry conversation has overcorrected, treating GEO as something exotic that requires a fundamentally new playbook. As Entrepreneur reported in April 2026, teams are making preventable mistakes by treating GEO “like an exotic new discipline” and shifting budget away from technical SEO into untested “AI visibility hacks.” Research from AirOps found that pages ranking number one in Google were cited by ChatGPT 3.5 times more often than pages outside the top 20.

Strong SEO remains the foundation. GEO is the structural extension that makes your existing authority legible to AI systems. They are not two strategies. They are one architecture.

What Makes Content “Citation-Ready” for Both Search and AI?

Citation-Ready Content Architecture is the practice of structuring content so it simultaneously ranks in traditional search results and gets extracted and cited by AI answer engines. It is not a new technology stack or a separate editorial workflow. It is a design principle: every piece of content your organization publishes should be built for reuse from the start.

Three characteristics define citation-ready content:

Modular structure. AI systems do not read your article top to bottom and decide whether to cite the whole thing. They extract passages – a definition, a statistic, a direct answer to a question. Content with clear headings, self-contained sections, and answer-first paragraphs gives both search engines and AI systems clean material to work with. The Princeton GEO study found that adding statistics to content improved AI visibility by 41%, and citing credible sources improved it by 115% for lower-ranked pages.

Demonstrated authority. Seer Interactive’s September 2025 study of 3,119 queries across 42 organizations found that brands cited in AI Overviews earned 35% more organic clicks and 91% more paid clicks than those not cited. Authority is no longer just a ranking signal – it is the qualification for being included in AI-generated answers at all. Author credentials, original research, linked sources, and topical depth are now dual-purpose investments.

Specificity over generality. AI systems select content that provides extractable facts – numbers, definitions, named frameworks, concrete comparisons. Content that gestures vaguely at a topic (“there are many factors to consider”) gets skipped in favor of content that states something specific and citable. We have written previously about how LLMs index and use content – the same accessibility and structural principles that help AI crawlers parse your pages also make your content more citation-worthy.

Why Are Healthcare and Higher Education Hit Hardest?

Because AI Overviews appear at disproportionately high rates for the query types these industries depend on – and the consequences of being absent or misrepresented are far more serious than lost traffic.

Conductor’s Q1 2026 analysis of 21.9 million searches found that healthcare queries trigger AI Overviews at a rate of 48.75% – nearly double the overall average of 25%. Technology queries trigger at roughly 30%. For healthcare organizations and universities, AI is already mediating nearly half the informational queries that drive patient acquisition and enrollment.

The real-world impact is already measurable. U.S. News reported in March 2026 that nearly 80% of people searching for degree information read Google’s AI Overviews, and many never click through to an institution’s website. The University of Maryland Global Campus responded by using AEO and GEO techniques to revise its degree pages and A/B test FAQ-style content. Johnson County Community College found that while AI-driven traffic represents less than 1% of its website visitors, engagement from that group is 59% above its site-wide average – suggesting AI-referred visitors arrive further along in their decision-making process.

For healthcare, the stakes go beyond enrollment. When AI engines synthesize clinical information, the accuracy of that synthesis depends on the quality and structure of the sources available. Organizations that have not optimized their content for AI citation are not just losing visibility – they are ceding authority over how their expertise gets represented to patients who increasingly trust AI-generated answers.

What Does the HubSpot Collapse Tell Us About This Shift?

That traffic built on loosely related content is structurally fragile in an AI-mediated search environment.

Multiple industry analyses documented an approximately 80% traffic drop across HubSpot’s blog properties as AI Overviews began answering the high-funnel informational queries that had driven HubSpot’s organic growth for over a decade. Pages about “famous sales quotes” and “cover letter examples” had driven enormous traffic but had minimal connection to HubSpot’s core CRM platform. When Google’s algorithm update prioritized content closely tied to a website’s core expertise, and AI Overviews began answering those generic queries directly, the traffic evaporated.

The lesson is not that content marketing failed. It is that content disconnected from your organization’s core authority is exactly the kind of content AI systems will summarize without ever sending a visitor your way. In our GEO optimization Q&A, we outline why organizations should start with their highest-authority content when optimizing for AI visibility rather than trying to cover every possible keyword.

For organizations in regulated industries – where your content is tightly tied to your institutional expertise by design – this is actually an advantage. A hospital publishing evidence-based patient education content is inherently closer to citation-ready than a SaaS company publishing tangentially related blog posts for traffic volume. The structural alignment is already there. What is often missing is the formatting and schema work that makes it extractable.

What Should Content Teams Do First?

Start with what you already have. The gap between SEO-optimized content and citation-ready content is usually structural, not substantive.

1. Audit your top 20 pages for extractability. Read the first paragraph of each section in isolation. Does it directly answer a question someone would ask an AI tool? If not, restructure it. AI systems and Google’s AI Overviews pull from the opening sentences of well-structured sections – bury your answer three paragraphs deep and it will not get cited.

2. Add the schema AI systems actually use. Implement FAQPage, Organization, Article, and author schema across your priority content. BrightEdge found that sites implementing structured data and FAQ blocks saw a 44% increase in AI search citations. Author schema is especially high-impact: websites with author schema are 3x more likely to appear in AI answers.

3. Track AI visibility alongside traditional rankings. Oomph’s GEO Analytics and Reporting service configures tracking in GA4 and Google Search Console to monitor AI bot traffic and AI-generated search impressions that standard analytics miss. At minimum, create referral segments for chat.openai.com, perplexity.ai, and other AI platforms, and watch for the signature pattern of rising impressions with declining clicks – the clearest signal that AI is summarizing your content without sending traffic.

The organizations that will maintain visibility over the next two years are not the ones choosing between SEO and GEO. They are the ones building content that works across both discovery surfaces from the start – structured for extraction, grounded in genuine expertise, and specific enough that AI systems treat it as source material rather than background noise.

That is not a new content strategy. It is the old one, built to the standard the new environment actually requires.

Summary

Most content strategies optimize for one outcome: ranking. Ranking is only half the visibility equation now. Citation-Ready Content Architecture, developed at Oomph, helps organizations build content that performs across traditional search results and AI-generated answers simultaneously. It rests on three principles – modular structure, demonstrated authority, and extractable specificity – and we apply it with clients in healthcare, higher education, and government where being cited accurately is as important as being found.

This crystallized during a client conversation earlier this year. We were looking at their analytics – a major healthcare organization – and the pattern was striking. Impressions were climbing. Rankings were stable. But clicks were dropping steadily, month over month. The content was being surfaced by Google, but patients were getting their answers from AI Overviews without ever visiting the site.

That’s a visibility problem most of us weren’t trained to solve – and it requires a different content architecture.

Gartner predicts traditional search volume will drop 25% by the end of 2026 as users migrate to AI-powered answer engines. Ahrefs found that 80% of URLs cited by ChatGPT, Perplexity, and Copilot don’t rank in Google’s top 100 for the original query. And the Pew Research Center’s study of 68,879 actual Google searches found that only 8% of users clicked a traditional result when an AI Overview appeared, compared to 15% without one – roughly half the click-through rate.

Content that ranks and content that gets cited aren’t always the same – but they can be, if you build for both from the start. That’s Citation-Ready Content Architecture.

What Is Citation-Ready Content Architecture?

Citation-Ready Content Architecture is the practice of structuring digital content so it simultaneously ranks in traditional search engine results and gets extracted, synthesized, and cited by AI answer engines like ChatGPT, Google AI Overviews, and Perplexity. Developed by Oomph as a framework for regulated industries, it combines modular content structure, demonstrated authority signals, and extractable specificity into a unified content design principle – replacing the need to maintain separate SEO and GEO strategies.

The key word in that definition is “simultaneously.” That means content architecturally designed to work across every discovery surface – ranked results, AI summaries, voice assistants, whatever comes next – because the underlying structure supports all of them.

In our work with clients across healthcare, higher education, and government, we’ve found this transition isn’t a massive lift for organizations with strong content fundamentals. The gap between SEO-optimized and citation-ready content is structural, not substantive – it’s about how content is organized, not whether it’s good.

Why Do Organizations Need a New Content Architecture Now?

Information discovery has forked. Content built for only one path leaves visibility on the table.

Two parallel discovery systems now exist. Traditional search ranks your content in a list users scan. AI-powered answer engines synthesize information from multiple sources into a single response – often without the user ever clicking through to your site.

The research is unambiguous. The foundational Princeton GEO study demonstrated that content optimized for generative engines can boost visibility by up to 40% in AI responses. But it also showed that the most effective strategies vary by domain – what works for a law firm doesn’t necessarily work for a children’s hospital. A March 2026 study from researchers at the University of Tokyo found that structural optimization alone – independent of content changes – improved citation rates by 17.3% across six major generative engines.

The most striking finding: research from AirOps found that pages ranking number one in Google were cited by ChatGPT 3.5 times more often than pages outside the top 20. Strong SEO remains the foundation. Citation-ready architecture is what makes that foundation legible to AI systems too.

What Are the Three Principles of Citation-Ready Content?

The framework rests on three principles. Each serves both search engines and AI systems simultaneously – that dual purpose is the point.

Modular structure

AI systems don’t read your article start to finish and decide whether to cite the whole thing. They extract passages – a definition, a data point, a direct answer to a specific question. Content with clear headings, self-contained sections, and answer-first paragraphs gives both search algorithms and AI systems clean material to work with.

We’ve written about how LLMs index and use content – and the takeaway is that the same accessibility principles that help AI crawlers parse your pages also make your content more citation-worthy. Semantic HTML, logical heading hierarchies, and sections that can stand on their own aren’t new concepts. They’re just worth more now than they’ve ever been.

Demonstrated authority

Being cited by AI systems has become a meaningful competitive advantage. BrightEdge found that sites earning citations inside AI Overviews see CTR increases of up to 35% compared to traditional organic rankings alone. Websites with author schema are 3x more likely to appear in AI answers, and sites implementing structured data and FAQ blocks saw a 44% increase in AI search citations.

In practice, demonstrated authority means: Author credentials on every piece. Original data and research when you have it. Linked sources for every claim. Topical depth across related content – not one-off articles, but interconnected clusters that demonstrate sustained expertise.

Authority isn’t just a ranking signal – it’s the entry qualification for AI inclusion.

Extractable specificity

This is the one that separates citation-ready content from content that’s merely well-written. AI systems select content that provides extractable facts – numbers, definitions, named frameworks, concrete comparisons. Content that gestures at a topic (“there are many factors to consider”) gets skipped in favor of content that states something specific and citable.

The Princeton study found that adding statistics to content improved AI visibility by 41%, and citing credible sources improved visibility by 115% for lower-ranked pages. That 115% figure is significant: it means content that isn’t winning the traditional ranking game can still earn AI citations by being specific and well-sourced.

How Does This Apply Differently in Regulated Industries?

For regulated industries, the stakes are higher and the timeline compressed – but the structural fit is actually better.

Conductor’s Q1 2026 analysis of 21.9 million searches found that healthcare queries trigger AI Overviews at a rate of 48.75% – nearly double the overall average. For healthcare organizations and universities, AI is already mediating close to half the informational queries that drive patient acquisition and enrollment.

The structural advantage for regulated industries is real. Organizations in regulated industries – healthcare systems, universities, government agencies – produce content that’s inherently tied to their institutional expertise. A hospital publishing evidence-based patient education content is structurally closer to citation-ready than a SaaS company publishing tangentially related blog posts for keyword volume. The authority is real. The specificity is built in by the nature of the content. What’s typically missing is the formatting and schema work that makes it extractable.

When we optimize content for GEO, the biggest wins often come from restructuring content that already exists – not creating new content from scratch.

What Should You Do First to Make Your Content Citation-Ready?

Start with what you have. The gap is almost always structural, not substantive.

1. Audit your top 20 pages for extractability. Read the first paragraph of each section in isolation. Does it directly answer a question someone would ask an AI tool? If it doesn’t, restructure it. AI systems pull from the opening sentences of well-structured sections. Bury your answer three paragraphs in and it won’t get cited.
2. Implement the schema that AI systems actually use. FAQPage, Organization, Article, and author schema across your priority content. Author schema is especially high-impact – BrightEdge’s research shows it triples your likelihood of appearing in AI answers.
3. Track AI visibility alongside traditional rankings. Oomph’s GEO Analytics and Reporting service configures tracking in GA4 and Google Search Console to monitor AI bot traffic and AI-generated search impressions. At minimum, watch for the pattern of rising impressions with declining clicks – that’s the clearest signal that AI is summarizing your content without sending visitors.
4. Build for reuse from the start. Every new piece of content should include at least one standalone definition, one specific data point, and one direct answer to a question your audience would ask an AI tool. Make it easy for AI systems to cite you. That’s the architecture.

In 20 years of building digital experiences, I’ve watched a handful of shifts fundamentally change how content needs to be structured. Mobile was one. Accessibility-first was another. The shift to AI-mediated discovery is the next.

Citation-Ready Content Architecture isn’t a bolt-on to your existing strategy – it’s the design principle that makes your existing strategy work across today’s fragmented discovery environment. Organizations that build for it now will compound that advantage as AI-mediated search grows. Those that wait will be optimizing for a world that has already moved on.

We’re helping clients across healthcare, higher education, and government make this shift. If your analytics show that pattern – impressions climbing, clicks dropping – start here.

As direct website traffic decreases and LLMs slurp up text from multiple sources to mix together and redistribute to users, it has never been more important to maintain high-quality online content. A ROT analysis — which stands for Redundant, Obsolete, Trivial — is a framework through which we can evaluate site content to improve it for usability, SEO, retrieval, and GEO. 

This is a flexible exercise that can apply to a variety of digital properties: web pages, PDFs, intranets, social media pages, call center databases, support knowledgebases… Anywhere that you, as an organization, are speaking to your audience, you have an opportunity to share knowledge, build trust, and solidify your brand image.

Similarly, ROTten content can mislead users, seed doubt, and damage your reputation.

When you use a ROT analysis to kickstart a content clean-up project, you’re ensuring that users and bots alike find only your latest, clearest, most accurate and relevant information. When done properly, it can even set up your team for better content production and management in the future.

How Oomph Approaches Content ROT Analyses

Every ROT analysis looks a little different depending on the industry, content, and what a particular audience needs. 

Make a Plan

Before jumping into dashboards and spreadsheets, we start with a conversation. With any project, we need to understand what problems your organization needs to solve: What’s important to you and your users? Where are you struggling? This is our chance to understand the why behind your content.

As we learn more about what you need, we’ll define what ROT is for your organization. What existing policies do you have in place around archiving old or outdated content? If you don’t have policies, what makes sense for you? What key user journeys should the analysis focus on? We’ll answer these questions and more to make sure we’re going into the analysis with a clear vision of what your content should look like so we can see where it’s missing the mark.

Find the ROT

Let’s get into what ROT looks like specifically and where we look for it.

Redundant means the content communicates information in more than one place. This can result in an inefficient information architecture and messy user paths. There are times duplicate content can be helpful, like when separate task flows require some of the same information. That’s why it’s important to know upfront what journeys are most important to prioritize. In these cases, when the same content shows up in multiple places across a website or app, it’s important to have a method for keeping all content in sync. If it’s possible to edit this content in a single place while distributing it across multiple pages, that can be a great method for maintaining a single source of truth.

Redundant might also refer to several articles written over time that deal with the same topics in similar ways. This can result in the newest content on the topic having its SEO/GEO cannibalized by older content on the same topic. Users might more easily find older content when you want them to find the latest. 

Obsolete content includes outdated information, language, and (probably broken) links. This type of ROT is especially damaging when it’s related to products, services, or something users are trying to take action on. It’s important to keep in mind your entire digital landscape; Maybe you’ve updated the content on your main service page, but did you remember to update automated emails, support articles, and meta descriptions? What pages aren’t built directly into a user flow but can still be found by Google? 

Consider whether it makes sense to archive or unpublish old content, like past news and events. And consider your audience: Is there a reason users would be looking for a historical record, and is that need strong enough to justify keeping it available? If you do choose to keep outdated information published, make sure that it’s clear to users that the content is old and consider providing a link to the latest version.

Trivial content can be harder to define and is highly subjective based on the organization. This might look like “fluff” pieces shared for the sake of SEO or maintaining a publishing schedule, or excessive marketing language that ultimately doesn’t serve you or your users. It might be low-traffic fine print details that apply to a specific audience who typically finds it another way. Maybe it’s content that is related to but outside of your core business function. You’ll need to make some decisions about what is important to you. 

To find ROT, we’ll use a variety of collection and measurement tools. SortSite, Screaming Frog, and Siteimprove can locate broken links, orphaned pages, and other SEO issues. Google Analytics, Hotjar, Contentsquare, and MS Clarity can show common user flows and help identify trivial content. Data from these tools can also prioritize the analysis by surfacing what content is most important to users. If a page gets a lot of traffic, we know that it needs to be clear, up-to-date, and accurate. If a page isn’t visited much, we need to ask whether it should be more highly trafficked, consolidated with higher performing content, or removed.

Deliverables and Next Steps

After all this sorting and evaluating, you might be wondering what you’ll tangibly get out of the process. We know content teams are busy, and going through a review can feel like adding more work to the pile. How can we help prioritize meaningful progress here?

The big outcome is one of my personal favorites: a clean, annotated, actionable spreadsheet. Specifically, we’ll put together an audit of your content with links, page titles, notes on whether the content falls into any of the three ROT categories, and what to do about it: keep, modify, combine, or delete. Depending on the tools your content team uses or what you are willing to subscribe to, we might prepare dashboards and reports directly within an app that your team can use as an ongoing progress tracker. Wherever this list of to-do’s lives, we’ll help you prioritize it so you can start ticking off the most crucial items. Depending on what we decided in early scoping agreements, we can even help work through some high-impact issues, like bulk deleting content, suggesting rewrites, and fixing broken links.

We can also set up an ongoing content hygiene plan. While a dedicated content ROT analysis is a great way to identify and work through issues, an effective content plan should prevent ROT as much as possible and reduce the need for a large effort in the future. This might involve setting up policies, practices, and tools to guide future content management. We’ll help you find ways to see the bigger picture when updating or developing new content to make sure all pieces are accounted for. And when ROT falls through the cracks, you’ll have a plan to regularly review site content, setting ahead of time the when, what, and who.

One Piece in the Puzzle of Strong Content

As we continue to inspect the quality of your website and other digital properties, we can use this ROT analysis as a jumping off point. The initial audit may lead directly into a deeper content audit to evaluate URL paths, heading usage, performance metrics, reading level, and more. As we consider reworking, combining, and cutting entire pages, we may find the need to restructure your information architecture and taxonomy structures, in part or in whole, informed by research exercises like card sorts and tree tests. Depending on what we’ve found in the existing content and how it needs to change, we might suggest changes to your content model, adding, modifying, or removing content types and the relationships between them.

A content ROT analysis is a flexible and fruitful way to take a fresh look at your content ecosystem. If you need help getting started, let us know. We’d love to dig in with you!

To avoid significant financial penalties, which increased on January 1, 2025 to up to $7,988 per intentional violation, your website must function as a compliant interface for consumer privacy rights. Use this checklist to assess your current standing.

1. Mandatory Homepage Links

• “Do Not Sell or Share My Personal Information”: A clear and conspicuous link must be in the footer or header if you sell or share data for targeted advertising. This includes:
  • Retargeting Ads: Uploading your email list to Facebook (Meta), Google, or LinkedIn to show ads to those specific users or to find “Lookalike” audiences.
  • Data Brokerage: Selling your email list to another company or “renting” it out for their own marketing.
  • Third-Party Analytics: Sharing email-linked identifiers with ad networks that track users across multiple unrelated websites.
• “Limit the Use of My Sensitive Personal Information”: Required if you collect sensitive data (e.g., precise geolocation, health info, or race) for purposes beyond providing the core service.
• Alternative Option: You may use a single, combined link labeled “Your Privacy Choices” or “Your California Privacy Choices” that includes an icon if desired.

2. Automated Privacy Signals (Global Privacy Control)

• GPC Detection: Your website must automatically detect and honor “Global Privacy Control” (GPC) signals from user browsers (e.g., Brave, DuckDuckGo) as a valid opt-out request.
• Status Confirmation: As of January 1, 2026, you must display a clear confirmation to the user, such as a message stating “Opt-Out Request Honored,” when a GPC signal is detected.

3. Notice at Collection

• Timely Disclosure: You must provide a notice at or before the point of collection (e.g., on a sign-up form or via a cookie banner).
• Content Requirements: The notice must list categories of personal and sensitive info collected, the specific purpose for each, and how long each category will be retained.

4. Consumer Rights Intake (DSARs)

• Dual Methods: You must provide at least two designated methods for submitting requests (e.g., a web form and a toll-free number).
• Verification: Establish a process to verify a consumer’s identity without requiring them to create a new account solely for the request.

5. Technical & Policy Maintenance

• Accessibility: All notices must follow Web Content Accessibility Guidelines (WCAG) and be available in every language in which you conduct business.
• Annual Update: The online Privacy Policy must be reviewed and updated at least once every 12 months.
• No “Dark Patterns”: Ensure the user interface is symmetrical; for example, it should not be significantly harder to “Opt-Out” than it is to “Opt-In”.

Is your website one missing link or undetected signal away from a costly CCPA violation? Oomph’s team can walk you through a compliance audit, identify gaps in your current setup, and help you implement the technical and content updates needed to protect your organization. Get in touch with us today to book your CCPA compliance call.

In recent months, Generative Engine Optimization (GEO) has been gaining attention, often positioned as the next evolution beyond traditional Search Engine Optimization (SEO). For some clients, this presents an exciting opportunity to rethink and restructure their digital content. For others, it can feel overwhelming, raising more questions than answers. As AI-powered search tools like ChatGPT, Perplexity, and Gemini change how people discover content online, clients increasingly ask: What is GEO, and how can we prepare our sites for it?

The following handy Q&A guide aims to demystify Generative Engine Optimization (GEO), explain why it matters, and provide practical steps your team can take to get started.

Q: What is GEO and how is it different from SEO?

A: GEO stands for Generative Engine Optimization. While SEO (Search Engine Optimization) focuses on getting your content to rank in traditional search engines like Google (via keywords, backlinks, and site performance), GEO focuses on getting your content mentioned, referenced, summarized, or cited in AI-generated answers from tools like ChatGPT, Gemini, and Perplexity.

Think of SEO as getting your content listed, whereas GEO is about making your brand and its content the answer.

Q: Why should my organization care about GEO?

A: AI platforms are rapidly becoming the first stop for users looking for answers, especially younger audiences and professionals. If an answer appears via Gemini on the top of a Google search, fewer people may scroll further down the page to look for other sources. They got the answer they needed from just one search. If your content isn’t optimized for these tools, you’re missing out on certain traffic data, visibility, and an opportunity to build trust. 

In 2026, ChatGPT alone sees over 4.5 billion visits per month, and Perplexity handles nearly 500 million monthly queries.

Q: How is GEO impacting my site’s analytics? 

A: Likely a lot. Generative engines often summarize content without requiring a click. That means you may see fewer impressions and clicks, even if your content is powering the AI’s answer. Most websites are seeing direct traffic declining across the board. With that said, users who do click through to sites are often engaging more deeply, leading to longer session durations and higher conversion rates. 

Because of this, it’s crucial to learn these new patterns and recognize them within your site’s analytics by setting up new reports. 

Q: How do AI engines choose which content to cite?

A: AI tools evaluate a number of factors, with the most important being:

• Authority: Are you a trusted source? Do you have backlinks, credentials, or media citations? 
• Structure: Do you use schema markup, headings, and clear Q&A formatting?
• Freshness: Is your content updated regularly?
• Relevance: Does your content align with how users ask questions in natural language?

Each tool has its own algorithm, but clear, factual, structured content with recent updates from trusted sources performs best.

Q: What kind of content works best for GEO?

A: Content that answers questions directly, especially with a conversational tone, tends to work well. Additionally, you want your content to explain not just the what, but also the why and how, since generative engines often expand on user intent. Content structures that perform well for GEO include:

• Q&A sections
• “Top” or “Best” lists (Examples: Top Restaurants in Providence, Rhode Island or Best fall events in California)
• Evergreen guides that are updated annually
• Content that is organized for machines and humans (aka clear headings, mobile-friendly, structured data and metadata)
  

Q: How can we tell if our content is being featured in AI tools?

A: While most AI platforms don’t yet provide native analytics, you can track GEO success through:

• GA4 segmentation: Filter referral traffic by sources like chat.openai.com or perplexity.ai
• Landing page patterns: AI-driven referrals often land users deep into your site (e.g., specific blogs, not just the homepage)
• Google Search Console: Look for queries with high impressions but low click-through rates, these may indicate your content is being shown in AI Overviews
• Manual Testing: In an incognito window, search for the types of queries you want your site’s content to appear for and see what answers are returned. These might be simple questions like “What does [your organization] do?” or more in-depth research questions that your popular articles have addressed. 
• Third Party Tools: As the field continues to develop, more third party tools are becoming available or adapting their analytics to provide insight into GEO success. SEMrush in particular is a tool that we recommend for clients interested in uncovering more data.

Q: Is there a way to make our site more “AI-friendly”?

A: Yes! Here are key GEO best practices:

1. Use schema markup: Help AI models understand your content’s structure and intent. You can use schema.org to help guide you through improving your site’s markup. 
2. Write in a Q&A or conversational format: More people are asking full questions or prompts in ChatGPT—rather than just listing keywords. Match your content with how users phrase queries in AI tools. 
3. Optimize your About page: Make sure that your About page is thoughtfully written to answer who you are, what you do, and why. ChatGPT, for example, pulls from these pages to assess trustworthiness and authority.
4. Refresh content: Update existing articles with new data and a clear structure (aka headings, bullets, FAQ sections, summaries). Note: You don’t need to create new URLs, just refresh the content to make sure it is relevant and current for today. 
5. Include citations and data points: Wherever possible, add data and sources. These increase your authority and credibility.
   

Q: Do we need to optimize differently for each AI tool?

A: The core strategies (trustworthiness, schema, natural language, performant) apply across all platforms, but there are nuances:

• Gemini: Heavily tied to Google’s ecosystem. Focus on crawlability and Core Web Vitals.
• Perplexity: Prefers cited, factual content and uses real-time web data.
• ChatGPT: Draws from authoritative sources like Wikipedia, news outlets, and Reddit. Strong personalization and structured content help here.
  

Q: Can we block AI tools from using our content?

A: Yes, but be thoughtful about what you are blocking. Adding a file like robots.txt can block AI crawlers, but doing so may reduce your visibility and lead to attribution from AI tools. It could also block legitimate crawlers and thus negatively impact both SEO and GEO, so be thoughtful about how you compose and format that file. 

Note: If your brand has legal or content ownership concerns, we can help you assess what should or shouldn’t be available for AI training or citation.

Q: Do AI Tools honor authenticated access?

A: Yes, but remain mindful. Models like ChatGPT can’t “log in” or bypass authentication. If full research content is only available behind a user login, it won’t be included in training data or scraped summaries. But still pay attention to how content is displayed. If your research is behind a login or subscription paywall, ensure that:

• No full-text content is available to crawlers
• Abstracts or summaries shown publicly are limited in detail

Q: What is llms.txt and should I add it to my site?

A: llms.txt is a proposed convention for websites to provide a lightweight, machine- & human-readable summary (in Markdown) of the “important” parts of the site, to help large language models (LLMs) more easily crawl, interpret, and use content. More sites are starting to add it to their sites to help guide which pages AI should pay attention to. However, it is not yet a universally supported or enforced standard. Many LLMs or AI platforms do not currently yet automatically look for or honor llms.txt. As of now, you can think of it as a nice-to-have, not a requirement.

Q: How often should we update content for GEO?

A: Best practice recommends updating at least once a year for evergreen content. Prioritize updates for:

• Posts using phrases like “top,” “best,” or “recommended”
• Pages that receive seasonal traffic or include stats
• Key content that’s losing impressions or traffic in Google Search Console

Even simple updates like reordering information, adding new facts, or improving layout can go a long way with AI engines.

Q: Is GEO just another passing trend?

A: Not at all. GEO is a direct response to how AI is changing digital search and content discovery. Platforms like Google are rethinking their search experience through tools like Gemini, as more people turn to these tools for answers. GEO is how brands stay visible in this new AI landscape.

Q: What’s the first step we should take for GEO Optimization?

A: Start with a content and schema audit of your top-performing pages. From there, apply structured markup, rewrite headlines for clarity, add Q&A sections where applicable, and refresh key posts. A phased approach focused on high-value content will have the biggest immediate impact.

Need help figuring out what content to prioritize for GEO? Our team at Oomph can assess your current visibility and build a roadmap tailored to AI performance.

For more insights into GEO optimization, read…

• Everything You Should Know About Optimizing for GEO in 2026
• How LLMs Index Your Site — and How Accessibility Improves Their Answers and Your GEO

Generative Engine Optimization (GEO) is making organizations scramble — our clients have been asking “Are we ready for the new ways LLMs crawl, index, and return content to users? Does our site support evolving GEO best practices? What can we do to boost results and citations?”  

Large language models (LLMs) and the services that power AI summaries don’t “think” like humans but they do perform similar actions. They seek content, split it into memorable chunks, and rank the chunks for trust and accuracy. If pages use semantic HTML, include facts and cite sources, and include structured metadata, AI crawlers and retrieval systems will find, store, and reproduce content accurately. That improves your chance of being cited correctly in AI overviews.

While GEO has disrupted the way people use search engines, the fundamentals of SEO and digital accessibility continue to be strong indicators of content performance in LLM search results. Making content understandable, usable, and memorable for humans also has benefits for LLMs and GEO.

How LLM systems (and AI-driven overviews) get their facts

Understanding how LLMs crawl, process, and retrieve web content helps us understand why semantic structure and accessibility best practices have a positive effect. When an AI system generates an answer that cites the web, several distinct back-end steps usually happen: 

1. Crawling — Bots visit URLs and download page content. Some crawlers execute javascript like a browser (Googlebot) while others prefer raw HTML and limit their rendering.
2. Chunking — Large documents are split into small, logical “chunks” of paragraphs, sections, or other units. These chunks are the pieces that are later retrieved for an answer. How a page’s content is structured with headings, paragraphs, and lists determines the likely chunk boundaries for storage.
3. Vectorization — Each chunk is then converted into a numeric vector that captures its semantic meaning. These embeddings live in a vector database and enable systems to find chunks quickly. The quality of the vector depends on the clarity of the chunk’s text.
4. Indexing — Systems will store additional metadata (URL, title, headings, metadata) to filter and rank results. Structured data like schema metadata is especially valuable. 
5. Retrieval — A user asks a question or performs a search and the system retrieves the most semantically similar chunks via a vector search. It re-ranks those chunks using metadata and other signals and then composes its answer while citing sources (sometimes). 

The Case for Human-Accessible Content

There are many more reasons why digital accessibility is simply the right thing to do. It turns out that in addition to boosting SEO, accessibility best practices help LLMs crawl, chunk, store, and retrieve content more accurately.

During retrieval, small errors like missing text, ambiguous links, or poor heading order can fail to expose the best chunks. Let’s dive into how this can happen and what common accessibility pitfalls contribute to the confusion.

For Content Teams — Authors, Writers, Editors

Lack of descriptive “alt” text

While some LLMs can employ machine-vision techniques to “see” images as a human would, descriptive alt text verifies what they are seeing and the context in which the image is relevant. The same best practices for describing images for people will help LLMs accurately understand the content. 

Out-of-order heading structures

Similar to semantic HTML, headings provide a clear outline of a page. Machines (and screen readers!) use heading structure to understand hierarchy and context. When a heading level skips from an <h2> to an <h4>, an LLM may fail to determine the proper relationship between content chunks. During retrieval, the model’s understanding is dictated by the flawed structure, not the content’s intrinsic importance. (Source: research thesis PDF, “Investigating Large Language Models ability to evaluate heading-related accessibility barriers”) 

Descriptive and unique links

All of the accessibility barriers surrounding poor link practices affect how LLMs evaluate their importance. Link text is a short textual signal that is vectorized to make proper retrieval possible. Vague link text like “Click here” or “Learn More” does not provide valuable signals. In fact, the same “Learn More” text multiple times on a page can dilute the signals for the URLs they point to.

Using the same link text for more than one destination URLs creates a knowledge conflict. Like people, an LLM is subject to “anchoring bias,” which means it is likely to overweight the first link it processes and underweight or ignore the second, since they both have the same text signal. 

Example of the duplicate link problem: <a href=“[URL-A]”>Duplicate Link Text</a>, and then later in the same article, <a href=“[URL-B]”>Duplicate Link Text</a>. Conversely, when the same URL is used more than once on a page, the same link text should be repeated exactly.

Logical order and readable content

Simple, direct sentences (one fact per sentence) produce cleaner embeddings for LLM retrieval. Human accessibility best practices of plain language and clear structure are the same practices that improve chunking and indexing for LLMs

For Technical Teams — IT, Developers, Engineers

Poorly structured semantic HTML

Semantic elements (<article>, <nav>, <main>, <h1>, etc.) add context and suggest relative ranking weight. They make content boundaries explicit, which helps retrieval systems isolate your content from less important elements like ad slots or lists of related articles. 

Lack of schema

This is technical and under the hood of your human-readable content. Machines love additional context and structured schema data is how facts are declared in code — product names, prices, event dates, authors, etc. Search engines have used schema for rich results and LLMs are no different. Right now, server-rendered schema data will guarantee the widest visibility, as not all crawlers execute client-side Javascript completely. 

How to make accessibility even more actionable

The work of digital accessibility is often pushed to the bottom of the priority list. But once again, there are additional ways to frame this work as high value. While this work is beneficial for SEO, our recent research uncovers that it continues to be impactful in the new and evolving world of GEO.

If you need to frame an argument to those that control the investments of time and money, some talking points are: 

• Accurate brand representation — Poor accessibility hides facts from LLMs. When customers ask an AI  assistant for “best X for Y,” your content may not be shown — or worse, misrepresented. Fixing accessibility reduces brand risk and increases content authority.
• Engagement boost — Improvements that increase accurate citations and AI visibility can increase referral traffic, feature mentions, and lead quality. In a landscape where AI Answers are reducing click-through rates, keeping the traffic you have on your site for longer and building brand trust becomes vital.
• Increased exposure — Digital inclusion makes your content widely accessible to machines and the machines that assist humans. Think about a search engine as another human-assistive device, just like a keyboard or screen reader. 
• Multi-pronged benefits — Accessibility improvement improves traditional SEO, can benefit mobile performance, and reduces the risks associated with accessibility compliance policies. 

Staying steady in the storm

Let’s be clear — this summer was a “generative AI search freak out.” Content teams have scrambled to get smart about LLM-powered search quickly while search providers rolled out new tools and updates weekly. It’s been a tough ride in a rough sea of constant change.

If you continue to have questions within this rapidly evolving landscape, talk to us about your questions around SEO, GEO, content strategy, and accessibility conformance. Ask about our training and documentation available for content teams.

Additional Reading

• AHREFs.com: Is SEO Dead? Real Data vs. Internet Hysteria
• SearchEngineJournal.com: How LLMs Interpret Content: How To Structure Information For AI Search
• InclusionHub.com: SEO and Web Accessibility: What You Need to Know (from 2020, but still relevant)

As a digital services firm partnering with destination marketing organizations (DMOs) across the U.S., we’re helping teams navigate what’s already proving to be a volatile 2025—especially on the inbound side. Analysis from the World Travel & Tourism Council (WTTC) projects a stark reality: the U.S. economy will miss out on $12.5 billion in international visitor spending this year, with inbound spend expected to dip to just under $169B, down from $181B in 2024. Even more concerning, the U.S. is the only country among 184 economies in WTTC’s study forecast to see an inbound-spend decline this year.

While external market forces remain largely beyond control, we’ve identified three strategic areas where DMOs can focus their digital platforms to weather this storm and continue demonstrating measurable demand to their partners.

1. Transform Content Into Action-Driving Experiences

Why this strategic shift matters now

With inbound spend shrinking by $12.5B and key feeder markets weakening, undecided travelers need clarity and confidence to choose your destination. Content that reduces uncertainty and highlights immediate value converts better than generic inspiration.

Strategic implementation approach

Activate “Go Now” signals. Combine always-on inspiration with time-sensitive reasons to visit—shoulder-season value, midweek deals, cooling weather breaks—strategically mapped to the soft periods your analytics reveal. 

Elevate discovery through intelligent architecture. Curate SEO-optimized content hubs organized by Themes (outdoors, arts, culinary) and Moments (fall colors, winter lights). Implement structured data (FAQ, Event, Attraction) with strategic internal linking architecture so travelers find relevant options fast.

Deploy micro-itineraries for immediate conversion. Design 24–48-hour “micro-itins” featuring embedded maps, transit and parking guidance, and seamless handoffs to bookable partners. Partnering with platforms like MindTrip reduces content team effort while accelerating output—a strategy that’s proven particularly effective for our DMO clients facing resource constraints.

Authority-driven event content optimization. Event pages generate the highest intent traffic. Enhance them with rich media, last-minute planning resources, and strategic “if sold-out, try this” alternatives.

Transparent value communication. Feature free experiences prominently, implement intuitive budget filters, and deploy “Best Time to Visit” calendars comparing crowds and pricing by week and month. Transparency builds trust, and trust drives conversion.

2. Build Your Competitive Moat Through Data-Driven Audience Cultivation

Your first-party data represents your most defensible competitive advantage. As platform targeting becomes increasingly constrained and inbound spending softens, DMOs that build and activate their own audience will capture attention far more efficiently than those relying solely on paid channels.

Strategic audience development

Implement high-intent capture everywhere. Deploy contextual email and SMS prompts across high-intent templates—events, itineraries, trip planners, partner directories. Offer valuable micro-perks like exclusive maps and early event alerts. 

Master progressive profiling. Collect visitor preferences—season, interests, party type, origin market—over multiple touchpoints rather than overwhelming users with lengthy initial forms. 

Create actionable audience segments. Develop cohorts around 2025’s market realities: last-minute planners, shoulder-season seekers, road-trippers, value hunters, family weekenders, and meetings planners. 

Future-proof attribution systems. Combine GA4 with server-side tagging and standardized UTM schemas for every partner handoff. Track outbound clicks, partner session quality, itinerary saves and usage, offer redemptions, and newsletter-driven sessions. This comprehensive approach ensures you maintain visibility into conversion paths as third-party cookies disappear.

Deploy trend-driven editorial strategy. Develop weekly dashboards blending organic query trends, on-site search terms, partner click-through rates, and feeder-market signals. When interest dips in one market, pivot homepage modules and paid social toward value and itinerary content targeting more resilient markets.

3. Transform Partner Relationships Through Measurable Value Delivery

In a softening inbound environment where domestic spending carries approximately 90% of the economic load, your partners need two critical elements: qualified attention and proof of conversion. Your website should function as the region’s premier meta-directory and conversion engine.

Experience optimization strategies

Enable one-click handoffs with context preservation. Pass user filters—dates, neighborhoods, price ranges—directly into partner sites and booking engines while preserving state if travelers return. 

Deploy persistent trip planning tools. Allow users to save places and generate shareable itineraries with intelligent handoffs: “Book these two hotels,” “Reserve rentals,” “Get festival passes.” 

Create compelling partner storefronts. Develop rich partner profiles featuring availability widgets, authentic reviews, social proof, and clear calls-to-action. 

Implement strategic co-op modules. Design paid placements that provide value rather than feeling like advertisements: “Local Favorites” carousels, sponsor highlights, seasonal deal tiles—rotated by audience cohort and season. This generates additional revenue while maintaining user experience quality.

Establish closed-loop reporting systems. Standardize UTM tracking, monitor outbound events, and where permitted, implement partner pixels and offer codes to report assisted conversions by category and campaign. Partners need proof of ROI, and data-driven reporting builds stronger, more profitable relationships.

How Oomph Can Accelerate Your Success

If you’re experiencing softer international interest, shorter booking windows, or declining partner satisfaction, you’re facing the same challenges as DMOs nationwide. The organizations pulling ahead aren’t waiting for market recovery—they’re strengthening their digital platforms through strategic content optimization, systematic audience cultivation, and demonstrable partner value creation.

Our proven methodology transforms these challenges into competitive advantages.

We’ll conduct a comprehensive audit of your digital platform against these three strategic pillars, quantify immediate optimization opportunities, and provide your partners with what they need most: qualified, measurable demand. The market headwinds are real, but the right strategic approach can help you maintain resilience and emerge stronger when conditions improve. Let’s navigate these challenges together.

In 2026, the way people discover and engage with digital content has shifted. Traditional Search Engine Optimization (SEO) is no longer the only strategy that brings people to your website. Meet Generative Engine Optimization (GEO), the emerging frontier for organizations looking to earn visibility through AI-driven platforms like ChatGPT, Google’s Gemini, and Perplexity.

If your organization hasn’t begun adapting its content strategy for GEO, now is the time. Here’s what GEO is, why it matters, and how to start optimizing for it.

What is GEO and How Is It Different From SEO?

While SEO focuses on improving your visibility on traditional search engine results pages (SERPs) through keywords, backlinks, and technical performance, GEO is about making your content the answer in AI-generated responses.

Rather than presenting users with a list of links, GEO centers on AI tools that synthesize information. These platforms use large language models (LLMs) to provide direct answers to questions. Instead of competing for a top 10 ranking on Google, you’re aiming to be cited, summarized, or linked to by tools like Gemini or ChatGPT.

In short: SEO gets you found, GEO gets you featured.

Why GEO Matters in 2026

AI tools are no longer sidekicks to Google—they’re central to how people research, compare options, and make decisions. As of late 2025, ChatGPT receives over 4.5 billion monthly visits, while Perplexity processes over 500 million searches per month. Google remains the dominant force in online search with billions of daily visits, but with the direct integration of Gemini into search results, the way people find information is changing. Users can now get answers without ever clicking through to your website—a “zero-click search result.”

If your content isn’t showing up in AI answers, you’re missing visibility with a massive and growing segment of your audience. Depending on what your digital experience delivers, this affects brand recognition, traffic and lead potential, and your credibility as an authority in your space.

In 2026, AI summaries are the new front page of search.

How GEO Works: What AI Tools Are Looking For

Each generative engine has its quirks, but several patterns are emerging across platforms:

1. Structure Matters More Than Ever

AI tools rely on clear, structured content. Use schema markup generously—particularly FAQPage, Organization, Article, and Product types. Structured data helps AI understand your content contextually, making it easier to reference in generated answers.

Tip: Google’s Structured Data Markup Helper is a great place to start reviewing your schema.

2. E-E-A-T Principles Still Rule

Google’s Expertise, Experience, Authoritativeness, and Trustworthiness (E-E-A-T) framework, a core concept for SEO, now extends to AI tools like Gemini. Show credentials, cite data, link to reputable sources, and provide content authored by credible experts.

If you have certifications, awards, partnerships, or original research, feature them clearly.

3. Conversation > Keywords

GEO is less about keywords and more about natural language. Write in a conversational tone and frame your content in terms of questions and answers. Think: “What are the best family vacation spots in California?” instead of “California vacation destinations.”

4. Content Freshness is Key

AI platforms—especially Perplexity, which indexes content daily—prioritize content that’s up to date. Refresh evergreen posts annually and use a content calendar to track when to review content. Prioritize articles with titles like “Top” or “Best,” as these perform well in answer generation, particularly on ChatGPT.

5. Visuals Are Increasingly Important

Gemini and Perplexity are both investing in multimodal search. Media assets like charts, videos, and well-optimized images can increase the chance of being featured. Also make sure your image alt text, captions, and surrounding content are descriptive.

6. Prioritize Performance & Mobile-Responsiveness 

A site that performs well on mobile loads quickly, displays clearly on small screens, and avoids frustrating interactions like unclickable buttons or pop-ups. Poor mobile performance—including slow Core Web Vitals—can hurt your rankings, which in turn reduces your visibility to LLMs that rely on search results as input sources.

Tool-Specific GEO Tips

Gemini (Google)

• Optimize for the Search Generative Experience (SGE) with crawlable content and Core Web Vitals in check.
• Use a hub and spoke content model to build topical authority. This model organizes content around a central “hub” topic page that then links to related and more detailed “spoke” pages. 
• Regularly monitor impressions and click-through rates in Google Search Console. A dip in clicks with high impressions could signal that your content is being used in AI answers.
  

Perplexity

• With an emphasis on factual accuracy, source transparency, and user control over search scope, sources are essential. Focus on citations and factual, digestible content.
• Use Question & Answer formatting to align with Perplexity’s research focus.
• Include multimedia assets and data points that back up your authority—charts, diagrams, and maps in addition to video and images.
  

ChatGPT

• Embrace personalization. ChatGPT seeks out phrases like “top” or “best” that give users the feeling of receiving personalized insights.
• Optimize your About Us page to clearly articulate your mission and values. ChatGPT often uses this to evaluate trustworthiness and authority.
• Strengthen your backlink profile to compete with high-authority sources like Wikipedia, Reddit, and news outlets frequently cited by the model.
  

Tracking GEO Performance

A consequence of AI summaries is that websites may see a drop in clicks and visits within their analytics, particularly a decrease in organic traffic month over month. With users getting answers from AI-generated search responses, they may no longer need to visit your website for information. However, those users who do click through often stay longer and discover more pages than they did previously.

Websites may also see an increase in impressions or referrals from AI assistants. This data is increasingly important to track.

Even if AI tools don’t always send traffic directly, you can still measure their impact:

• Google Analytics 4 (GA4) Segmentation: Create segments by referral source (e.g., chat.openai.com, perplexity.ai, gemini.google.com) to track AI-specific sessions.
• Landing Page Analysis: AI tools often link deep into your site. Use GA4 to monitor which long-tail pages are receiving AI-generated traffic.
• Google Search Console: Identify FAQ-style queries with high impressions but low CTR. These may indicate your content is being summarized in AI answers.

What This Makes Possible

For organizations investing in GEO, the shift isn’t just about traffic—it’s about creating the foundation for how your brand shows up when decisions are made. When your content is structured, current, and authoritative, you’re positioned to be the answer AI platforms cite. That visibility translates into trust, consideration, and the ability to shape how your expertise is perceived across the platforms your audiences use most.

Organizations that optimize for GEO now are building systems that can adapt as AI search continues to evolve, ensuring their digital presence performs across both traditional and emerging channels.

Action Items for Digital Teams

1. Audit your existing content with these optimization strategies in mind. You can use AI tools like Gemini to identify optimization opportunities for particular pages.
2. Update schema across all major content types, especially Q&A and organizational pages.
3. Refresh your high-performing or evergreen content regularly, especially pieces tied to seasons, events, or top lists.
4. Revise your content strategy to include multimedia assets, structured data, and topic clustering.
5. Optimize your About page and author bios to strengthen trust signals for LLMs.
   

Final Thoughts

Optimizing for GEO is a fundamental shift in how people find and interact with content. As AI-generated answers become a dominant part of the discovery experience, your organization’s ability to show up in these spaces affects whether you gain trust or go unnoticed.

By embracing schema, writing conversationally, and refreshing content with purpose, your digital presence can evolve to meet the moment—one where the best answer often wins over the best ranking.

Ready to optimize your content for AI-powered search? Let’s talk about what that looks like for your organization.

Search and SEO are evolving rapidly in the wake of new AI options. Many of our clients are concerned about continuing to receive a return on their SEO investment. They worry about putting effort into the right places. And they worry about how to prepare for a drastic shift in the landscape, should it come. 

The speed of evolution has made these questions difficult to answer with authority. But we conducted research, asked some experts, and have some theories that put these fears into context. Hopefully, they can help your organization navigate these uncharted waters.

Do AI Overviews reduce click-through rates?

In 2024, Google introduced AI-generated answers to queries in its search results. These “AI Overviews” are more likely to appear when a visitor phrases their search query like a question, using “what,” “how,” or “why” language. These overviews provide citations to their sources and a right sidebar (on laptops) with other references. Some are calling the traffic these overviews generate “zero-click” searches.

While the answer is yes, click-through rates have reduced by as much as 10%, others argue that most websites will be unaffected. For one, Google has scaled back their AI Overviews to only 1.28% of its billions of daily searches. This will likely increase now that AI has become less likely to provide incorrect answers, but the misconception that AI Overviews are everywhere is overblown. 

Further, the same article goes on to assert that 96.5% of all AI Overviews appear for informational keywords — meaning very few overviews are created for transactional, navigational, and local searches. Informational questions are much easier and safer for AI to answer and will likely remain the dominant use case.

Others argue that AI Overviews keep low-performing traffic away from your site. For many years, Google has already been answering queries with information cards. When you Google a business, you are likely to get a card with the business name, phone number, web address, and even a map with their location. Popular businesses might include reviews and specific details like daily open hours. These information cards have already been taking traffic away from your site. But was that the traffic that you wanted? 

These folks argue, if the searcher just wanted to know an answer to a question they had while having a conversation with a friend, they would have come to your site for that information and then left. Their visit would have counted as a bounce and negatively affected your monthly traffic data. Same with the ones that just needed a phone number or wanted to know what time you close. They would have come to your website for that one thing and then left.

Google’s own research says that when people use AI Overviews to start understanding a topic, they end up searching more frequently and express higher satisfaction with the results. Their position is that these overviews scratch the surface and help visitors ask more in-depth follow-up questions. Other recent studies have found that click-through increased for companies featured in AI Overviews, while those without an AI Overview lost traffic.

One thing is for sure: AI Overviews’ prominent position at the top of the results have pushed down organic results and made it harder for high-ranking organic websites to get noticed.

Takeaway:

Mixed. Yes, it is possible that AI Overviews are preventing click-through. It is also possible this traffic was not going to convert. And depending on your product and position in the market, AI Overviews might drive slightly more traffic than organic search. Either way, the result is an even more competitive search landscape than before.

Should I optimize my content for AI Overviews? 

The most obvious next question is “How can my brand rank for AI Overviews?” While this is an important question, remember that AI Overviews often include citations from multiple sources. So while your business may rank for an overview, it is likely not going to be alone. 

The answer to this question is more of the same things you should already know. In order to rank highly, you should: 

• Follow SEO best practices
• Be authentic (leverage first-hand experiences like anecdotes, reference data sources, and be as unique as possible with your perspective)
• Anticipate next steps (what does someone need to learn and in which order)
• Use structured data (schema, JSON, etc.)
• Include multimedia (images, video, gifs) 

Lots of SEO companies want to help your business rank, and AI Overviews is the next frontier. But from all the articles we have reviewed (and there were many), the same best practices apply — there are no shortcuts to great content. 

Takeaway

Yes, optimize your content for AI Overviews, but this does not mean you need to do more than what you are already doing. To be a highly quoted source within your industry has benefits for brand recognition and trust, but just like long-tail keywords, these searches may have low volume. In the end, it is an investment vs. return question. There is a significant overlap between the sources cited in AI Overviews and the top organic search results, therefore, if your site already ranks well, you can’t do much more to get into an AI Overview.

Should I continue investing in SEO for Google?

Some clients worry that Google will be unseated as the dominant search engine now that tools like OpenAI’s ChatGPT have seen an explosion of millions of users. While these tools are indeed experiencing hockey-stick growth, Google completely dominates search volume.

SparkToro charted a 20% growth in search queries for Google in 2024, and crunched the numbers to conclude Google receives 373 times more searches than ChatGPT. 

To put that into context, Google handles 14 billion searches per day. The next closest competitor is Bing search with 613.5 million per day, followed by Yahoo, DuckDuckGo, and then Chat GPT. In other words, your investment would see a larger return if your team optimized content for Bing.com than for ChatGPT. 

These numbers are fresh from March 2025. Things can change, of course, but AI tools are not used only for search, have a relatively small market share, and do not get used daily. They suffer from not being the default tool at hand, which for most people, is a web browser. Google remains synonymous with search for a large percent of the population.

Takeaway

Yes, continue to invest in SEO for Google specifically. Google is still the biggest player in the search market, and their share is gaining, not decreasing (yet).

If we don’t implement structured data, are we losing out on AI crawler traffic?

Structured data is great for all SEO, so actually, you should implement structured data like Schema.org for across-the-board SEO value. 

For those of you using Google Tag Manager (GTM), you might know that you get some structured data for free. When a Googlebot crawls your site, it includes structured JSON data that it creates client-side, which means that Google gets the structured data but it is inaccessible to any other crawler. If the data existed server-side, other bots could access it. 

Most non-Google robot crawlers do not execute Javascript, therefore, they miss out on anything rendered in the browser. These crawlers include Bing, Yahoo, ChatGPT, Claude, and Perplexity. So again, server-side structured data would benefit all the search engine crawlers that are not Google.

But do LLMs really need structured data?

Large Language Models (LLMs) use statistical analysis to predict what word will follow the previous set of words. They do not understand language as much as they can mathematically reproduce its patterns. Therefore, they create structure from unstructured data all the time. 

But while LLMs process and understand unstructured text, providing structured data would significantly help interpret and categorize your content effectively and accurately.

Takeaway

The short answer is no, LLMs do not require structured data to create meaningful connections between content and search intent. But structured data would help them and any other search service to correctly label, tag, and organize your data. The longer answer is an investment in structured metadata would pay off in dividends for all search engines and crawlers.

How can we prepare for SEO’s evolving future?

In mid-2024, when Google first introduced AI Overviews, some in the SEO/SERP industry claimed sites could lose up to 25% of their traffic. That has not come to pass, with some sites reporting as high as 12% and others lows of 8.9% and 2.6% — not insignificant, but lower than expected. And the data is still coming in, with others reporting increases in traffic with specific kinds of intent.

While AI increasingly shapes search results, content strategy will need to shift for sites to remain visible and relevant. High-quality, authoritative, and authentic content that offers depth, accuracy, and unique insights is still valuable currency. AI algorithms are designed to identify and prioritize quality, trustworthy, and well-researched content for inclusion in their summaries. 

Sites should continue to target long-tail and question-based keywords to align content with visitor’s increase in natural language queries. This type of content is often more challenging for AI to fully synthesize and may still necessitate user click-through for a comprehensive understanding. Going deeper to investigate specific intents behind longer conversational queries could also be crucial for attracting relevant traffic. 

Finally, diversifying content formats by incorporating video, infographics, and interactive elements will continue to enhance engagement and provide unique value that text-based AI summaries don’t fully replicate. And optimizing content for featured snippets remains important, as appearing in these snippets increases the likelihood of a website’s content being cited within AI Overviews. 

Takeaway

The fundamentals of great content and best-practice SEO has not changed as dramatically as the tools that crawl your site and serve your content have.

Final Thoughts

Anything in the tech space evolves rapidly, and SEO is no exception. While the methods and the tools we leverage might change, the fundamentals remain strong. Keep doing what you have been doing, keep being curious, and keep asking these important questions of those in your circle whom you trust. We’re all figuring these things out in real time and can benefit from each other’s expertise.

If you have in-depth questions about SEO, content management, and the evolving AI-powered landscape, reach out to our team and we’ll always do our best to answer them thoughtfully and from multiple angles.

AI disclaimer: Google’s Deep Research was used for initial exploration and source gathering. All sources cited in this article were reviewed by the author. ChatGPT was used for follow up questions, as well as AI Overviews for examples of common questions. This article synthesizes these sources and was written by a human.