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.

Structured content distribution is the decoupling of content from presentation through a headless CMS and Content as a Service (CaaS) architecture. It is a sound strategy for organizations managing complex content distribution networks across multiple channels. 

To be the most successful, this digital transformation requires organizations to change both their publishing workflows and their content ownership structures. Governance complexity affects 41% of CaaS adopters (PDF), workflow mismatches impact a third, and training requirements average 14 to 18 weeks. 

We have implemented these systems for clients in healthcare, financial services, and higher education, and the pattern is consistent: the three failures that kill structured content initiatives are the preview gap, the ownership vacuum, and the training deficit. Here is what we have learned about each one — and what actually works.

The Promise 

The pitch for structured content distribution is compelling: create content once, store it as modular data in a headless CMS, deliver it via API to any channel (web, mobile, kiosks, AI agents) without reformatting. The CaaS market is projected to reach $2.8 billion by 2035, and over 65% of enterprises have adopted headless CMS architectures.

What they do not tell you is that integration challenges affect 46% of adopters using legacy CMS platforms, and that 31% of enterprises encounter deployment delays exceeding six months. The technology works, but the governance requires just as much attention and is often overlooked. We have seen this avoidable pattern repeat across many structured content implementations.

Why Do Structured Content Migrations Stall?

In short, because organizations implement the technology without redesigning how their teams create, review, approve, and own content. That’s the governance problem.

A headless CMS decouples content from presentation. But most editorial teams have spent years, sometimes decades, working in systems where creating content and seeing how it looks are the same activity. WordPress, Drupal, and even SharePoint have a visual editing experience: build a page, see the page, publish the page.

Structured content does not work this way. Authors fill in fields like title, body, metadata, and related entries to publish content objects, not pages. As one analysis of Contentful’s editorial interface notes, “content editors work in structured content entry forms without seeing how content will render in production.” The front-end determines how those objects appear to users.

That architectural distinction is the correct one for consistent omnichannel delivery. It is also the one most likely to break editorial workflow expectations when teams do not deliberately plan for this big shift. In our experience, three governance failures account for the vast majority of structured content stalls.

What Is the Preview Gap, and Why Does It Derail Teams?

The preview gap is the loss of visual context that editorial teams experience when moving from a WYSIWYG (what you see is what you get) environment to a structured content interface, and it is the most immediate friction point in any headless CMS migration.

Authors who previously built pages visually are now filling in form fields and trusting that a front-end will render them correctly. The shift from “building a page” to “managing a content object” takes adjustment, and “once teams adapt, the structured approach tends to produce more consistent, reusable content.” The problem is what happens before they adapt.

What happens is that authors create workarounds. They paste formatted content into rich text fields, breaking the structured model. They submit tickets to developers asking “what will this look like?” multiple times per week. They maintain shadow documents in Google Docs so they can see their work in context. Every workaround is a governance failure — content that exists outside the system, formatting that undermines the content model, and developer time consumed by preview requests instead of feature development.

The planning that pays off includes building live preview environments for as many content sources as possible. This development work typically gets deprioritized because it is not user-facing, but it determines the success of the new system. As one migration guide puts it, headless platforms deliver excellent editorial experiences “when configured correctly — visual editing, live preview, flexible page-building, role-based permissions. But that configuration is work, it doesn’t happen by default.” Budget for it, build it first, and do not launch editorial access without it.

What Is the Ownership Vacuum?

The ownership vacuum is what happens when structured content crosses departmental boundaries without clear governance over who maintains the content model, who approves changes to shared components, and who is accountable when content is reused in a context the original author never intended.

In a traditional CMS, the marketing team owns the marketing pages, the product team owns product pages, etc. Structured content breaks this model deliberately — a product description created once might appear on the website, in a mobile app, in an email campaign, and through a chatbot simultaneously. But governance complexity affects 41% of CaaS adopters, and multi-team collaboration across 6 to 10 departments increases governance overhead by 27%.

Questions seldom asked include: 

• When the compliance team changes a regulatory disclaimer, who is responsible for verifying that the change renders correctly across every channel consuming that content object?
• When marketing adds a field to the product content type, who assesses the downstream impact on the mobile app and the support knowledge base?

We have seen organizations discover these questions six months post-launch, usually during a content audit that reveals inconsistencies no one can trace. In regulated industries — healthcare, financial services, higher education — those inconsistencies are compliance risks.

Knowing these pitfalls ahead of time can lead to the establishment of a content model governance board before migration begins. A small, cross-functional group (typically 3 to 5 people spanning content strategy, development, and compliance) owns the content model as a shared organizational asset. They approve changes to content types, evaluate reuse implications, and maintain a living inventory of where shared content objects appear. This role does not exist in traditional CMS organizations because it’s not needed. But in structured content environments, it is absolutely necessary.

Why Does the Training Deficit Compound Everything?

Because organizations allocate 90% of their transformation budgets to technology and implementation, and only 10% to change management — the part that determines whether anyone actually uses the system they built.

Training requirements for CaaS implementations average 14 to 18 weeks, the elapsed time from initial exposure to genuine editorial fluency. This training creates the confidence for authors to create, structure, and publish content without reverting to old habits or filing developer tickets. Most implementation budgets account for a one-day training session and a knowledge base article. The gap between that and actual fluency is where adoption dies.

The compounding effect of the training deficit makes this particularly damaging. Undertrained authors hit the preview gap and panic. Without clear governance ownership, there is no one to answer their questions authoritatively. They build workarounds. Those workarounds corrupt the content model. The corrupted content model undermines the case for structured content. Stakeholders lose confidence. The transformation stalls.

BCG’s study of 850+ companies found that only 35% of digital transformations meet their value targets globally. The failure rate is a change management problem that looks like a core problem with the technology itself.

To avoid this failure spiral, structure editorial onboarding as a phased engagement, not a one-and-done event. In our implementations, we start with a pilot group of 3 to 5 authors working with the system while the front-end is still being built. They surface friction points the development addresses in real-time. When the broader editorial team is onboarded, the common pain points have been resolved, and the pilot group serves as advocates who can answer questions and support their peers. This approach adds little cost and dramatically improves adoption velocity.

What Should Organizations Do Before Starting a Structured Content Migration?

Treat governance design as a foundation to build a successful digital transformation:

1. Audit your editorial workflows as they actually operate. Map who creates content, who reviews it, who approves it, and where informal workarounds exist. As one migration planning guide advises, most publishing workflows “are often based on legacy systems, informal approvals, or staff availability. The result? Delays, missed steps, and content that never quite gets finished.” Your structured content governance must account for the real workflow, not the theoretical one.
2. Define content model ownership before selecting a platform. Determine who will own the content model as an organizational asset, who can request changes, and what the approval process looks like. This governance structure should be platform-agnostic — it is an organizational decision, not a technical one. We have helped clients build this through our roadmapping and strategy engagements, and it consistently reduces mid-project governance confusion.
3. Budget for editorial experience parity. If your authors currently have WYSIWYG editing, live preview, and visual page building, do not assume they will accept a simpler and more limiting form-based interface. Calculate the development effort required to provide contextual preview in your new architecture and include it in the implementation scope, not as a phase-two enhancement. Phase two rarely arrives before editorial frustration does.

Wrap Up

The CaaS pitch is not wrong. Structured content distribution is the right architecture for organizations publishing across multiple channels, and it is increasingly the right architecture for AI readiness — structured data is what AI systems consume most effectively. But the promise underestimates the organizational effort to make it successful.

Technology is the easy part. Governance, training, and editorial adoption are harder, and that is where implementations succeed or fail.

We have built these systems on Contentful, Drupal, and composable architectures for organizations in regulated industries where getting content wrong has real consequences. The lesson we keep relearning is the same one: start with the team, not the platform.

Bill Gates wrote “Content is King” back in 1996. He was right for about thirty years. On the open web, the winners were the ones who could produce, distribute, and monetize content at scale. That era shaped how we built digital products, how we organized marketing teams, and how we thought about content platforms.

That era is getting a new chapter.

When content becomes context

In the age of agents, content is context. It’s the raw material an AI uses to answer a customer’s question, draft a proposal, summarize a policy, or make a decision on behalf of your business.

If your context is a mess, your agent is a mess. Garbage in, confident-sounding garbage out.

For organizations in healthcare, higher education, and associations (industries where we work every day) that governance layer isn’t a nice-to-have. A health system deploying an agent to answer patient questions needs to know which clinical protocol is current, who approved it, and what the agent is and isn’t allowed to cite. An association managing member benefits can’t afford an agent that surfaces a two-year-old policy document as current guidance. And it’s not just the regulated organizations themselves. The enterprise technology companies that serve these industries, the SaaS platforms, the data providers, the system integrators, face the same challenge: if the content powering their products isn’t structured and governed, the agents built on top of it will inherit every gap. The stakes in regulated industries make the content-as-context problem concrete and urgent, but the same dynamics show up everywhere brand, voice, and accuracy matter: retail pricing, financial disclosures, B2B product specifications, public sector policy. Different risk profiles, same fundamental problem.

This isn’t theoretical. Gartner predicts that 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025. The shift is already moving from prediction to product.

The platforms we work with every day show the movement clearly. The Drupal AI Initiative launched last June and hit $1 million in funding within five months, with the Drupal AI and AI Agents modules reaching production-ready status in October 2025. Acquia built on that foundation with Acquia Source, shipping three AI agents for its Drupal-powered SaaS CMS in December. Contentful open-sourced its MCP server and has been publishing active guidance on agentic content operations. These aren’t experiments. They’re shipping.

Across the category, the pattern is broad. Contentstack launched Agent OS in September 2025 and introduced what it calls the “Context Economy” as its positioning. Kontent.ai shipped what it calls an Agentic CMS the following month. The Model Context Protocol that Anthropic introduced in late 2024 has become the connective tissue, adopted by OpenAI, Google DeepMind, and most of the CMS world.

The platforms are ready. The question is whether your content is.

What agents actually need

An agent doesn’t want a rendered web page. It wants structured, canonical, permissioned, versioned truth. That means:

• Structure so the agent can reason over content rather than scrape through marketing copy
• Versioning so it knows which policy, price, or product spec is current
• Permissions so the agent answering a customer question can’t pull from an internal-only HR doc
• Freshness signals so stale content doesn’t get treated as authoritative
• Governance so legal, brand, and compliance can trust what the agent says on their behalf

That’s the same job a mature content platform has been doing for years, just pointed at a new kind of consumer.

We’ve seen this movie before

Every channel shift exposes whether your content was ever really structured to begin with. CD-ROM, then the web, then mobile, now agents. Each one forces organizations to untangle content from presentation. Headless CMS platforms like Drupal, Contentful, Sanity, and Strapi won that argument. Content as structured data, delivered via API, rendered wherever you need it.

Agents are the most demanding channel yet. They don’t just display your content. They consume it, reason over it, and then take action. If your content is trapped inside HTML blobs or buried in PDFs that no one’s touched since 2021, it’s not ready to be context. Structure is the whole game now.

Where context lives today

Right now, company context is scattered across:

• Websites and headless CMS platforms
• GitHub repos full of markdown
• Confluence, Notion, SharePoint, Google Drive
• Salesforce, HubSpot, and a dozen other systems of record
• PDFs, Slack threads, and somebody’s laptop

Some of these are built for governance. Most aren’t. GitHub is hands-down great for technical content and version control, but marketing and legal teams aren’t opening pull requests to update a pricing page. Notion is excellent for collaboration, weak on structured content models and role-based delivery. Every organization I talk to has some version of this scatter, and it’s about to become a much bigger problem.

The rise of the Context Management System

The old acronym still works. CMS. New job.

Headless CMS platforms have quietly solved about 70% of what agents need. Structured content models. API-first delivery. Editorial workflows. Roles and permissions. Versioning. Audit trails. What they’re adding now is the connective tissue. Acquia is embedding AI agents directly into Drupal-powered workflows through Acquia Source, and Contentful has open-sourced its MCP server to let agents take action on content operations. Across the rest of the category, Sanity launched its Content Agent in January 2026, and Storyblok, Brightspot, and dotCMS have released MCP servers of their own. MCP servers, vector indexing, semantic metadata, agent-optimized delivery endpoints. That’s a much smaller leap than building the whole governance layer from scratch.

The “just throw it all in a vector database” approach has real merit as a retrieval layer. Retrieval is one job. Governance is a different one: who owns canonical truth, who approved the content, when it expires, and who’s allowed to see it. That’s always been the CMS job. It matters more now, not less.

For teams working on Drupal, Contentful, or Acquia Source, this is encouraging. The architectural decisions those platforms made years ago (structured data, granular revisioning, API-first design) turn out to be exactly what AI agents need. Your investment in content architecture is paying off in ways you didn’t plan for. Call it a head start.

What to do about it

If you’re building agentic products, or planning to, the content question is the quiet one that will bite you later. This is the work we’re spending most of our time on with clients right now. A few forward moves:

1. Audit where your content actually lives and who owns it. You will be surprised.
2. Pick a source of truth for each category of content. Don’t let five systems claim the same ground.
3. Get your structured content models right. If your content is trapped inside HTML, it isn’t ready to be context.
4. Build the governance layer before you need it. Versioning, permissions, approval workflows. Your legal team will thank you. So will your agent.
5. Connect your CMS to your agents via MCP or equivalent. This is how context flows. Do it early.

Content was king when the battle was for attention. Context is king now that the battle is for correctness. Agents are only as good as the material you feed them, and that material has to be managed with the same rigor we’ve applied to code, to data, and yes, to content itself.

The organizations that treat content governance as infrastructure, not a cleanup project, will be the ones whose agents are trustworthy from day one. That window is shorter than it looks.

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!

Selecting a content management system in healthcare is no longer a purely technical decision. In today’s environment, a CMS directly impacts compliance, accessibility, speed to publish, and ultimately, trust. Healthcare organizations are under growing pressure to deliver accurate, timely information across multiple digital channels, while meeting strict regulatory and accessibility requirements. The CMS at the center of that effort needs to support far more than page updates.

Why Healthcare CMS Decisions Are Uniquely Complex

Healthcare websites serve a wide range of audiences, from patients and caregivers to providers, partners, and regulators. Content must be clear, accurate, and easy to update—often by multiple teams—without introducing risk.

At the same time, healthcare organizations face constraints that many other industries don’t. Accessibility standards, privacy expectations, and governance requirements are non-negotiable.

A CMS that lacks flexibility or control quickly becomes a bottleneck.

“The healthcare content management system market is projected to grow to over $61 billion by 2031, underscoring how healthcare organizations are prioritizing modern, scalable digital platforms to support compliance, multi-channel delivery, and governance.”

According to Mordor Intelligence

What Healthcare Teams Should Prioritize

• A healthcare CMS must support strong governance without slowing teams down. Role-based permissions, approval workflows, and auditability are essential to ensure content accuracy and accountability.
• Accessibility also needs to be built into everyday publishing, not treated as an afterthought. The CMS should make it easy for teams to maintain WCAG-compliant content as sites evolve.
• Equally important is the ability to scale across channels. Healthcare content increasingly lives beyond the website—patient portals, mobile apps, email, and emerging digital touchpoints all require consistency. Managing this content from a single system reduces duplication and risk.

Flexibility Without Compromising Security

Healthcare organizations often rely on complex digital ecosystems, including EHRs, portals, analytics tools, and consent platforms. A modern CMS should integrate cleanly with these systems rather than trying to replace them.

Flexibility matters, but not at the expense of security. The right CMS supports modular integration while keeping sensitive data protected and clearly separated from content operations.

Planning For Change, Not Just Launch

CMS selection shouldn’t be based solely on current needs. Healthcare regulations, digital expectations, and technologies continue to evolve. The most effective platforms are designed to adapt without requiring frequent replatforming.

This means supporting incremental improvements, phased rollouts, and long-term scalability—so teams can modernize at a pace that aligns with organizational priorities.

The Role Of Modern, Composable CMS Platforms

Composable CMS platforms are gaining traction in healthcare because they treat content as structured data rather than static pages. This approach supports reuse, consistency, and omnichannel delivery while maintaining governance.

For healthcare teams, this translates into faster publishing, fewer bottlenecks, and greater confidence in content accuracy without sacrificing compliance.

What This Means For Healthcare Teams

Healthcare CMS selection is about more than choosing a tool. It’s about enabling teams to communicate clearly, operate efficiently, and adapt responsibly in a complex digital landscape.

Organizations that prioritize governance, accessibility, and flexibility position themselves to deliver trusted digital experiences today and in the years ahead.

Ready to Evaluate Your Healthcare CMS? Our team helps healthcare organizations navigate complex CMS decisions with a focus on governance, accessibility, and long-term scalability. Let’s talk about what the right platform looks like for your organization.

Contentful is no longer just an alternative CMS—it’s become a foundational platform for organizations navigating complexity, regulation, and rapid digital change. In 2026, the question isn’t what is Contentful? It’s why are so many organizations rebuilding their digital ecosystems around it? The answer lies in how digital experiences are built, managed, and scaled today.

Contentful Is Built for Systems, Not Pages

Traditional CMS platforms were designed around pages and templates. That model breaks down when content needs to move faster, live in more places, and remain consistent across teams and channels.

Contentful takes a different approach. It treats content as structured data, not static pages. That means teams create content once and deliver it anywhere—websites, apps, portals, email, or future channels that don’t yet exist.

In 2026, this isn’t a “nice to have.” It’s how modern digital platforms operate.

Composable Architecture Is Now the Default

Composable architecture has moved from trend to standard. Organizations want the freedom to choose best-in-class tools without being locked into monolithic platforms.

Contentful fits cleanly into this model. It integrates with design systems, analytics platforms, personalization tools, consent managers, and AI services through APIs—without forcing teams into rigid workflows.

This flexibility allows organizations to evolve their stack over time instead of rebuilding every few years.

AI Depends on Structured Content

AI-driven experiences are only as good as the content behind them. In 2026, organizations are using AI to support personalization, search, localization, content optimization, and automation.

Contentful’s structured content model makes this possible. Clean, well-defined content enables AI tools to understand, reuse, and adapt content accurately—without introducing risk or inconsistency.

For teams exploring AI responsibly, Contentful provides the infrastructure needed to scale with confidence.

Governance and Compliance Are Built In, Not Bolted On

For regulated and mission-driven organizations, governance isn’t optional. Publishing controls, audit trails, permissions, and review workflows are essential.

Contentful supports these needs at scale. Teams can define roles, control who edits or publishes content, and maintain visibility into changes across environments. This level of governance is critical in industries like healthcare, legal, finance, and the public sector.

In 2026, compliance isn’t something teams add later—it’s designed into the platform from day one.

Marketing and Development Work Better Together

One of Contentful’s biggest advantages is how it aligns marketing and engineering teams. Developers maintain design systems and integrations. Content teams manage content without breaking layouts or workflows.

This separation of concerns reduces friction, speeds up delivery, and minimizes production errors—especially as digital ecosystems grow more complex.

Ready to explore what Contentful could do for your organization? Whether you’re evaluating platforms, planning a migration, or looking to optimize your current setup, Oomph can help you build a content infrastructure designed for the long term. Let’s talk about your next move.

Why Organizations Move to Contentful Now

Organizations typically migrate to Contentful when legacy systems start holding them back. Common triggers include:

• Slow publishing workflows
• Heavy developer dependency
• Difficulty scaling across channels
• Growing compliance requirements
• The need to support AI and personalization

In 2026, Contentful isn’t chosen because it’s new. It’s chosen because it’s resilient.

For organizations new to the platform, getting started doesn’t have to mean a complete rebuild. Oomph’s Contentful Kickstart Package helps teams move from decision to deployment with a structured, low-risk approach—giving you the foundation to scale as your needs evolve.

The Takeaway

Contentful has evolved alongside the modern digital landscape. It’s not just a CMS—it’s a content platform designed for scale, governance, and change.

For organizations planning beyond their next website launch and toward long-term digital maturity, Contentful provides the flexibility and confidence needed to move forward.

Ready to explore what Contentful could do for your organization? Whether you’re evaluating platforms, planning a migration, or looking to optimize your current setup, Oomph can help you build a content infrastructure designed for the long term. Let’s talk about your next move.

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

Museum websites have a unique duality. Unlike many other digital platforms, their primary goal is to encourage visitors to come in person. Their website may feature engaging articles or archives, cool virtual experiences, or highlight important research, but the physical space remains the heart of the museum, home to priceless collections and host to educational tours and programs. While the digital experience is still an essential one, the main objective of most museums is to welcome people through their doors. 

That is why the Visit section of a museum website is extra important. Visitors are looking for a single page that clearly outlines everything they need to know: admission prices and hours, what they can and can’t bring, accommodations for nursing mothers or individuals with disabilities, and so much more. Then again, different people need to know different information, so how do you keep everything together without it ballooning out of control? Despite its importance, many museum websites miss the opportunity to provide clear, concise, and accessible visit information in one central place.

A Survey of Website Visit Page Trends for Museums 

As part of a recent engagement with the Isabella Stewart Gardner Museum in Boston, we conducted a cohort analysis of other leading museum and cultural organization websites. The study focused on key elements of museum digital platforms including menu design, navigation, and the Prepare for your Visit page. We noticed a theme that several Visit pages on museum websites felt like long, endless scrolls. They’re often filled with lots of information, but a lack of structure or thoughtful design makes them difficult to quickly parse. Through this exercise of finding what is and isn’t working well and questioning why, we walked away with a strong sense of what makes a successful Visit page. 

Answer Visitors’ Top Questions

Who, what, where, when, how. When thinking about what information should be contained on the Visit page, these timeless questions are a strategic starting point. Though simple, they are the questions visitors will ask themselves before they arrive at the museum. These questions can take many forms, but for the Visit page, we’re prioritizing logistics:

• Where is the museum located? 
• What does it cost? 
• When is it open? 
• How do I get there? 
• Who can come along? 

If you are writing the content for this page, start by answering these key questions. 

You may have your content set, but you also need to think about how it is prioritized through strategic page design. You should make sure that the most important information (usually hours and admission prices) is at the top of the page and always visible. Don’t hide this information in accordions. And even if your admission is always free, point that out. Visitors want to have that information before they visit your museum, so make sure it is clearly stated. After all, free or reduced pricing is often an enticing reason for many to come!

Despite what you may think, duplicating some key content in different locations across your website can be helpful, as long as it doesn’t get confusing. Just because you have the hours on the homepage, doesn’t mean you should skip it on the Visit page. Presenting the information in different formats can also be helpful. For example, MoMA’s visitor guide provides a contained experience which includes a lot of content that can be found elsewhere on the site, but organized for a particular need (someone coming to the museum now).

Strike the Balance between Enough and Too Much with Accordions

Nearly every Visit page we studied used accordions. When you’re looking at a long list of content, the option of tucking away big chunks of it into a collapsible block sounds pretty appealing. That said, there are ways to do it well and plenty of ways it can go wrong.

Whenever you use an accordion, you’re asking users to click or tap to see more. While requiring an action like this can be a nice way to keep visitors engaged, whatever they see before interacting needs to accurately represent what’s inside. Let’s say a user wants to know whether they can carry a backpack around the museum. A generic heading — like “Guidelines” — doesn’t speak to its contents and the user could easily overlook it. Accordions that are organized well and labeled clearly — more like ”What You Can Bring in the Gallery” — can improve content organization and reduce cognitive load.

Also take care to make sure that the accordions are built in a way that everyone can use them. Test them with a screen reader and navigate through with only your keyboard to make sure they are meeting accessibility standards.

Our recommendation: use accordions, but strategically. Don’t have more than 7 or 8 and never add essential information there that visitors would be looking for at a quick glance. 

Guidelines & Policies

One large category that sometimes stumps museum stakeholders is where to put all the guidelines and policies that they often need to state, sometimes even for legal protections. Oftentimes these get lumped into a large accordion or series of accordions on the Visit page, without the key policies pulled out and clearly stated for visitors looking for quick guidance on whether strollers are allowed in the galleries or whether they can take photos with their new fancy camera. 

Particularly when you have an extensive list of guidelines, a successful approach can be linking to a larger guidelines and policies page with the information organized by clear headings and categories (which is also good for SEO/GEO), as seen on The Frick’s website. Just remember our earlier point about duplicate content: For essential guidelines, such as bags and security policies, consider also including this information on the main Visit page.

Help Visitors Plan Their Day

Planning your Visit is a big topic and depending on your museum’s particular offerings, might encompass a lot. Preparing ahead can include everything from directions and parking, what’s on view, amenities (dining, shopping), types of tours offered and at what times, etc. The goal for this content is to make it easy for visitors on the day of their visit, both logistically and emotionally. At the end of the day, you want visitors to get the most out of their time at the museum. Assess what is considered essential information that should be included on the main Visit page, but also what might warrant getting its own subpage. This is where in-page linking can be your best friend. 

• Setting Expectations — Setting the right expectations is especially important when a museum provides an experience that deviates from the norm. For the Isabella Stewart Gardner Museum, for instance, they do not have wall labels for every object on display and instead rely on audio and room guides accessible via QR codes throughout the Museum. In their use case, making sure visitors know to bring headphones and have a fully charged phone is key information that may not be applicable to other cultural organizations, nor assumed by visitors before arriving.
• Themed Itineraries — One trend we uncovered in our cohort analysis is the rise of themed itineraries, giving visitors different ways to experience a museum. When creating these, consider what makes your museum unique and the audience groups you want to serve. For example, if you have a garden, you might design a “Garden Lover” itinerary that highlights outdoor spaces alongside artworks featuring landscapes or floral still lifes. Or, if time is the constraint, you could offer a one-hour itinerary like MoMA’s thoughtfully titled “The Unmissables.”
• Keeping the Delightful — In our conversations with museum stakeholders and throughout our cohort analysis, we learned that it’s common for most visitors to arrive at a museum having done very little, if any, preparation about the type of experience they will receive. Though every museum operates a little differently and has its own quirks, people come thinking they know what to expect based on past experiences. The resulting surprise can be anywhere from delightful to disorienting. Balancing the element of surprise with the right amount of logistical information for expectation setting can be a challenge, but hopefully a fun one to think through.

Prioritize an Easy Mobile Experience 

Visitors often state that they want to “disconnect” while at a museum. They might be happy to pull out their phone for a photo, but otherwise want to spend their time and energy on the physical space around them. We truly love that for them, but also know that the website can, at times, meaningfully enhance the visitor experience. When thinking about what types of content should be considered from a mobile-first perspective, these come to mind:

• Maps — Include key features like restrooms and elevators. Enable common gestures like pinch-to-zoom and panning. Bonus points if the map is interactive, for example letting the user tap on a gallery to see what’s in it.
• Audio Guides — Provide basic controls, including play/pause, skip forward and backward (e.g. 15 seconds), and speed control. Let users access the transcript for greater accessibility.
• Artwork Information — This is especially important in the instance, like at the Gardner, where wall labels are not displayed in-situ and visitors are encouraged to access these via their phone in the galleries. They’ve addressed the need with digital Room Guides.

Ultimately, any content that is meant to be accessed while at the museum — member login and event schedule, for example — needs to be optimized for mobile. It’s especially important for this content to be easy to use and navigable on a small screen. We don’t want visitors to get lost in their phones or frustrated and ultimately give up. It needs to be intuitive to be a smooth piece of the whole experience.

Building a Successful Visit Page for Museums

Similar to building a successful navigation for a museum website, the first task of any organization looking to refresh their Visit page is to put yourself in the shoes of your visitors. Come up with a few key user journeys for various audiences. What would a family with small children need to know before coming to the museum? How about a person who requires a wheelchair or someone with low vision? What information would a student be searching the Visit page for? 

Beyond walking through the experience first-hand yourself, it helps to get an outside perspective. If you have the means to talk to visitors while they’re on-site, that can lead to some fascinating insights on their in-gallery experience. However, know that you’ll most likely encounter a positive bias in their responses. Not only are they enjoying a day at the museum, but it can be tough to give critical feedback to someone standing right in front of them. 

To counter that bias, gather feedback from additional sources: pop-up or email surveys, controlled usability testing, and website analytics. All of that data together can help give you the building blocks to ensure your visit page strikes the balance between being engaging and informative. By prioritizing clarity, accessibility, and thoughtful design, museums can ensure that visitors arrive knowledgeably at ease and excited to explore. 

A well-crafted Visit page is more than just a logistics hub, it’s the digital admissions desk of your museum.

When done right, it reduces friction, answers essential questions, and sets the stage for a memorable in-person experience. Ultimately, the Visit page isn’t just about driving attendance, it’s about shaping the visitor’s journey from the very first click to the moment they step through your doors.

Learn more about building a successful Visit page in a Case Study of our 2025 Re-Architecture project for the Isabella Stewart Gardner Museum.

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.