10 Key Technical AEO Challenges in 2025

Answer engine bots may struggle to process a web page due to their internal limitations or algorithmic architecture.

There are especially various parts of websites and types of code that bots focused on web crawling and content understanding may struggle to crawl or fail to understand.

This deficiency usually stems from difficulties in accessing, understanding, or making sense of content.

As a result, answer engine optimization professionals and developers dealing with technical AEO face significant challenges.

In this article, you’ll find 10 technical AEO challenges along with tips on how to solve them.

Why Do These Challenges Matter?

Is your brand showing up in answer engines the way you envision?

Or will it not appear at all, or show up in ways you didn’t intend?

That’s the real issue.

And in tackling it, there are some technical optimization processes that help your content be understood by answer engines.

If these are not in place, answer engines may never discover your content, or may understand it partially or incorrectly.

Therefore, understanding key challenges allows you to develop strategies that increase your credibility in the eyes of answer engines.

1. Dynamically Generated Content

Single Page Applications (SPAs)

🚩 Challenge:

In sites developed with frameworks like React, Angular, or Vue.js, content is dynamically generated via JavaScript after the initial page load.

💥 Impact:

Basic bots or bots that only review HTML might not be able to see this content.

More advanced bots may use a headless browser to execute JavaScript, but this process is resource-intensive and may not always yield accurate results.

👇 Example:

On a SPA-based page of a shadow work expert, content like “What is shadow work?” and “What are shadow archetypes?” is loaded only after the user clicks.

Although the page appears full, it contains a bare HTML structure before content loads.

When an answer engine crawls this page, it can’t find meaningful context, so the site doesn’t appear as a result for the query “What is shadow work?”

🛠️ Suggestions:

• Present content in a pre-rendered form to provide meaningful HTML before page load, allowing answer engines direct access.

• Use Next.js, Nuxt.js and then, 👇

• Create static-looking, semantically meaningful routes. For example, subpages like /what-is, /how-to, /examples help bots better map content.

• Instead of structured data only, design your content with systematic semantic groupings and question-and-answer format sections (e.g., clearly divided Q&A sections using headings).

Interaction-Only Data (Content Loaded by User Actions such as Click or Scroll)

🚩 Challenge:

Bots can’t see content triggered by forms (e.g., calculators).

Content that appears upon clicking “load more” or through infinite scrolling depends on interactions that bots can’t trigger automatically.

💥 Impact:

Bots can’t process your site or do so incompletely.

Since they must rely on static data, incomplete information may lead to incorrect answers.

👇 Example:

Bots fail on interactive areas like “Calculate price including VAT.”

🛠️ Suggestions:

• Present results not only inside the DOM after form submission, but also through server-side rendering (SSR) or as JSON.

2. Inability to Detect Real-Time Updates

🚩 Challenge:

The bot is limited to the content visible at the moment it takes a snapshot.

💥 Impact:

Content that changes over time (e.g., live prices, hourly weather, currency exchange) is processed either incompletely or using outdated versions.

👇 Example:

Even if the correct page exists for a query like “What is the latest iPhone price?”, the answer may be wrong.

🛠️ Suggestions:

• Provide critical data (price, score, time, etc.) directly in HTML.

• Use structured data to indicate freshness (e.g., lastUpdated or priceValidUntil).

• Prefer static HTML over client-side data.

3. Authentication-Required Areas

🚩 Challenge:

Login-required areas like member dashboards, profile pages, or post-payment content are inaccessible to bots.

No matter how valuable, this content remains outside the system if bots can’t access it.

Also, limiting this content to logged-in users disrupts the “semantic map” of content presentation.

💥 Impact:

Since these areas don’t provide content to bots, they’re invisible in related queries.

Also, the site’s information delivery strategy becomes blurry; the bot can’t distinguish between public and private content.

This reduces the page’s value in terms of trust, authority, and content integrity.

👇 Example:

A trauma therapist has a “frequently asked questions” section only accessible to registered users.

It includes content like “How to identify ancestral trauma?” and “What should I do before the first session?”

Since bots can’t see this, the page isn’t recommended in queries like “ancestral trauma symptoms.”

🛠️ Suggestions:

• Offer summary versions of locked content on public pages with semantic coherence.

• Create open content referencing locked sections.e.g., “Only members can access our detailed guide on ancestral trauma. But you can find the key information below.”

• Externalize context. For example, introduce “client guides requiring member login” on public pages to give bots contextual information.

• Use SSR or Static Site Generation (SSG) to create public versions of key content blocks that don’t conflict with membership systems.

4. Overfitting Due to High Semantic Density

🚩 Challenge:

The bot tries to infer more than the content offers on some pages, leading to misinterpretations.

💥 Impact:

Increased chances of generating hallucinations — inaccurate or misleading answers.

👇 Example:

On a product page, a user comment like “We loved this product” may be interpreted as general consensus.

🛠️ Suggestions:

• Use clear, simple, and measurable statements.

• Separate subjective feedback (like user reviews) from technical content.

• Support context with structured data such as review, prosCons, faqPage.

5. Interactive and Complex Forms

🚩 Challenge:

Forms containing Captcha, reCAPTCHA, multi-step flows, or conditional fields prevent bots from understanding and completing them.

Answer engines can’t extract data from such forms because the content is either hidden or bound to inaccessible logic.

💥 Impact:

If valuable information is presented through forms (e.g., “Which therapy method suits you?”), bots can’t reach it.

Results generated through such forms contribute nothing to AEO, as they remain invisible.

👇 Example:

A psychological counseling site offers a test titled “Find the right therapy for you.”

The user answers questions, and the system returns a result like “Cognitive Behavioral Therapy is suitable for you.”

But this result is shown after a form with Captcha.

Bots can’t understand the content, so the page doesn’t appear for queries like “Which therapy is right for me?”

🛠️ Suggestions:

• You can present form results as separate static URLs and HTML content. For example, each result-directed page could be a separate URL like /therapy-recommendation/cbt. These pages should include both the result and explanatory content.

• Instead of conditional fields, you can create AEO-friendly explanation blocks. For example, sections like “If you answer yes to this question, the following suggestion may be suitable for you” should present all possibilities clearly in text.

• You can relocate CAPTCHA systems to areas independent of the form. CAPTCHA should be structured only near the submit button and should not obstruct form content.

• Beyond the logic of the form, you can create guide content. Producing static pages that answer the same questions builds a linking structure (semantic sitemap) that guides bots.

• The logic behind the form can be externalized using JSON-LD FAQPage or HowTo schemas. For example: “If you answer yes to question X, usually Y type of therapy is recommended” – such explanations can be schema-supported.

6. Heavy and Complex JavaScript

🚩 Challenge:

JavaScript code used to load, manipulate, or generate content through user interaction poses dual issues for bots:

• Overly complex and nested structures delay or completely block content visibility in the DOM.

• Obfuscated JavaScript (minified or encrypted) makes it impossible for bots to interpret the content.

In client-side rendering models, content becomes visible only after being interpreted by the browser.

💥 Impact:

Since content is not visible in the HTML, answer engines cannot detect it or match questions to answers.

This situation prevents bots from understanding data structures within the code, making it impossible for them to build contextual representations, even if the content technically exists.

As a result, structured data embedded via JavaScript becomes unrecognizable, and expected answer engine outputs (e.g., recipe summary, step-by-step lists, price boxes) cannot be delivered.

👇 Example:

On a real estate site, home listings are loaded only via JavaScript after the user applies filters.

There is no listing data visible in the page source.

When Copilot crawls this page, it cannot answer questions like “Are there houses with these features at this price in this area?”

Additionally, the developer has minimized and obfuscated all JS code, further complicating the DOM extraction process.

🛠️ Suggestions:

• Serve content following the Progressive Enhancement principle. Basic content should remain visible even with JavaScript disabled.

• Ensure the page content is server-rendered and clearly embedded in the HTML.

• Validate post-hydration content: check if the DOM and structured data are truly visible after the page loads.

• Embed JSON-LD and schema.org tags directly in HTML: not through JavaScript, but within raw HTML to enable quicker and clearer understanding by answer engines.

• Simplify complex JS structures and ease content access. Avoid deeply nested setTimeout, event, and ajax chains; opt for cleaner data flows.

7. Content Drawn on Canvas Element

🚩 Challenge:

The <canvas> element, introduced with HTML5, allows JavaScript-based drawing.

However:

Text or graphics drawn directly on <canvas> are not part of the DOM and cannot be read by bots.

If canvas content includes meaningful data (e.g., statistics, headings, navigation, diagrams), and this isn’t accessible, critical information is lost.

💥 Impact:

If a user asks, “What’s the best-selling product in X category?”, and this is shown on a canvas, bots can’t see it.

👇 Example:

An education platform draws performance graphs using <canvas>:

The student’s last 5 exam scores and average are displayed only on the canvas using JavaScript.

There is no text or structured data for these values in the page source.

Answer engines cannot answer questions like “What score did this student get on the last exam?” or “What’s the average?”

🛠️ Suggestions:

• Provide fallback text inside <canvas>. For example: <canvas>Fallback: Last 5 exam results – 80, 90, 75, 95, 85</canvas>

• Add parallel textual content alongside the canvas. The same data can be listed in the DOM (even if hidden from users). Example: <ul class="visually-hidden"><li>Exam 1: 80</li><li>Exam 2: 90</li>...</ul>

• Support your content with structured data. Use schema.org types such as Dataset, EducationalOccupationalCredential, Rating, or Statistics.

• Link canvas explanations with ARIA labels: Use attributes like aria-label, aria-describedby to provide contextual understanding.

• If using WebGL or graphic libraries, support with alternative JSON APIs. If canvas data comes from external JSON, make this JSON bot-accessible.

8. WebAssembly (Wasm)

🚩 Challenge:

WebAssembly (Wasm) allows code written in languages like C/C++ or Rust to run in browsers at near-native speed.

However:

This code doesn’t reflect directly in the HTML DOM.

Content produced via WebAssembly is typically transferred into a canvas, video, or binary structure using JavaScript.

Thus, answer engine bots can’t understand what is being visualized or described.

This is common in games, interactive data simulations, or some SaaS tools.

💥 Impact:

Since content exists outside the DOM, bots can’t answer questions like “What does this app do?”, “What is the user shown?”, or “What is the result of the simulation?”

It creates semantic gaps for answer engines.

If textual info, graphics, statistics, or interactive data are produced solely via Wasm code, bots can’t access them and assume zero content.

👇 Example:

A weather forecast app is written in Rust + WebAssembly.

When a user selects a city, the Wasm engine visualizes weather modeling: temperature, wind, humidity.

However, this data is rendered only on a canvas and not written to the DOM.

An answer engine can’t extract info like “What’s today’s wind speed in New York?” because the data is neither in the DOM nor in a structured format.

🛠️ Suggestions:

• Implement DOM reflection. Data generated by Wasm should be visible within the DOM. Example: document.getElementById("output").innerText = "Wind: 23 km/h, Humidity: 65%";

• Use hidden DOM elements. Deliver content hidden from users but visible to bots (aria-hidden, visually-hidden).

• If the app generates predictable datasets (e.g., temperature, windSpeed), use JSON-LD with schema.org types such as WeatherForecast, Dataset, Place.

• Make the API accessible. If Wasm data comes from an external JSON API, structure it so bots can access it (including CORS permissions).

• Log data generated by WebAssembly and inject it into the DOM (progressive enhancement).

9. Applications Not Server-Side Rendered (Client-Side Rendering – CSR)

🚩 Challenge:

In the CSR approach, HTML, CSS, and JavaScript are sent to the client; content is dynamically generated in the browser after the page loads.

However:

Bots—especially those with limited resources or those that don’t execute JavaScript—only see the initial HTML (empty <div>s, script tags).

Since meaningful content is added to the DOM later, bots struggle to understand the page’s content and purpose.

This poses a major problem for answer engines, which work based on the question: “Is there any information at first load?”

💥 Impact:

CSR applications, where all or most content is generated via JavaScript in the browser, offer only a basic structure in the initial HTML. Bots need JavaScript to be executed in order to see meaningful content.

If the initial HTML output is meaningless, bots may assume “there is no content.”

Answer engines may fail when analyzing the page’s DOM-based structure during content generation or answer production.

👇 Example:

A university uses a React-based CSR website.

When the homepage first loads, it contains nothing but <div id="root"></div>.

Everything loads afterward: announcements, departments, application dates.

Since the bot doesn’t execute JavaScript, it sees none of this information.

As a result, the site doesn’t appear in queries like “X University application dates.”

🛠️ Suggestions:

• Enrich the initial HTML output. Core structures should be visible before JavaScript runs.

• Use Isomorphic/Universal Rendering. Frameworks like React and Vue offer solutions that render on both the server and client (e.g., Next.js, Nuxt.js).

• For pages that don’t change often, you can use prerendering services to generate static content.

• Provide fallbacks for answer engines: Titles, summary content, and structured data should be visible without JavaScript.

• Instead of serving different HTML to bots, build a Core AEO structure that provides meaningful content to all users. The goal isn’t just to “hack” the bot, but to build a system (HTML, content, structure, context) that is naturally meaning-focused.

10. Content Hidden via CSS (Except Malicious Use)

🚩 Challenge:

Some content, although present in HTML for user experience or accessibility purposes, may be hidden using CSS techniques like display: none, visibility: hidden, opacity: 0, or off-screen positioning.

This can pose a problem for AEO:

Bots may struggle to distinguish whether this content is visible, supportive, or deceptive.

This uncertainty may affect the accuracy of the meaning the site “represents.”

💥 Impact:

Hidden content may be ignored or incorrectly considered by answer engines.

Meaningful but low-visibility content may be skipped in semantically focused answer engine crawling.

Some systems may interpret such content as a “spam signal” (even if it’s not malicious).

👇 Example:

A health website hides the message “this content is intended for healthcare professionals only” using display: none.

The bot only sees drug names and dosage information, but cannot understand that the intended audience is medical professionals.

This leads to content being highlighted in the wrong context—or not at all.

It also disrupts content consistency across other pages.

🛠️ Suggestions:

• Clarify the context of hidden content: Use accessibility tags like aria-hidden, role="presentation" to convey intent to bots.

• Support semantic meaning: Provide alternative explanations for hidden content visibly or via structured data.

• Avoid placing critical information in hidden content. Core meaning-bearing content must always be visible.

• Add AEO-friendly explanations: Use <noscript> content or summary sections to give bots supportive signals.

• Create semantic contrast: Hidden content that carries no system-level meaning can be marked with attributes like data-visibility="low" (especially if serving content to custom bots).

Summary

To summarize, the areas where AI bots often struggle during crawling include:

• Dynamic content and JavaScript execution issues

• Content requiring user interaction

• Restricted areas behind authentication

• Interactive and complex forms

• Heavy and complex JavaScript usage

• Content drawn on canvas elements

• WebAssembly (Wasm)

• Content not server-side rendered

• Content hidden via CSS

🔑 Key Takeaway: The common denominator in these crawling issues is often JavaScript. This is why standard offers like llms.txt have been proposed as partial solutions.

Final Thoughts: Technical AEO Challenges Create Opportunities

Answer engines use advanced AI bots and web crawling tools.

Features like headless browsers, evolving JavaScript interpretation, and compliance with accessibility standards are critical advances to overcome these challenges.

However, developers also have responsibilities in this process.

The technical challenges discussed in this article are crucial for ensuring that AEO-compatible content is properly processed by bots.

By recognizing and addressing these issues, brands can gain a significant advantage in appearing in AI-generated answers.