The advent of AEO has given marketing strategists a new and seemingly endless realm of opportunity. Of course, it comes with its own issues and pitfalls. AI-powered platforms like ChatGPT, Perplexity, Google’s AI Overview, and more are reshaping how people discover and interact with content. These platforms don’t just link to results; they generate answers, pulling from a range of structured and unstructured sources to deliver what users are looking for instantly.
That shift is forcing marketers, content strategists, and SEO professionals to adapt. Enter Answer Engine Optimization (AEO): the practice of shaping your digital presence to ensure your brand, product, or service gets surfaced directly in AI-generated answers.
But AEO isn’t just traditional SEO with a new name. It comes with its own set of challenges, from getting your structured data right to building visibility in LLMs that don’t always cite sources. Since this space is evolving rapidly, many organizations are still figuring out where to start (or why their current efforts aren’t gaining traction).
This article breaks down the most common AEO challenges brands face today; and how to overcome them. These insights will help you stay ahead and build a more resilient, future-proof search strategy.
Structured data helps AI-powered engines understand what your content is, not just what it says. Without it, you’re far less likely to show up in AI-generated answers, voice search, or rich SERP features like featured snippets. Goodie has compiled a resource outlining the importance of understanding the role of structured data in AEO.
Proper schema improves:
Most websites either don’t use schema at all or implement it incorrectly. Missing, outdated, or overly basic markup prevents answer engines from surfacing your content (especially for fact-based or product-related queries).
Tip: Tie your schema to known entities (e.g. Wikidata) to boost credibility in LLMs.
Answer engines aren’t looking for long-winded blog posts; they’re looking for clear, direct answers.
Content that’s buried in fluff, lacks headings, or fails to address common questions head-on often gets skipped by AI platforms. If your content doesn’t look like an answer, it won’t be treated like one.
Most web content is written for human readers, not AI models. Without tight structure, consistent formatting, or scannable sections, engines struggle to extract useful responses.
Tip: Target ~30–50 word answers where possible. That’s the sweet spot for most answer engines.
It’s not enough to include the right keywords; your content has to match why someone is searching.
Answer engines prioritize intent over exact match. If your content doesn’t directly solve the user’s problem or answer their question, it won’t get surfaced, even if it’s technically “optimized”.
Many pages are built around broad, high-volume terms without considering the actual question behind the query. This disconnect makes your content invisible in AI-generated answers.
Tip: Use AI search results (like ChatGPT or Perplexity) to reverse-engineer how queries are being answered, then spot where your brand can do better.
Answer engines want trusted sources. If your site only touches a topic here and there (or lacks internal depth); it’s unlikely to be pulled into AI-generated results.
Topical authority signals to AI that your brand knows the subject, making it more likely to be cited or featured.
Many brands publish scattered, one-off content without building a strong content ecosystem. This limits visibility across both traditional search and answer engines.
Tip: Use E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) as your guiding framework for content creation.
Even if your content is great, answer engines won’t surface your brand if they don’t know it exists.
Many AI platforms (especially closed models like ChatGPT or Google SGE) rely on internal datasets, knowledge graphs, and external citations. If your brand isn’t present in those ecosystems, you’re effectively invisible.
Smaller or newer brands often struggle to break into the AI index due to limited mentions, weak domain authority, or a lack of citations from trusted sources.
Tip: Don’t just publish. Promote. Distribution across credible domains is key to training the algorithms on you.
One of the biggest challenges with AEO is knowing whether your efforts are actually working.
Traditional tools like Google Analytics and Search Console weren’t built to track AI-generated answers, voice mentions, or zero-click impressions, making AEO performance feel invisible.
Without clear visibility into where or how your brand appears in answer engines, it’s hard to optimize, prove ROI, or prioritize next steps.
Tip: Treat AI visibility like a performance channel; measure it, test it, and iterate like you would with paid or SEO.
AI-powered voice assistants like Google Assistant, Siri, and Alexa now answer millions of queries daily. However, most content isn’t built to speak.
Voice search relies on concise, natural-language responses. If your content doesn’t match how people talk, it likely won’t be selected.
Long blocks of text, jargon, or indirect answers make it harder for voice interfaces to extract clean, spoken replies.
Tip: Read your answers out loud. If they sound awkward or robotic, rewrite them.
As search evolves beyond links and into answers, AEO is becoming essential.
From structured data gaps to intent mismatches and tracking blind spots, the challenges of AEO are real; but they’re also solvable. With the right content strategy, formatting, and tools in place, brands can not only adapt to AI-powered search—they can lead it.
The key is consistency: structure your content clearly, match how users ask questions, build topical authority, and monitor how and where your brand shows up. As answer engines continue to reshape the search landscape, the brands that prioritize AEO now will have a serious edge in visibility, engagement, and trust.