Marketers’ Top 5 Challenges With AEO
HubSpot's State of AEO in 2026, based on insights from 4,000+ global marketers, shows that measurement, understanding, technical implementation, content, and tooling are among the biggest AEO challenges. Here is what they mean and what teams should do next.
Jeremy Prasetyo
Co-CEO, Lanesra AI
AEO is moving from an interesting experiment to a real marketing discipline. HubSpot's State of AEO in 2026 shows that marketers around the world are already adapting: 58% say their businesses are optimizing content for answer engines.
The report, based on insights from 4,000+ global marketers, also shows where teams are getting stuck. Across B2B and B2C, the biggest challenges are not simply “how do we write one more article?” They are measuring success, understanding how AEO works, technical implementation, creating the right content, and having the right tools and resources.
The short answer
AEO is not one tactic. It combines measurement, content, technical foundations, source authority, and continuous monitoring across AI search engines. The teams that treat it as a system will move faster than teams that treat it as another SEO checklist.
HubSpot also reports that 44% of marketers have made a business purchase based on brands they discovered through answer engines. That is why these implementation challenges are increasingly commercial, not experimental. HubSpot, State of AEO in 2026
The challenge map
Marketers’ AEO challenges
B2B vs. B2C respondents - HubSpot, State of AEO in 2026
1. Measuring success
The biggest operational gap
Traditional SEO has rankings, clicks, and organic traffic. AEO introduces different outcomes: mention rate, recommendation rate, citation visibility, answer position, sentiment, prompt coverage, and competitor share of voice.
The mistake is measuring only traffic. AI answers can influence a buying decision before a user ever clicks. Teams need a prompt set, a baseline, competitor benchmarks, and repeated measurement over time.
2. Understanding how AEO works
The conceptual gap is still large
AEO sits on top of several moving parts: retrieval, grounding, citations, query fan-out, entity understanding, source selection, and final answer generation. That is why simple advice such as “add FAQs” is not enough.
Google now explicitly documents retrieval-augmented generation and query fan-out as part of generative search, while OpenAI explains that ChatGPT Search can use web search and citations. The mechanics vary by engine, which is why visibility needs to be measured across more than one model. Google Search Central · OpenAI
3. Technical implementation
B2B feels this problem more strongly
For many brands, AEO becomes technical quickly. Pages need to be crawlable, important information needs to exist in text, structured data should match visible content, and product or company information needs to be consistent across the web.
Google’s own guidance for generative AI search still starts with technical search fundamentals: crawlability, internal linking, useful textual content, page experience, and accurate structured data. Google Search Central
4. Creating appropriate content
AI search rewards useful evidence, not content volume
Content for AI search needs to answer the questions people actually ask during discovery, comparison, and decision-making. It also needs evidence: clear product facts, use cases, comparisons, original expertise, documentation, and third-party validation.
Google specifically recommends unique, non-commodity, people-first content for generative AI search. The implication for marketers is straightforward: creating more generic pages is not the same as creating more useful evidence. Google Search Central
5. Lack of tools or resources
Manual testing does not scale
You can manually ask a few prompts in ChatGPT, Gemini, or another AI engine. But that does not tell you whether your visibility is improving across hundreds of commercially relevant questions, how competitors are moving, which sources are being cited, or whether sentiment is changing.
This is where dedicated AEO infrastructure becomes necessary. Lanesra continuously tracks visibility, recommendations, citations, sentiment, prompt coverage, and competitor performance across AI search so teams can move from manual checking to an operating system for AEO.
Two challenges just behind the top five
The pattern matters more than any single percentage. The biggest AEO problems are systemic. Marketers need to understand the mechanics, measure the right outcomes, fix technical gaps, produce better evidence, and monitor the result continuously.
What the mature AEO team looks like
A mature AEO program is not owned by one content writer or one SEO specialist. It connects marketing, data, content, product, PR, and technical teams around a shared question:
When a customer asks AI for advice in our category, are we understood, trusted, cited, and recommended?
That is also why AEO increasingly looks less like a campaign and more like a system. The work is continuous: monitor prompts, diagnose gaps, improve the evidence, measure again, and repeat.