Best practices for answer engine optimization (AEO) marketing teams can’t ignore

A few months back, I was having a bit of a professional identity crisis. And it’s all thanks to answer engine optimization (AEO) and AEO best practices.

Before 2024, I spent the better part of a decade focused on topping search engine result pages — and, frankly, I was great at it. I knew the ins and outs of keywords, schema, and even technical SEO aspects like site speed.

But with the rise of AI, those skills were slowly becoming less urgent, for lack of a better word. (Cue marketer existential panic.)

Search and consumer behavior have changed dramatically. While traditional search engines still dominate, people increasingly turn to AI tools like ChatGPT to answer their questions. Heck, with 79% of those who already use AI for search believing it offers a better experience than traditional search engines, even Google has introduced AI overviews to stay competitive.

But what about all my SEO glory? This shift demands a new approach. Unfortunately, AEO is generally a mystery to businesses and marketers alike. HubSpot is no exception, but we’re finding our way.

We’ve been researching and experimenting with how we produce and format content for AI and loop marketing for almost a year. In this article, I’ll share some of the most critical AEO best practices we’ve uncovered.

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To get started, map user questions, structure content for quick answers, add the right schema markup for AEO, and track your visibility with tools like HubSpot’s AI Search Grader. Ready to see where you stand? Check it for free.

Read: 8 SEO Challenges Brands Face [HubSpot Blog Data]

Answer engines don’t just give users possible resources; they attempt to provide the exact answer they want.

For example, if I ask ChatGPT for the best action movies of all time, it’ll give me a list compiled from many sources rather than simply linking to some pages for me to check out.

Because of that, the goal of AEO is citations and inclusion in those answers.

As marketers, you need to structure your content for extraction, use schema markup to clarify meaning, and build authority so language models trust and reference your expertise. And you’ll track success with the number of zero-click answers, AI summaries, and voice responses, even when users never visit your website.

The strategic difference is visibility without traffic. A well-optimized answer might get cited thousands of times in ChatGPT conversations or Google AI Overviews without generating a single session in your analytics. This challenges traditional attribution models but extends your brand’s reach into entirely new contexts where buying decisions increasingly begin.

In short: SEO gets traffic. AEO owns the answer.

Read: The essential SEO tutorial for thriving in the age of AI-driven search

Google reports that nearly 60% of searches now end without a click as users get what they need directly from AI Overviews, featured snippets, or knowledge panels. On top of that, generative AI is being embedded into every major platform (i.e., Microsoft Copilot, Perplexity, and Gemini), and voice assistants answer queries in seconds, often citing a single source.

ChatGPT alone has nearly doubled its weekly average users to 800 million from February to August this year, so clearly, this trend is not slowing down.

Brand visibility now depends on being cited and summarized by these systems, not just ranking well in search. But that doesn’t mean you can neglect SEO.

AI engine optimization actually complements SEO and inbound marketing; it doesn’t replace them. AEO draws on many SEO foundations — strong content, domain credibility, internal linking — but reorients priorities so that content is machine-friendly, structured, and ready to be quoted or excerpted.

While traditional SEO remains essential for driving traffic, AEO determines whether your brand appears in the most important answers. So, think of it as a new layer to your existing content strategy, not a separate thing competing for resources.

Once collected, audit your existing content to identify gaps or opportunities to update content. Also, research them in both search engines and AI tools to see how your competitors are currently performing for them.

From there, segment questions by funnel stage and buyer persona. Here are some general guidelines you can follow:

• Awareness-stage questions need educational, jargon-free answers.
• Consideration-stage questions require comparisons, frameworks, and proof points.
• Decision-stage questions demand specifics about implementation, pricing, and support.

Pro tip: Track this inventory in a shared spreadsheet or your CRM, noting which questions you’ve covered, which are in progress, and which represent content gaps your competitors might be filling first.

2. Structure content for direct answers and extractions.

When you search Google, its AI doesn’t read your entire article linearly. Instead, it identifies answer-like structures (short paragraphs after questions, numbered steps, comparison tables) and decides if that content directly addresses the user’s query.

Large language models (LLMs) like ChatGPT do something similar during training and retrieval, prioritizing content that presents information in clear, modular blocks that they can confidently cite.

To optimize for this behavior, lead every key section with a 40-60-word direct answer that fully addresses the question, similar to how you would typically go after “featured snippets” in Google (more on that later).

If someone asks, “What is inbound marketing?” define it completely in two or three sentences in your first paragraph, no fluff, preamble, or quips (as much as this one pains me), just the answer. Follow that with supporting detail, examples, and context for readers wanting depth.

Also, use scannable formatting like bullet points, numbered lists, and tables, and keep paragraphs under four sentences when possible. This isn‘t about dumbing down your content, it’s about making valuable information accessible to both hurried readers and parsing algorithms.

If you have the resources, adopt reusable content block patterns that answer engines recognize. Think definition blocks for terminology, step-by-step blocks for processes, pros-and-cons blocks for evaluations, example blocks for illustration.

Here’s an example from one of my HubSpot articles on organic marketing:

Source

These patterns act as semantic signals that help AI identify what type of information you’re providing and how to extract it accurately.

Pro tip: Content Hub can help you templatize these patterns, streamline content briefs, and maintain editorial governance at scale as your team produces more AEO-optimized content. So can schema.

3. Implement schema that answer engines read.

Schema markup is structured data you add to your HTML to explicitly tell search engines and AI systems what your content represents.

It’s the difference between Google guessing that your page is a how-to guide and Google knowing with certainty that it is, with five specific steps, an estimated completion time, and required tools.

Focus on these core schema types for AEO impact:

• Use FAQPage schema on pages with question-and-answer pairs. This helps Google surface your content in rich results and gives LLMs clear question-answer associations to extract.
• Apply HowTo schema to instructional content, marking each step, its position in the sequence, and any images or warnings.
• Tag editorial content with Article schema, including headline, publish date, author, and organization. This establishes freshness and authority signals.
• Add Speakable schema to key sections you want voice assistants to prioritize when reading answers aloud.
• Finally, implement Organization schema sitewide to clarify your brand identity, logo, and social profiles for consistent entity recognition.

CMS SEO tools in platforms like HubSpot let you templatize schema across content types so your team doesn’t hand-code for every post. If you’re a HubSpot user, set up templates for your most common content types— blog posts, guides, FAQs, and product pages — and the schema will be applied automatically with clean, crawlable HTML.

4. Win featured snippets and “People Also Ask.”

Featured snippets and “People Also Ask” boxes are Google‘s most visible answer formats, and they’re training data for how AI Overviews select and present information.

When your content appears in a featured snippet, you’ve essentially been pre-selected by Google as the authoritative answer, which definitely increases your chances of being cited by AI summaries and language models that crawl the web.

To win featured snippets, keep these guidelines in mind when creating content:

• Format your answers to match the snippet type in Google. If the existing snippet is a numbered list, structure your answer as a numbered list. If it‘s a paragraph, lead with a concise paragraph answer. If it’s a table, present your information in a comparison table with clear rows and columns.
• Mirror the question wording in your H2 or H3 header. If the PAA question is “How do you calculate ROI?”, your header should match that phrasing exactly.
• Place your answer high on the page. Ideally, this is within the first two scrolls. Google prioritizes content that’s easily accessible and clearly structured.
• Use the inverted pyramid approach: answer first, then provide context, examples, and related information for users who want to go deeper.

Pro tip: To systematically capture more features,  harvest “People Also Ask” questions for your target topics every quarter. Open an incognito browser, search your core keywords, and document every PAA question that appears. Note which ones you already answer well, which you answer poorly, and which you don’t address at all.

Prioritize updating existing high-authority pages to target new PAA questions rather than creating net-new content. Google favors established pages for featured snippets, so enhancing what already ranks often delivers faster results.

5. Prioritize credibility.

Recent research shows that content including citations, quotes, and statistics is 30-40% more visible in AI search results. This emphasizes the importance of backing up claims with credible sources and maintaining high editorial standards. That said, strengthen your content by:

• Format your content for easy skimming. Think bullet points, schema, etc.
• Supporting all claims with facts. Including data-driven insights and expert citations to increase trustworthiness and demonstrate expertise. (Even better if it’s original data or research.)
• Use trusted resources. Leverage authoritative publications that AI models favor while maintaining originality in your analysis.
• Update existing content regularly with new data and insights. This maintains relevance and helps already-ranking pages stay on top.

6. Build a strong, positive online presence across multiple channels.

Social proof works. I mean, it’s marketing 101. The more people rave about something or buy it, the more others are likely to believe it’s true. AI and LLMs work similarly. They learn what to trust based on which sources appear frequently across authoritative contexts.

In other words, LLMs are more likely to treat your content as credible and worth citing if your brand is cited in reputable industry publications, discussed in high-quality forums, and referenced in academic or government sources.

Off-site authority isn’t just about backlinks for SEO, however. It’s about establishing proof that your brand is a legitimate subject-matter expert across many different online territories. Think other publications, forums, review sites, and social media platforms.

Knowing this, you want to develop a multichannel distribution strategy that prioritizes platforms where your audience and AI training data intersect. This could mean:

• Publishing thought leadership on LinkedIn. As a professional platform, this will help you reach others in your industry and establish executive visibility.
• Creating educational video content for YouTube. Video transcripts are crawled by AI systems and often more detailed than blog posts.
• Participating authentically in relevant Reddit communities and Quora discussions. These platforms are increasingly cited by AI as sources of real user sentiment and practical advice.
• Pitch byline articles to industry publications with strong editorial standards. These third-party endorsements signal authority far more than content published exclusively on your domain or smaller publications.
• Creating original research and data visualizations. When you publish a survey, benchmark report, or data-driven insight, create link-worthy assets that get cited across the web. Each citation reinforces your authority and increases the likelihood that AI models surface your data when answering related questions.
• Establishing a distribution cadence and repurposing workflow. A single piece of research can become a LinkedIn post, a YouTube video, a contributed article, a Reddit discussion, and a Quora answer, each tailored to the platform and audience.
• Assigning a content distribution owner. This person will be responsible for adapting core assets and tracking where they’re shared. Include PR angles and thought leadership opportunities in your planning; speaking engagements, podcast interviews, and media mentions all contribute to the authority signals that LLMs evaluate.

Multi-channel diversification is built into the Loop Marketing playbook in the Amplify stage. Learn more about it here.

Pro tip: Content Remix can help you with this repurposing in one click.

Plus, Marketing Hub automation can help orchestrate this distribution at scale, scheduling cross-platform posts, tracking engagement, and measuring which channels drive the most authority signals and referral traffic back to your owned content.

7. Optimize for voice answers across assistants.

Voice assistants like Alexa, Siri, and Google Assistant choose answers differently from visual search results and LLMs.

They need concise, factually unambiguous, and structured content that can be spoken aloud in 15-30 seconds and is formatted for natural language comprehension.

When someone asks their smart speaker a question, the assistant typically cites one, single source. You want that to be yours. Here’s how you can do that:

• Write answers in spoken-friendly language. Avoid jargon, long dependent clauses, and ambiguous pronouns. A voice assistant reading “It enables seamless integration” out loud leaves the listener confused about what “it” refers to. Instead, repeat the subject: “HubSpot’s API enables seamless integration.”
• Use Speakable schema markup. This tells assistants, “This paragraph is concise, self-contained, and ready to be read aloud.”

• Test voice queries on Alexa, Siri, and Google Assistant to audit your visibility.
• Create a naming convention for voice-optimized content blocks in your CMS. Label FAQs, definitions, and key takeaways with Speakable markup. This helps your team knows which sections have been voice-optimized.

Read: «How and Why to Optimize Your Website for Voice Search»

8. Ensure local optimization for Google AI mode and voice.

Local businesses face a unique AEO challenge: queries that seem non-local often surface local entities in AI-generated answers.

For example, when someone asks “best coffee shop for remote work,” Google AI Overviews and voice assistants frequently respond with specific nearby options, pulling data from Google Business Profile and local landing pages.

You’re invisible in these high-intent moments if your local data is incomplete or inconsistent.

Cover your bases by:

• Optimizing your Google Business Profile. This means you need to verify your business name, address, and phone number match your website exactly. Add complete business hours, including holidays and special events. Upload high-quality photos of your location, products, and team. Select all relevant categories. Google uses these to match your business to voice queries. Write a keyword-rich business description that includes the services and questions your customers actually search for.
• Building a strategy for getting reviews. Ask satisfied customers to leave Google reviews, and respond promptly to every review — positive or negative. Review volume and recency are strong ranking signals for local AI results, and LLMs sometimes cite review themes when recommending businesses.
• Create local landing pages for each service area. This was one of the first strategies I saw big wins from for a client years ago, and it is still effective. Even if you’re a single-location business, dedicated pages for “marketing consulting in Austin” or “HVAC repair in Brooklyn” give AI systems clear geographic and service signals to extract. Use consistent name, address, and phone number (NAP) formatting across all pages.

• Ensure your local business data is accurate and consistent across sources. This means on major platforms like Google Business Profile, Apple Maps, Bing Places, your website, and even Mapquest (Yes, they’re still around!). Voice queries like “What time does [business name] close?” or “Is [business name] open today?” pull from structured sources. Inconsistent data confuses customers as well as AI systems and dilutes your local authority. With this in mind, set a quarterly audit schedule to check and update this information as your business evolves.

Source

Answer engines favor content that loads quickly and doesn’t frustrate users. Aim for Core Web Vitals that pass Google’s thresholds: LCP under 2.5 seconds, FID under 100ms, CLS under 0.1. Compress images, minimize render-blocking resources, and use a CDN.

Write clean, descriptive URL slugs for every page.

A URL like /blog/what-is-inbound-marketing clearly signals what the content is about. A URL like /blog/post-47293 tells AI systems nothing, making your content harder to categorize and cite.

Maintain strict heading hierarchy with one H1 and logical H2-H3 structure.

Every page should have exactly one H1, while H2s divide the body into its major sections. From there, H3s and H4s should divide it further.

Don‘t skip levels (H2 to H4) or use headers for styling instead of structure. This hierarchy is one of the strongest signals AI systems use to parse your content’s organization.

Add internal links with specific, descriptive anchor text.

When referencing related content, use anchor text that describes what the linked page is about, not generic phrases like “click here” or “learn more.” Internal links help AI systems map your content relationships and understand topic clusters.

HubSpot’s Content Hub and CMS Hub provide built-in tools to manage internal linking at scale and ensure every page connects logically to your broader content ecosystem.

Test that essential content remains accessible with JavaScript disabled.

Test your page with JavaScript disabled. Can you still read your answers, navigate headings, and see essential information?

If critical content disappears without JS, crawlers and assistive technologies can’t access it either. Build a baseline experience that works without JavaScript, then enhance progressively.

For instance, track your share of featured snippets and PAA appearances over time using tools like HubSpot’s AI Search Grader. Monitor branded search volume. If your AI visibility increases, you should see more people searching your brand name directly after encountering it in AI answers.

Also, survey new customers about how they first heard of you; increasingly, answers will reference “saw you mentioned in an AI search” or “found you when researching with ChatGPT.” Correlate AEO milestones with pipeline velocity and deal size to demonstrate business impact even when the path isn’t linear.

Challenge: It’s difficult to know which AI engines actually cited your content.

Solution: Most AI platforms don’t provide “Search Console for LLMs,” where you can see when and how often you were cited. So, you’ll need to create a simple manual tracking system.

Start by assigning a team member to periodically query major AI platforms (ChatGPT, Perplexity, Google AI Overviews, Bing Chat) with your target questions and document when your brand appears.

Log the query, platform, date, and whether you were the primary source or mentioned alongside competitors.

This qualitative data helps you understand which content formats and topics earn the most AI visibility. Over time, patterns will emerge. Certain content types get cited more reliably, or specific platforms favor different answer structures. Use these insights to refine your AEO content strategy even without perfect analytics.

Challenge: Content teams don’t have the capacity to retrofit existing content.

Solution: Prioritize ruthlessly.

AEO can feel like an overwhelming lift if you‘re trying to optimize thousands of existing pages at once. Start with your top 20 highest-traffic pages and the 20 pages that rank on page one but don’t yet win featured snippets. These are your highest-leverage opportunities.

Add schema and answer-first formatting to these pages first. Then expand to pillar pages and core conversion content.

Challenge: Teams are unfamiliar with schema and structured data.

Solution: Schema implementation is often the bottleneck because it requires collaboration between content creators who understand the information and developers who can implement JSON-LD correctly. Bridge this gap by creating schema templates that your content team can populate without writing code.

Tools like Google’s Schema Markup Generator or HubSpot’s built-in schema modules let non-technical users add structured data through form fields.

Pair this with a validation workflow where someone tests each page with Google’s Rich Results Test before publishing. Over time, as your team sees the impact of schema on featured snippet wins and AI citations, they’ll build fluency and confidence.

Challenge: AI answers change rapidly, and there’s no clear “winning” format.

Solution: The way Google AI Overviews format answers today may differ from how they format them next quarter, and ChatGPT’s citation behavior evolves with each model update. This unpredictability makes teams hesitant to invest, but hey, the volatility of search engines didn’t stop SEO from being a non-negotiable.

Anchor your strategy in principles that remain stable regardless of algorithm changes:

• Answer questions directly
• Structure content clearly
• Build authority across the web
• Use semantic markup to clarify meaning

These fundamentals improve user experience and site performance even if AI algorithms shift. Instead of optimizing for a specific engine’s quirks, you’re making your content universally understandable and valuable, which pays dividends across all discovery channels.

Challenge: Legal and compliance teams worry about AI misrepresenting your content.

Solution: This is a real concern, especially in regulated industries. AI systems sometimes paraphrase incorrectly or cite out of context. Mitigate this risk by being extremely precise in your answer’s first paragraphs.

If the first 60 words fully and accurately answer the question, there’s less room for AI to misinterpret. Avoid nuance and caveats in your direct answers; save those for supporting paragraphs.

For highly sensitive topics, consider whether you want to be cited at all. In these cases, you can use robots.txt rules to block certain AI crawlers, though this, of course, limits your visibility. Balance risk and opportunity with your legal team, and establish a monitoring process to flag and correct instances where your content is misrepresented in AI outputs.

HubSpot’s CMS Hub makes adding schema automatic with templates.

How can we track AI citations without a new platform?

You don’t need expensive enterprise software to begin tracking your AEO performance. Start with a simple spreadsheet and a manual audit process, then layer in free or low-cost tools as you scale.

Create a tracking log with these columns: date, query, AI platform (ChatGPT, Perplexity, Google AI Overviews, Bing Chat), your brand mentioned (yes/no), cited as primary source (yes/no), competitor mentioned, and notes. Assign someone on your team to query 10-15 high-priority questions across multiple AI platforms each week. Document whether your brand appears in the answer, how prominently, and what content gets cited.

This qualitative tracking reveals patterns. Certain topics earn more visibility, specific content formats get cited more often, or particular platforms favor your brand over competitors.

Use HubSpot’s AI Search Grader to get a baseline assessment of your AI visibility across key queries. This free tool shows where you’re already appearing in AI-generated answers and identifies opportunities to improve.

Combine this with Google Search Console to track featured snippet wins and PAA appearances; while these aren‘t exactly AI citations, they’re strong proxy metrics for content that AI systems find extract-worthy.

Set up branded search monitoring in Google Analytics 4. If your AEO efforts increase awareness, you should see more users searching your brand name directly after encountering it in AI answers.

Create a custom report that tracks branded organic sessions, new users from branded queries, and conversions from branded traffic. Increases here suggest your AI visibility translates to downstream business value even when the original discovery happened outside your website.

As your AEO program matures and you need more sophisticated tracking, consider platforms built for AI visibility measurement. However, in the early stages, a disciplined manual process and smart use of free tools provide more than enough insight to guide strategy and demonstrate progress to stakeholders.

Will AEO replace SEO?

No. AI engine optimization and search engine optimization are complementary, not competitive. Both are essential for maximum visibility in an AI-augmented search landscape, and trying to choose one over the other leaves significant opportunity on the table.

Off and on-page SEO remain foundational because they determine whether AI systems discover your content in the first place. Language models and answer engines crawl the web the same way traditional search engines do.

If your site has poor technical health, slow load times, or weak domain authority, AI systems won’t index your content deeply or trust it enough to cite it. Strong SEO fundamentals (e.g., fast pages, clean HTML, authoritative backlinks, and crawlable structure) are prerequisites for AEO success.

Invest in both.

What’s the best way to keep AEO content fresh?

AEO content requires ongoing maintenance because AI systems prioritize recency and accuracy. Outdated answers hurt your credibility and reduce the likelihood of being cited.

• Start by assigning ownership. Every piece of AEO-optimized content should have someone with subject-matter expertise responsible for keeping it accurate and up to date.
• Set a review schedule based on content type and topic volatility. High-velocity topics like industry news, tool comparisons, or regulatory guidance need monthly or quarterly reviews. Evergreen content like foundational definitions or historical explainers might only need annual updates.
• Monitor People Also Ask and AI-generated answers for new questions. If Google starts showing PAA questions you haven’t addressed, update your existing pillar page or FAQ to include them rather than creating a new article. AI systems favor established, comprehensive pages over scattered content, so enhancing authoritative pages often delivers better results than publishing fresh posts.
• Track product and market changes that invalidate existing answers. Stale answers erode trust fast.
• Use AI Search Grader and manual audits to identify citation drops. Refresh your page with updated data, examples, and direct answers to reclaim any visibility.

AEO content isn’t “set it and forget it.” Treat it like a living knowledge base that evolves with your business and the questions your audience asks. The brands that commit to continuous refinement will maintain AI visibility as algorithms and user behavior shift over time.