How to Create an AEO Strategy for SaaS: A 7-Step Framework 

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You spent months building an SEO strategy, chasing high-intent keywords, optimizing pages, and finally getting to the first page on Google, just to notice that your prospects never land on your website. They got their answers from Google’s AI Overview, asked a follow-up in ChatGPT, and landed on your competitor’s site, which got cited instead.

Why is the traffic disappearing? Why does a competitor get cited instead of you, even when your content is better? And what can you do about it? 

You need a tailored AEO strategy that includes deeply understanding the prompts your audience asks, identifying which sources AI engines already trust, building content around the right entities and questions, and measuring whether those efforts actually translate into AI visibility.

In this guide, you’ll learn how to create a successful AEO strategy for your SaaS business, how to identify high-intent questions, create AI-optimized content, increase your chances of being cited, and the framework you need to become the source they choose. 

What Is an AEO Strategy?

AEO strategy is the process of getting your content cited in AI answers from tools like ChatGPT, Perplexity, Gemini, and Google AI Overviews. 

Think of this AEO SaaS strategy as a whole system, where you should analyze:

• Which prompts actually matter to your buyers
• How to structure your content
• How to build third-party trust signals
• Whether they move the needle

Answering each question can help you gather the right data to create a detailed AEO strategy that maps your target audience’s needs.

How Is AEO Different from SEO? 

Short answer: SEO gets you ranked. AEO gets you cited. They’re related, but they’re optimizing for different outcomes. 

Aspect	SEO	AEO
Goal	Rank on the SERP and earn the click	Get cited or summarized directly in AI-generated answers
Success looks like	Positions 1–3 in search results and growing organic traffic	Brand mentioned in ChatGPT, Perplexity, or Google AI Overviews—even if users never click through
Optimization target	Keywords, backlinks, and technical SEO	User questions, entities, structured data, and trusted third-party citations
Content shape	Long-form, keyword-focused content	Answer-first, well-structured content that’s easy for AI to extract and cite
Where it happens	Primarily on your own website	Across your website and trusted third-party sources (e.g., Reddit, G2, review sites, industry publications, and press coverage)

Why Most SaaS Brands Fail at AEO

AEO doesn’t replace SEO. It builds on it. If your SaaS technical SEO is a mess – like a slow site, broken internal links, no crawl setup, etc, generic content, unclear heading structure – AI crawlers will struggle to access your page and your content too.

You can’t skip the foundation.

Before optimizing content for AI-generated answers, ensure your SaaS website is technically sound, easy to crawl, and provides a clear structure that both search engines and AI models can reliably process. 

In addition to technical SEO, your content structure and quality matter just as much. If your page contains the same information that your competitors already have, without a unique perspective, or fails to demonstrate first-hand expertise, AI systems have no reason to cite your content as a source. 

You are not doing anything different. In other words, you repeat what’s already there, which doesn’t bring any value for readers and AI search engines.

This is why we don’t see AEO as a standalone optimization tactic. 

Why B2B SaaS Needs an AEO Strategy Now 

SaaS B2B businesses need to adapt to AI search faster, because they have long and critical decisions to make. In this journey, AI feels like a faster way to compare options, rather than clicking on 10 different pages on the SERP.

Some key signals that it’s time to invest in an AEO strategy include: 

• Most of the searches end without a click. The zero-click search means the engine already gave the answer without the need to click on a blue link.
• Buyers need to analyze 10+ pieces of content before deciding. This step is easier if done with AI search engines, as they will highlight the most important information as a summary.
• Being cited builds trust. When AI consistently references your brand as a credible source, you gain visibility before buyers even reach your website. 

How to Create an AEO Strategy: Our 7-Step Framework

Let’s go through the whole process step by step.

Step 1: Baseline Your Current Brand AI Visibility 

You can’t start your AEO strategy without knowing your brand’s current AI visibility status. 

Start by creating a strategic map that will include:

• 5-10 prompts that your target audience type in ChatGPT, Perplexity, or other LLM, when they search for your product or service. For your SaaS, this may look like: “What’s the best [category] software for a startup?”, “[Your product] vs [Competitor A] – which is better for [use case]?” and similar.
• Run each prompt across the most popular LLMs (at least 3 different), and see if your brand is being cited. 
• Document your results, analyze if your brand shows up, analyze the AI citations to understand where your brand is recommended as a solution, and identify gaps compared to competitors. 

Step 2: Map the Prompts Your ICP Actually Asks 

This is actually the most important step in the B2B SaaS AEO strategy. 

You should create the prompts based on your Ideal Customer Profile (ICP). This means understanding how your target audience searches for your service or product. For this purpose, you should analyze:

• Commercial/transactional queries that actually drives singups, demos, and consultations.
• Community threads on Reddit or Quora that target SaaS-related questions.
• Reverse-engineering on your competitor’s content and find prompts that win in AI search engines.
• People also ask (PAA) are the exact same questions people ask related to your subject matter.
• Follow-up questions that most LLMs suggest to you. Those are prompts real users will probably ask next. 

These are the follow-ups Perplexity suggest me when I searched for “how to get saas content cited in google ai overviews”. You can use them as an H2 or H3 heading, or include them in the FAQ section.

• Long-tail queries in Google Search Console, where you can find potential long-tail keywords that your target audience asks. These exact keywords most closely match how people actually prompt AI. 

Step 3: Analyze Who AI Already Cites 

Before you start creating the content, you need to analyze which pages are being cited for the prompts you used in Step 2. 

This is an important part because it will help you understand why those pages get cited, what format the cited content follows, how it is structured, and any other patterns you can apply to your own content.

From our experience, when we build an AEO strategy for our clients, we try to inspect every aspect of the competitors’ content. And most of the time, the brand that gets AI visibility is due to these reasons:

• Strong E-E-A-T Signals. The best-performing content demonstrates clear experience, expertise, authoritativeness, and trustworthiness. So, adding a unique angle helps your content stand out, provide greater value, and increase its chances of being cited by AI. 
• Images and USG Content. We notice that pages ranking on Google more often include examples, original images, testimonials, case studies, and other forms of user-generated content that strengthen the page’s credibility. Using these elements shows that you are supporting your claims by providing practical evidence that builds trust. 

Therefore, analyzing who gets cited is also about understanding the opinion AI models have already formed about your own brand before you write a single word of new content. The SEO Director at SmartClick, Davor Karafiloski, explains this in depth, since he is involved in the AEO strategy for our clients, from tracking the right prompts to measuring AI visibility and its impact on pipeline. 

“When it comes to building AI search strategies for SaaS, we start by analyzing the gap and brand sentiment, then move from there. We try to understand which of the client’s core product-related topics they appear for, and more importantly, we try to understand the opinions AI models have already shaped about the brand, product, its weaknesses, and strengths. We call that brand sentiment analysis.

We usually get the necessary information from our query fan-out analysis, and then proceed by bridging those gaps. If, for example, LLMs believe the client’s product is slow to integrate, we first verify the information internally with the client. If it’s untrue, we then try to understand which sources shaped that opinion in AI models. Then proceed by influencing that opinion by creating internal documentation, influencing testimonials, reviews, forum discussions, etc. Basically, since AI models are shaping their opinion based on multiple sources scattered across the web, we try to naturally reshape that opinion using a variety of tools.“

Step 4: Structure Content for AI Extraction 

The best part starts here. From all the analysis you made, these tips will help you structure your content for AI extraction:

• Answer in the first two sentences. Provide a clear, direct answer at the beginning of the section. Avoid explanations, examples, or supporting details. This is what most AI engines tend to crawl and cite first. 
• Use the actual buyer’s question as subheadings. Use the real questions as subheadings, where each question will be directly answered, straight to the point.
• Break comparisons into a table where possible. Tables are the best format for comparisons. They extract information into a clearer structure, making comparison data easier for AI systems to extract accurately. 
• Support key claims with evidence. Once you’ve answered the question, include relevant data, a real example, or case study that supports your content. Try to explain from your own experience and how your team solved this for a real client. 
• Keep each section concise and focused. Write in smaller paragraphs, in a conversational tone. Avoid too many technical terms or jargon, as they make it harder for AI to understand and extract information. 
• Always include bullet points. Add bullet points under each subheading to break down detailed data. 
• Show clear freshness signals. Add a visible “Last updated” date on the page, as it shows LLMs that your content is relevant, with the newest trends and statistics.

Step 5: Implement the Schema Markup 

Schema markup will tell AI crawlers “here’s exactly what this page is and what it’s claiming”. For SaaS specifically, four schema types do most of the work, including:

• Organization schema – anchors your brand identity (name, logo, sameAs links to your social/review profiles) so AI systems can correctly attribute content back to you.
• FAQPage schema – wraps your Q&A content in a format built for extraction. This is the closest thing to a direct line into AI answers.
• Product / SoftwareApplication schema – surfaces pricing tiers, category, and ratings in a structured way, which matters a lot for comparison-style prompts.
• Article schema with clear datePublished/dateModified – since freshness signals influence which source an AI model trusts when two pages say similar things.

Example for FAQ markup:

Step 6: Build Off-Site Citation Signals 

Don’t forget the signals that work behind the scenes. AI engines trust third-party sources. For this matter, focus on the sources AI models cite most for B2B software categories: 

• Review platforms (G2, TrustRadius, Capterra). These pages get cited constantly for “X vs Y” prompts. 
• Participate in Reddit and niche communities. AI engines increasingly treat Reddit as a trust signal for authentic user experience. It is good to have your team members answer questions in relevant threads.
• Perform an AI visibility audit. Evaluate how AI engines perceive and present your brand. You can identify gaps and opportunities to implement in your answer engine optimization strategy.

Step 7: Measure AEO Performance and Its Impact on Pipeline 

The real value of the AEO strategy for SaaS comes from its performance. 

Regularly measure important AEO KPIs and metrics including:

• Visibility KPIs – AI citation rate, brand mentions in LLMs, featured in AI Overviews
• Authority KPIs – Referring domains, contextual backlinks based on your current DR and backlinks built
• Commercial KPIs – Qualified organic leads, demo requests, form completions 

Different AI engines use different sources, retrieval systems, freshness signals, and ranking logic, so citation visibility will naturally vary between them. Being cited in Perplexity but not ChatGPT does not automatically mean something is wrong. It means the engines may be relying on different parts of the web, different source types, or different signals for the same query.

For this matter, Stojan Peposki, SmartClick’s SEO Manager, who is directly involved in tracking AEO performance for our clients’ AEO strategy, explains how to interpret these differences and what matters most when measuring AI visibility. 

“For reporting, we should avoid treating one engine as the single source of truth. Instead, look for patterns across multiple platforms:

• Which engines cite the brand consistently?

• For which topics or query types?

• Which competitors appear more often?

• Which sources are being used to support the answers?

• Is visibility improving over time across the overall AI search?

The main client takeaway is: conflicting citation data is normal. What matters is the broader visibility trend, not whether every AI engine gives the same answer or cites the same sources.”

How SmartClick Builds AEO Strategies for B2B SaaS 

We check whether GPTBot, PerplexityBot, and ClaudeBot actually read your site before we touch your content. From there, our AEO work goes beyond the basics most agencies stop at:

• Fan-out query mapping – we map the three to five sub-queries LLMs silently run behind every buyer prompt, and build content to cover each one, since missing a sub-query is often why a strong page still gets skipped over.
• llms.txt creation – a structured map of your site’s expertise built specifically for AI crawlers, which most agencies still aren’t implementing.
• Citation trigger implementation – we restructure your existing high-value content so pages that already rank get quoted in AI answers, not just read.

None of this happens as a one-off audit. We report weekly, with a monthly KPI dashboard benchmarked against your Day 1 baseline across visibility, authority, and commercial metrics, so you’re never guessing whether the work is translating into pipeline.

Most clients see first AI visibility gains within 30–60 days, with measurable traffic and pipeline impact by the 90–120 day mark. If you want to know exactly where your brand stands in AI visibility, take a look at our AI SEO services and book a free consultation to understand how we can improve your AEO strategy.

FAQs

How is an AEO strategy different from just doing SEO?

SEO helps your pages rank in search results and earn clicks. AEO goes a step further by making your content easy for AI tools to understand, cite, and use in generated answers even when the user never clicks through to your site. Both work together, but they focus on different outcomes. 

How long does an AEO strategy take to show results?

Most teams see initial AI citations within 2–4 months on restructured content. A durable, category-wide presence takes longer, since it depends on content and off-site authority compounding together.

Do I need special schema markup for AI Overviews? 

No, Google has stated there’s no additional technical requirement or special schema for AI Overviews or AI Mode. Standard, well-implemented FAQPage, Product, Organization, and Article schema is what supports AEO.

How to know if my AEO strategy works?

Look beyond traditional rankings. Track whether your content is appearing in AI-generated answers, being cited by AI platforms, and increasing visibility for relevant questions. Over time, monitor these signals alongside your SEO metrics to see whether your content is gaining visibility across both search and AI results. 

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