How to make customer stories discoverable in AI search: Joanna Borkowska on AEO, GEO, and what really matters

At a glance: Making customer stories more discoverable in AI search

• Customer stories are becoming foundational to AEO. Original experiences, attributable quotes, and unique results provide credible evidence that generic content can't replicate.
• SEO, AEO, and GEO work together. Traditional search optimization remains important, while content structure, brand authority, and distribution take on additional significance.
• Make content extractable, not fragmented. Lead with key findings, metrics, and summaries, but preserve the context that gives each piece of information meaning.
• Long-form content still has a place. Detailed stories can provide the depth and substance that make them useful to readers and AI systems alike.
• Know your audience and where they search. Different LLMs retrieve information from different sources, making content distribution an important consideration.
• Keep humans in charge of the story. AI can support the process, but original interviews, editorial judgment, and fact-checking remain essential.

1. What's the difference between SEO, AEO, and GEO?

Host: We're hearing terms like GEO and AEO more and more, but the distinction between them isn't always very clear. How would you define Generative Engine Optimization and Answer Engine Optimization, and where do they overlap? And how do they compare with traditional SEO?

Joanna: There's a lot of confusion around this because there is some overlap. It's similar to what happened when machine learning became mainstream. Everyone was mixing up machine learning, AI, and neural networks.

AEO, or Answer Engine Optimization, refers more to the structure of your content and the technical side of things. It's about how you lay out your thoughts, how you make content extractable, and how you use headings, FAQs, and other elements so AI engines can find your content and provide it as an answer to someone's question. GEO, or Generative Engine Optimization, is more about the PR of AI, I would say. It's about how your brand is perceived, where you strategically place your content, what associations you want to build, and how you establish authority.

SEO vs. AEO vs. GEO: What's the difference?

2. Why are customer stories becoming so important for AEO?

Host: At Wings4U, we work specifically with social proof, where the customer's voice brings a kind of third-party credibility that branded content alone can't always provide. In this new landscape, what role do customer stories play?

Joanna: I would say, and I say it with full responsibility, that customer stories have moved from supporting content to pillar content. They've become one of the most foundational elements of your AEO strategy.

If you're familiar with E-E-A-T, the principles of experience, expertise, authoritativeness, and trustworthiness, they come from traditional SEO. But they're also very relevant in the context of AI because AI systems look for content that provides something unique, something that hasn't been used everywhere else. Real human experiences, numbers that haven't surfaced before, and information that isn't already widely available. And a case study is almost the definition of that. You have something no one else has lived through. You have human voices, original results, and quotes that you can attribute to a specific person.

That's why I would say: invest in customer stories, but approach them a little differently than you did before. And there's another consideration. People still want to read credible evidence. If you're a company evaluating a multimillion-dollar software investment, are you really going to leave that decision entirely to AI? Probably not. You'll still want to read, check, and verify the evidence. That's exactly what customer stories provide.

3. How should teams structure customer stories for better AI discoverability?

Host: If customer stories already contain many of the things AI systems may value, what actually needs to change in the way we create and structure them?

Joanna: First of all, I think the change should be strategic. Creating customer stories can no longer be a retroactive exercise where a marketer needs something for the content pipeline and goes to the product team asking for a case study. It has to be incorporated into your marketing and content strategy from the beginning. You need to think ahead. How can we encourage customers to share reviews? How can we turn their experiences into case studies? Do our contracts allow us to use customer logos, quotes, and results?

Then, when it comes to the actual content, more than ever, you need to rely on those quotes and try to get meaningful numbers. They don't have to be huge revenue figures. They can be timelines, before-and-after comparisons, or other measurable improvements. What matters is that they're unique and based on real experiences.

Structurally, you want to put your most valuable, extractable information as close to the beginning as possible. That might include results, quotes, comparisons, or a technology summary. AI systems don't necessarily process your content from beginning to end. They retrieve relevant sections, often within limits on how much information they can process. So you want your strongest information up front, starting with a clear summary and followed by concrete, descriptive headings.

4. Do different LLMs require different content and distribution strategies?

Host: We tend to talk about AI discoverability as if all LLMs worked in the same way, but that's not necessarily true. How much does the platform itself matter? Should teams think differently about how they position a story depending on whether they want it to appear in ChatGPT, Claude, Perplexity, or another tool?

Joanna: Yes, and I think that's often overlooked. Teams should be thinking differently. Maybe not obsessing about it, but at least being aware that different LLMs and AI engines tend to look at different sources. Some rely heavily on search indexes, while others may draw information from directories, review sites, community platforms, or other sources.

My favorite example is Perplexity. I work with a customer in a very specific niche: network automation and orchestration. They're technical people selling software to other technical people, and they use Perplexity constantly. So that's where their audience is. For that company, it makes sense to think about how to make its case studies visible through the sources Perplexity may reference, including relevant communities and review platforms. That could mean connecting a customer review to a case study or sharing excerpts of the story in industry communities.

And that's also why you should distribute customer stories in different formats and channels. You don't just write a case study, publish one LinkedIn post announcing it, and consider the job done. You can create video snippets, develop several LinkedIn posts covering different aspects of the story, or adapt the content for other relevant platforms. This helps you reach different audiences, strengthen your online presence, and address different questions people might ask AI tools.

Ultimately, you need to know your audience, where they're looking for information, and which sources the tools they use are drawing from. That's another layer of work for marketers, but it's becoming an important part of content distribution.

5. Does long-form content still matter, and what's the difference between extractable and chunkable content?

Host: There's been a lot of discussion about whether long-form content still has a place in this new environment. When it comes to customer stories, is long-form still valuable for AI discoverability? And another term we hear quite often is making content more "extractable" or "chunkable." What does that actually mean in practice?

Joanna: A lot of people assume that because AI systems have limits on how much content they can process, long-form content doesn't work anymore. But that's not necessarily true. I've seen research showing that long-form content can actually perform better in certain AI search contexts. And why might that be? Probably because it provides more substance, more context, and more valuable information. Of course, it depends on the industry and the type of content, but if you need 2,000 words to say something meaningful, and those words contain fresh evidence and a new perspective, don't worry that AI won't pick it up simply because it's long.

What matters is how you organize that information. Focus on your introduction, headings, metadata, and a good, self-contained summary. Make sure the most valuable parts of your story are easy to identify without sacrificing the depth that makes the content useful in the first place.

Now, when it comes to extractable versus chunkable content, there's an important distinction.You want your content to be extractable. You don't necessarily want it to be chopped into pieces. In the early days of AI optimization, I used to create very short paragraphs, lots of headings, bullet points, and tables. And those elements can still be useful. But if you create a list with 100 bullet points, nothing stands out anymore. You lose the context, and context is something AI systems also need.

So find a balance. Use clear headings, shorter paragraphs, and pull quotes when appropriate, but don't break everything apart just to make it suitable for AI. Every section should be self-contained, meaning someone should be able to understand the main idea without having to read everything else. One idea, one paragraph. And honestly, there's nothing particularly new about that. These are basic principles of good content writing.

6. Where should human writers and editors still take the lead?

Host: With so much focus on AI optimization, where should human writers, editors, interviewers, and customer teams still be taking the lead? And how much of the process actually makes sense to hand over to AI tools?

Joanna: I think the human component is what allows you to stand out. Human judgment, creativity, and skill are essential, especially when so much content is becoming repetitive. When you use AI to generate a customer story directly from a transcript, you risk ending up with something that sounds very similar to everything else. I'm talking about the cadence, the flow, the rhythm of the language, and how you structure the story.

And then there's fact-checking. Working extensively with AI, I've seen how easily it can manipulate information or manufacture facts and quotes. So you have to be extremely careful. Personally, I never use AI for drafting or outlines because I find the results too repetitive. But I do use it to adapt tone, brainstorm ideas, or explore possible directions when I'm thinking about a storyline. I also use it for things like spelling or finding synonyms. I treat it as a tool.

At the end of the day, the goal isn't to create content for AI and hope humans will enjoy it. You start with the audience, create a strong story, and then make sure people can find it wherever they choose to look.

About Joanna Borkowska

Joanna Borkowska is a B2B content strategist and writer with over 16 years of experience spanning technical writing, content marketing, and digital strategy. Throughout her career, she's worked with brands including Microsoft, Zendesk, Intellias, Amartus, and Babbel, and has been collaborating with Wings4U for more than eight years.

Today, she helps global marketing teams in tech, SaaS, telecom, and IT outsourcing develop and execute content strategies, with a growing focus on AEO and making brands more discoverable in AI search. Outside of work, you'll find her exploring the jungle, boxing, or keeping her orange cat entertained.

Connect with Joanna on LinkedIn →

Make your customer stories visible in AI search with Wings4U

Customer stories already contain some of the most valuable ingredients for AI discoverability: real experiences, credible evidence, and original insights. But even the strongest stories can go unnoticed if AI systems struggle to find, interpret, or reference them.

That's where Wings4U's AI-Content Optimization comes in. We help B2B companies assess their existing customer content, identify opportunities for better AI visibility, and restructure stories to improve LLM readability without compromising the authenticity of the customer's voice. We also help establish a repeatable approach to keep future stories discoverable as AI search evolves.

Your customers have stories worth telling. Let's make sure AI can find them.

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