9 Ways to Improve Your AI Visibility in 2026

9 Ways to Improve Your AI Visibility in 2026

Written by Petar Marinkovic

Last updated: September 9, 2026

From SMBs to massive corporations, most companies are now in an unfair position. After spending years optimizing for SEO, they're nowhere to be found in the AI platforms that their buyers use first. According to the 2X AI Visibility Index from April 2026, 96% of B2B companies are effectively invisible during the earliest stages of AI-driven buyer discovery.

If you're in this boat and your brand doesn't appear when someone asks ChatGPT or Perplexity for recommendations in your category, I'll show you a practical playbook for becoming visible, cited, and recommended by AI.

1. Audit your AI visibility

You can't plug holes in AI search without knowing where they are, so a visibility audit is a logical first step.

If you're lucky, your site's authority and SEO efforts have already translated into AI visibility.

Sure, you might show up when someone explicitly uses your name in the prompt. But this doesn't mean they're discovering you — they're just validating you. The point is to show up for early-stage buyer questions to capture that key moment when AI is shaping vendor shortlists.

To do this, create a list of 20–30 prompts that reflect buyer questions without brand mentions. I wrote a whole article dedicated to prompt selection, but the general idea is to aim for specific prompt categories, most importantly:

Category Example
Category discovery prompts “What’s the best CRM for a mid-sized logistics company?”
Comparison prompts “Is [your brand] better than [competitor] for [use case]?”
Persona-specific prompts “What is the cheapest accounting software for freelance graphic designers?”

When you have your list, run each prompt across ChatGPT, Gemini, Perplexity, and Google's AI Overviews and AI mode to see if your brand shows up.

To speed up the process and avoid manually prompting each tool 20–30 times, you can use AI visibility tools like Surfer's AI Tracker, which shows your overall AI Visibility Score and deeper insights into platform-specific performance.

Now, a simple yes-or-no on whether you appear isn't enough. Score each response across four dimensions:

  1. Brand mentions: Do you appear at all?
  2. Average position: Are you the first recommendation or buried at the bottom?
  3. Accuracy: Is the AI describing your product correctly or hallucinating features you discontinued years ago?
  4. Sentiment: Is the AI recommending you enthusiastically, listing you neutrally, or presenting you with caveats?

There's a massive difference between "Brand X is the industry leader for logistics" and "Brand X is an option, though users frequently report challenging onboarding." The first does the promotion for you, while the second may put off a lead. Understand and shape the narrative so that your AI visibility efforts translate into actual sales.

For example, I ran an audit for Surfer's presence when it comes to AEO-related queries to find that we're lagging behind other competitors.

2. Create content for middle and bottom of funnel fan-out queries

Top-of-funnel (ToFu) content like "What is logistics?" is increasingly dead for traffic generation. AI can answer these generic questions directly, so there's no need to cite, mention, or recommend you.

The highest-leverage content now lives in the middle and bottom of the funnel. This includes:

This is where AI might need to actually cite a source or provide recommendations instead of just crunching its training data, and it's also where the purchase intent may be higher.

The good news is that you don't have to dig deep to find the topics you should cover. You just need to understand and use fan-out queries.

A fan-out query is a sub-query that an AI model generates internally while processing a more complex question to provide a detailed answer.

Each prompt can trigger multiple fan-outs — around 6 on average, according to Surfer's research of AI Overviews (AIOs). You'll notice them as specific subtopics covered after an AI tool gives you a direct answer to your question.

For example, when I Googled "How to manage inventory in a retail store," the AIO first gave me a direct answer, and it even directly guided me to a NetSuite video:

In the above image, you can also see that the answer expanded to include inventory management strategies. When I scrolled down, I also saw techniques and best practices:

These are all fan-outs of my main query. And as you can see, each has several citations of reputable articles.

That's what you're aiming for. You want an AI to cite you as a source, or even recommend you in some way, like it outright told me to watch the NetSuite video.

To achieve this, you should write content around fan-out queries. Map your product's value propositions to specific "how to" and comparison queries your buyers ask during evaluation. Then, create comprehensive guides that naturally answer multiple related sub-questions within a single piece, covering the topic cluster instead of a single query.

For example, if I were to write an article targeting "best SEO content optimization tools" — a topic very relevant to Surfer, I would need to cover these query fan-out topics that address competitive benchmarking.

You can even see this in action in Google's AI Overview answers. Google associates competitive entities with this topic.

As you cover relevant query fan-outs, focus on topical depth and cold facts.

Surfer's key facts study of over 57,000 URLs found that the most frequently reused "core" sources cited by Google AI Overviews showed nearly 2x the fact coverage of pages never cited. The likelihood of being cited maxes out at about 12 verifiable key facts per page, which is the sweet spot in terms of the signal-to-noise ratio.

Facts don't always have to be figures and statistics. You can enrich your content with:

All of this makes AI trust your content more and can drastically boost your AI search visibility.

3. Create dedicated pages for use cases and personas

Product pages used to be about keywords. But now, they're about what your solution does and who can benefit from it the most. When someone asks AI, "What's the best [tool] for [specific job/industry]?", the model looks for pages that directly address that intersection.

So if your pages still try to serve everyone, they'll almost always be overlooked in favor of those that clearly match the query's intent.

To make sure this doesn't happen, you need two types of pages:

  1. Use case pages
  2. Persona pages

For use case pages, you need a dedicated one for each goal that your solution can accomplish. All pages should have the same general structure:

Problem > Solution > Outcome

Specifically, each page should :

  1. Name the specific challenge
  2. Explain how your product addresses it
  3. Include concrete results and/or proof points

This way, the page becomes a citation target for use-case-specific AI queries.

Take Surfer's homepage as an example. In the top navigation, you can see the Platform section that clearly explains Surfer's core uses.

So if I ask ChatGPT, "What are the best platforms for finding content topics and ideas?" it will recommend Surfer among other tools.

You'll notice that ChatGPT placed Surfer in the competitor and content gap analysis category, which makes sense as the page uses this as one of Surfer's main capabilities.

This is how your pages can directly shape AI answers to queries related to use cases. And you can do the same with specific personas.

Identify the key characteristics and roles of your target audience (marketing teams, developers, etc.), and create dedicated pages that users will self-identify with. Then, tailor messaging to what each persona cares about. For example:

Besides appealing to each persona's core needs, this will let AI models use role signals to match recommendations to user context.

Let me use Surfer again as an example. In the Solutions tab, you'll see pages dedicated to each target audience.

I used one of the audiences and asked Claude which SEO platform is best for them. It not only recommended Surfer but also placed it first on its list as the best platform overall.

The more use cases your platform has, the more opportunities you'll have to target specific AI queries. This is especially important for companies that systematically build pages across use cases and personas, which can create hundreds of entry points for AI citation.

Take Zapier as an example.

It has thousands of integrations and use cases, with over 50,000 programmatically created pages for each. Each page targets specific "How to connect [app 1] + [app 2]" queries with integration descriptions, supported triggers and actions, and related tutorials, creating thousands of AI-citable entry points.

4. Keep your integrations, features, and pricing pages updated

Even if your brand's AI visibility isn't the best, you probably have users who know about you and will ask questions like "Does [your product] integrate with [tool]?" or "How much does [your product] cost?"

When this happens, answer engines will pull from the relevant commercial pages on your site (features, pricing, integrations, etc.). If these pages are outdated, vague, or poorly structured, AI will either skip you or show inaccurate information — both of which harm your brand and lose deals.

Each page has a structure that works best and helps your brand appear in AI responses.

For feature pages, the point is to organize features into logical categories instead of just having a list. Each feature entry should have:

  1. A concise definition sentence ("What it does")
  2. A use case statement ("Why it matters")
  3. Any specifications or notable limitations

This structure gives AI models easily extractable, quotable blocks with enough context for a precise answer.

As for pricing pages, clarity is key. Showcase comparable tiers with information like:

The tricky part is custom pricing. It's generally recommended to avoid hiding pricing behind "Contact us" because AI models can't extract or cite information that isn't on the page. Still, you may have genuinely custom plans that can't be clearly categorized, in which case you should at least provide a range or a reference point that AI platforms can quote.

Integration pages might require special attention because you need to provide detailed information. List every integration explicitly with:

No matter which data you include, make sure it's structured consistently across integrations. AI tools prefer structured, repeatable formats because they're easier to parse than salesy taglines, so prioritize clarity.

AI platforms care about content recency, so set a quarterly review cadence for these pages. Stale pricing or missing integrations signal outdated pages, which AI engines skip.

5. Audit and optimize your help center docs

Help centers are major AI visibility assets, yet they're overlooked in favor of product pages or blogs. Don't make this mistake, as knowledge bases are inherently structured around Q&A — the exact format AI engines need.

Each help article directly addresses a real user question, which makes it a potential citation source. Collectively, help articles build the topical authority that makes AI models trust your brand across a range of queries.

To do this, each help article needs an AI-friendly structure. This includes:

Each help article should have the right schema. While it's not a direct citation factor, schema markup helps AI platforms understand entities for easier parsing. Depending on the page type, you can use schema like:

Each help article should be structured for easy readability and clarity.

6. Earn brand mentions from the sources AI models rely on

Brand mentions in third-party sources are among the strongest predictors of AI visibility. Plenty of research has shown that they're much more important than backlinks and even a site's domain authority for AEO, which are the pillars of traditional SEO.

Specifically, Ahrefs' study of 75,000 brands found a 0.664 correlation between brand mentions and AI visibility, compared to just 0.218 for backlinks. This signals that you need to place your brand where AI will go looking for information.

This is where you may need to rely on AI search visibility tools because it's hard to know who already mentions you and which sites you should focus on. For example, Surfer's AI Tracker has a dedicated Sources feature that lets you see:

You can expand these insights to find the most important domains and if they mention your brand. You can then reach out to the site owner and ask to be featured.

7. Ensure accurate third-party representation across Reddit, affiliates, and review sites

AI prioritizes authoritative sources through high-quality third-party reviews and consistent brand information. If information about your brand in these sources is inaccurate, outdated, or vague, that's what AI will show potential customers.

This is where it becomes tricky as you have less control over third-party pages. But you can still influence the narrative around your brand by engaging in genuine conversations and approaching threads where AI commonly picks recommendations.

For Reddit specifically, focus on recent posts with context and upvoted responses. Engage in conversations where your brand is referenced naturally without being overly promotional. In this way, you can ensure relevant and accurate representation.

For affiliate sites, work on maintaining the accuracy of content across review platforms and make sure that all information on them is complete and up to date.

8. Structure content using answer capsules, tables, and lists

An answer capsule is a self-contained content block designed to be extracted and cited by AI systems without needing surrounding context. It's crucial to help AI engines pull key information immediately for easier citing.

Answer capsules follow a repeatable structure:

  1. Definition sentence
  2. Purpose statement
  3. Key components in a structured format

Position these capsules strategically throughout your content to enhance clarity and improve the chances of being cited by AI.

9. Measure citation frequency and brand mentions

Tracking LLM visibility requires a major shift. While traditional SEO tools measure impressions, rankings, and organic traffic, a solid AI brand visibility tool should focus on metrics such as:

Besides measuring those figures, perform sentiment analysis to see how AI platforms perceive your brand. A mention can be a strong recommendation, a neutral inclusion, or a warning that your product has downsides that may put off leads.

Turn AI visibility into your next growth channel

AI isn't going anywhere, so measuring your brand's visibility on different platforms isn't a one-off project. It's an ongoing channel that compounds over time. If you invest in visibility now when most of your competitors are still invisible, you can build a massive structural advantage.