I’ve worked in eCommerce SEO for over 5 years, and during this time I’ve seen a lot of changes!

It’s fair to say that our approach to category pages, or product listings pages (PLPs), has evolved over the years, from keyword-stuffing in the early days of search(!), to today’s intent-driven landing pages, which need to:

• Satisfy the humans who visit our site
• Rank well organically for relevant keywords in search
• Be featured and cited in the answers various AI search interfaces provide (e.g. Google’s AI Overviews, and tools like ChatGPT)

It’s quite the juggling act, huh?

In this article I’ll be focusing specifically on how to optimise category pages (or PLPs) for AI visibility. To do this, I will provide the framework and checklist that I use to future-proof category pages for AI Search and beyond.

How AI Search Differs from “Traditional” Search

Traditionally, SEOs focused on ranking specific pages for specific keywords, with the goal of appearing as close to 1st place in Google’s organic listings as possible.

Now, with AI systems such as AI Overviews and ChatGPT, results look different. Instead of a simple ranked list of links, AI systems generate conversational answers that summarise the information and reference/cite sources. While both traditional search and AI search rely on understanding user intent and context through natural language processing, the core difference is in how that information is delivered to the user:

Alt text: Google organic search results for the query 'best womens running shoes'.

Alt text: ChatGPT screenshot for the query 'best womens running shoes'.

This shift in presentation has affected category pages, as AI systems don’t simply extract keywords, they interpret entities, structure, clarity and topical relevance. This means that category pages can’t simply be product listing pages, they now need to serve as informational, navigational and entity hubs that help both AI systems and users understand their relevance. Essentially, thanks to these AI systems we have new opportunities to reach potential customers – I have seen numerous examples where well-optimised category pages are cited in AI summaries, even though they don’t rank first organically.

Understanding User Intent

SEOs have been identifying user intent and optimising for the big 4: informational, navigational, transactional, and commercial for years now. However, the process of identifying user intent has become increasingly complex.

Let’s take the query ‘best hiking boots’ as an example. In the past we may have viewed this as an informational query, and expected to see links to reviews, comparisons, and guides. However in today’s search landscape we are seeing hybrid results, with product recommendations, guides and FAQs surfaced together:

Alt text: Google search results for 'best hiking boots'

Alt text: ChatGPT response to query 'best hiking boots'

But traditional keyword research and search intent isn’t completely defunct for category pages, it just needs to be adapted for the AI era. I still tend to start with the original targeted keywords for a category page, but I expand them with prefixes such as ‘best’, ‘types’, ‘ideas’, ‘buy’, ‘compare’ etc - in order to capture other types of intent.

For example, if you are optimising a category page for ‘hiking boots’ you would:

1. Start with your base target keyword ‘hiking boots’
2. Add prefixes such as best hiking boots, buy hiking boots, types of hiking boots, compare hiking boots
3. Analyse these results to see what content appears for each of the modified queries. If it's mostly comparison tables and buying guides alongside product listings, the intent is informational + transactional. If the results are mostly reviews or guides, the intent is more informational. This gives you the framework to work from when looking at what type of content to create for the category pages.

If you have the resources, you can automate this process by using OpenAI’s API in Excel, to generate a list of prefix modified queries from your base keyword, then summarise the results that appear. This gives you a data-driven way to optimise your category pages for AI search alongside traditional search.

Practical Tip: Instead of writing category content for keywords, as we have been doing for years, write for questions and context that AI might need to answer.

5 Steps to Make Category Pages AI Ready

I have a 5 step process for re-writing category pages:

Step 1: Audit the existing page

As with traditional optimisation, you should audit the current page as it stands.

Ask yourself:

• Does the page fit well in the current site structure?
• Are there any content gaps between your page and your competitors' pages?
• How valuable or useful is this page to users? (I tend to look at engagement metrics to better understand this)
• What intent is the page currently serving, and what intents and entities are missing?

Step 2: Re-structure for intent

Add an introduction or above-the-fold content that establishes context, e.g. what the category is and who it is for. Then add in subsections of content for different user intents, such as styles, sizes or uses. Where relevant, adding FAQ-style content is incredibly valuable for AI search. I’ve seen a lot of success with category-level content being surfaced in AI Overviews from FAQs.

Step 3: Add entities & structured data

Entities are things that AI can clearly recognise and understand, such as types, finishes, materials and brands. Adding these entities helps AI to connect your category page to the types of queries users enter, and pull them into summaries.

For example, a women’s jackets page shouldn’t just say “We sell a wide range of women’s jackets.” It should instead mention the different types of jackets e.g. parkas, blazers, bombers, puffers, trench coats, etc; the materials e.g. wool, leather, suede; and brands that users might be looking for.

Adding structured data is another way of explicitly telling both search engines and AI systems what's on the page, giving more clarity, and context. For category pages, useful schema may include: ItemList to describe the list of products in the category, Product for featured items, FAQPage if you add in this type of content, and BreadcrumbList to clarify your site’s hierarchy.

Step 4: Enhance for User Experience

Now you have the content, you can tweak it to ensure it is scannable and valuable, not just sales-y. While we are optimising for AI search, we still need to remember to be human-first.

Ensuring there is clear product categorisation, possibly by including sub-groups of “by material”, “by style”, etc, to help reduce the endless scroll. Using images and graphics to highlight features or comparisons helps improve user engagement, and sends signals that the page is well-structured.

Lets not forget internal linking! Adding links to related categories and/or supporting content such as buyers guides or blog posts helps users, and gives further context about your topical authority.

Remember: pages that are easy to read and navigate tend to perform better.

Step 5: Making Content Accessible to AI Systems

Your content won’t be surfaced if AI can’t access it. It is always worth checking that your site setup isn’t unintentionally blocking AI discovery. Start with robots.txt to ensure you’re not blocking AI crawlers such as ChatGPT bot from crawling. The same applies for firewalls and bot management solutions, which sometimes block AI crawlers by default.

Rendering is also a common issue, if your content only appears after client-side JavaScript execution, some AI systems can’t process it, which renders it invisible to them.

Lastly, make sure you avoid using a “nosnippet” rule via meta robots, as this stops your content from being pulled into AI outputs such as Google’s AI Overviews and AI Mode.

Before & After Category Page Example

Below I’ve included an example of a rewritten category page.

Above the Fold: Intro Copy

Before (weak intro content)

We stock a wide range of door handles to suit any home. Browse our collection today and find the perfect door handle.

After (strong intro content)

Our door handle range includes lever handles, door knobs and pull handles in finishes such as brass, stainless steel, and iron. Whether you’re updating an interior door, fitting a fire door or finishing a commercial unit, you will find durable options from trusted brands such as Carlisle Brass and From the Anvil. Explore styles to suit both traditional and modern interiors, with matching hardware available across our collections.

Why this works: 

• It includes entities such as product types, finishes and brands
• It covers the transactional intent “buy door handles” as well as informational “what door handle finishes exist” and some use cases
• It gives detail to the human that aids decision making, and is structured well for AI to lift into a summary

Below the Fold : Expanded content

This is where I would add detail for multiple intents. AI systems often cite FAQ style content that is deeper down the page.

A weak version would just be a long list of products with image thumbnails.

A stronger version would:

• Add subsections for intent:
  • By style: modern, traditional, minimalist.
  • By material: brass, stainless steel, matte black.
  • By use case: interior doors, exterior doors, cabinet handles.
• Include an FAQ section including content that AI often references such as:
  • ‘What’s the difference between door knobs and handles?’
  • ‘Which style of door handle works best for modern interiors?’
  • ‘How do I choose the right handle for my door type?’
• Have internal links to buying guide content that expands on the topic and supports conversions such as:
  • ‘How to choose the right door handle for your home’
  • ‘Door handles installation tips for beginners’.

This turns the product listing page into a multi-intent, AI friendly category page that serves both informational and transactional needs.

Why this works:

• It improves UX as users can filter products quickly
• The FAQs and subsections provide structured content that supports both SEO and increases visibility within AI results
• The internal links to guides boost topical authority

Once your content is live and structured, the next question is how to measure performance.

Measuring Success

Traditionally in SEO we have looked at metrics like keyword rankings, and organic search traffic. In addition to this, we use engagement metrics like click-through rate, time on page, and internal clicks as an indicator of how valuable the page really is. (If users are spending more time or exploring further, it’s a strong sign the page is relevant, well-structured, and meeting intent.) Plus, of course, seeing the role the page plays in the path to conversion is also a strong signal, (e.g. when users move from the category to a product page, and/or add products to their cart).

In terms of tools, I like to use Google Search Console to track impressions, queries and CTR, and GA4 to track engagement and conversion. While these metrics measure organic search performance, not AI-specific tracking, they still remain critical for understanding how your pages are performing.

The graph below is taken from Google Search Console. It shows the increase in organic impressions following our PLP optimisation.

Alt text: Google Search Console impressions report showing the increase in organic search impressions for PLPs.

While these metrics remain important, with the rise of AI search there’s a growing need to supplement these traditional metrics with AI visibility. This means tracking how often your content is being cited or referenced in AI summaries, overviews or answers; and how much traffic is being referred from these sources.

I use regex in GA4 to track AI referral traffic, (check out this reddit thread for more information on how to do this).

Tracking AI visibility is a little trickier. There are a range of tools out there, for example, SE Ranking has an AI visibility tracker to monitor citations in Google AI Overviews, Ahrefs has Brand Radar tracker, plus there are platforms such as Profound, ZipTie and Otterly. These tools are undoubtedly useful, but can be expensive.

If, like me, you don’t have access to these tools, you can still get an indication of how visible your content is via manual testing. This involves running key queries in platforms such as ChatGPT, then seeing if the content is cited in AI generated summaries. I like to note which pages are constantly surfaced, even if they don’t rank number 1 in organic search.

Common Mistakes & How to Avoid Them

There are a few mistakes that I see consistently on new clients’ category pages, some I have been guilty of in the past too!

Treating category content as filler is a sure-fire way to suppress your visibility not only in AI search but traditional search too. Make sure your content is descriptive and genuinely useful. Over-optimising for keywords is also something I see regularly, while this technique has worked in the past, it won't give you the results it once did, and you should instead focus on optimising for multiple intents.

Structured data is often overlooked, too. Schema helps AI systems and search engines understand what your pages are about, making them more likely to be cited. Accessibility is also an afterthought on many category pages, I see small text, bad colour contrast and missing alt text all the time. This can not only impact users, but both search engine and AI visibility too.

Checklist for Future-Proofing Category Pages

When working on category page refreshes, I like to use the following checklist to ensure they’re set up appropriately for humans, search engines, and AI systems:

• Intent mapping - identify what the user is looking for, and answer those needs in your copy
• Entities - ensure your category page mentions the key brands, materials, types and use cases that both humans and AI would expect to see
• Structured data - add schema such as ItemList, Product,FAQ and Breadcrumb so that AI can easily interpret your content
• Clear, helpful copy - ask yourself if your copy could make sense if lifted into an AI summary - is it scannable, descriptive and valuable?
• User-first design - have an easy to navigate, logical layout with sub-sections and internal links to related content
• Technical Accessibility - ensure AI crawlers can access your content by allowing them in robots.txt and reducing reliance on client-side rendering
• AI testing - check how your page appears in AI search

Conclusion

To stay ahead of the game, we need to optimise for humans, search engines, and AI systems.

I believe that refreshing your category pages is one of the most impactful ways to drive visibility in AI search. By focusing on search intent, clarity, entities, and structured data you can future-proof your category pages as the way people search continues to evolve.