Product Led AEO: The playbook
Follow this playbook to extend your Product-Led SEO to an LLM interface
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If you have been regularly reading my newsletters over the last year, you will know that I firmly believe the SEO discipline is strategic, not tactical, and that, as a channel, it covers AI visibility under its umbrella, just like mobile, IOT, and wearables. With this disclaimer aside, many executives and investors have not been receptive to this message and believe that there needs to be a whole new playbook for AI visibility.
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No matter how many times I stress to these leaders that the existing playbooks can be adapted to new realities without throwing out the baby with the bathwater, there is still demand for a brand-new AI playbook. With this disclaimer aside, this post will discuss tactics for AI visibility, and I am admittedly repurposing some of the same points that would be showcased in a Product-Led SEO-only post. There are, of course, nuances that apply to AEO/GEO/AIO/FOMO, because SEO today focuses solely on search results. At the same time, AI visibility is a different UI.
Product-Led SEO still reigns
Regardless of what you want to call it, a Product-Led SEO lens would still apply because at the end of that AI search is a user, just like with all SEO. Most AI playbooks today focus solely on being visible in LLMs, but that is not enough. For it to matter, you need to go one step further and determine WHY you should be visible. When someone asks an AI assistant or search engine a question, your links within the response should provide the solution they seek.
Take a brand like ADP (the payroll platform). When someone searches for payroll platforms, they will undoubtedly see ADP listed among the top companies mentioned - not because of their SEO, but because of their brand efforts over many years. (Some of you probably don’t have the most positive views of the brand, but it’s a brand regardless of sentiment.) Yet, this AI visibility doesn’t necessarily matter if it doesn’t prompt the searcher, when the searcher is the ADP ICP, to take an additional action to get in touch with sales.
This is where Product-Led AEO would be effective. The marketers focused on AI visibility would take a step back to determine the prompts that users in-market would search for, AND what they would expect to see when they did so.
Future-proofing by aligning with the future
This is also somewhat future-proofed, as evidenced by ChatGPT’s foray into ecommerce. The ultimate goal of LLMs is not just to serve as robot-written content but also to serve as a gateway to the next step. When you optimize for visibility in that gateway, you are aligned with the user and the engine.
It’s not just ChatGPT that is making this end-user leap; Google has been trending in the same direction of providing solutions rather than links with featured snippets and knowledge panels. See the original results for “top payroll platforms” before the AI Overview.
The mechanics of Product-Led AEO require rethinking your entire SEO strategies, just like Product-Led SEO did if you were not already aligned with end-user needs.
Additionally, some best practices become more relevant with AI visibility. Data structure matters because answer engines need clean, parseable information. Structured data and schema markup remain helpful, but the focus shifts. Instead of just marking up a blog post about payroll, you structure data about your product’s (Product in this newsletter is your search attracting asset) actual capabilities, features, and pricing. This machine-readable information MAY help AI systems understand when and how to suggest your product.
Theory not data
I need to be transparent here: most of what I’m recommending is informed theory rather than proven strategy with concrete case studies and metrics. It’s almost impossible to A/B test with AI visibility right now, which means we’re working from logical inference and early signals rather than the kind of data-driven playbooks SEO should be. For now, AI prompt tracking is a bit of the wild west, given that what you see as your prompt ranking can often not be replicated in a manual search.
I could be wrong about specific tactics, and I hope that if you disagree, you can share why in the comments or in a message. I remain firmly convinced that within all activities targeting search users, the core principle remains sound because it’s based on decades of search behavior: solve real user problems, and the systems that connect users to solutions will find you. The interface changes, the algorithms evolve, but user intent doesn’t. That’s why I’m confident in the strategic direction even while acknowledging uncertainty about individual tactics.
API’s are important for an LLM world
API accessibility becomes critical in this AI-dominant world. If your product has an API that answer engines can actually query or integrate with, you’re creating a direct pathway for AI systems to use your solution in real-time. This goes beyond mere mention to actually powering the answer itself. The difference between being cited as a resource and being the engine that delivers the solution is substantial. When an LLM can call your API directly to fulfill a user request, you become infrastructure rather than just a reference.
Be fresh
Real-time data feeds and freshness signals matter more than ever with AI. LLMs need up-to-date information to remain relevant. If your product provides live data (stock prices, weather, availability, pricing), you become a more valuable source. Stale information gets deprioritized quickly, or it will when the system works as planned. This is especially true in industries where timeliness is everything: finance, travel, inventory, and breaking news. Your product’s ability to serve fresh data on demand can be the deciding factor in whether an LLM chooses you over a competitor.
User alignment
Most importantly, the product’s actual functionality needs to align with common user queries and problems. User intent mapping becomes more precise with Product-Led AEO. You’re not just targeting keywords but specific jobs to be done. Someone searching for “Euro to USD exchange” may be looking for information or to transfer money. If your product can solve that immediate need and you’ve optimized for that specific use case, you become the answer.
Think conversational
Conversational context optimization is a new consideration that traditional SEO never had to address. Unlike conventional search, where each query stands alone, AI conversations have memory and flow. Your product needs to work within dialogues, not just single questions. This means thinking about how your solution fits into the broader conversation and not just one search result. A user might start by asking about retirement planning, then drill into investment options, then need a life insurance calculator, then want to see a graph. Your product needs to serve all stages of that conversation and journey, not just the initial query.
Tools for traffic
Creating free, functional tools is one practical implementation of Product-Led AEO. These tools solve specific problems people are looking for, demonstrating your product’s value. A currency converter, a payroll calculator, a retirement modeling tool. These aren’t just keyword magnets but actual solutions that answer engines can point to when users have related queries. Remember, it’s not just about giving it away; it has to drive ROI for this to matter as a search effort. Rankings on a calculator can drive a lot of the wrong users.
Remember the feedback
AI results are more spam-resistant because the feedback loop is more direct. If you are not actually helping users but using the latest loophole to get cited in AI, this hack will close. The role of user feedback loops in training these systems cannot be overstated. When people accept or reject AI suggestions of your product, that signal matters. If users consistently dismiss your product when suggested, the AI learns. If they engage and complete their task successfully, that teaches the system to recommend you more often.
Citations
Links matter, but it’s not just domain authority or social posts. The citations have to be meaningful connections (context matters). Reddit is an excellent platform for promoting your brand, but it has to be authentic, not just a spam post. When it comes to citations, I believe quantity matters more than quality. It’s not that you are ignoring quality, but you need a lot more shots on goal because you never know what will drive the answers.
Competition
In traditional SEO, you compete mainly on content quality and backlinks. In Product-Led AEO, you compete on actual utility and integration quality. The best answer wins not because it’s written better, but because it solves the problem better. This potentially levels the playing field for smaller companies with superior products; a feature of AI is the diversity of results. Competitive displacement strategies also evolve. In traditional SEO, you might target competitor keywords, while with AI, you need to demonstrate why your product is the better solution. You need clear ways to show that your solution is faster, cheaper, more accurate, or better than alternatives.
Content
Content still plays a role in this new SEO, but it shifts from being the primary optimization target to supporting a journey. Documentation, onboarding flows, and guides help both humans and AI systems understand your product’s capabilities. Clear, direct content makes it easier for answer engines to understand what your product does accurately and when it’s appropriate to suggest it.
Multimodal
As AI systems handle images, voice, and video, your product representation needs to work across these formats. Think about how your product and other content appear in image results, how it sounds when described by a voice assistant powered by ChatGPT or Gemini, and how it translates across different input and output modes. The same product might be discovered through a spoken question, a Google Lens picture, a flick of a wearable, or a text query, and you need to be optimized for all of them.
Local
AI assistants know the user context that traditional search didn’t. Geographic location, past behavior, and when Google launches it: super personalization. Your Product-Led AEO should account for these personalization factors rather than assume a one-size-fits-all approach to visibility. Someone asking about travel tips in March while located in New York has different needs than someone asking in July from Florida. Your product eeds to reflect these variations so AI systems can make appropriate, contextual recommendations.
Measurement
A key component of AI visibility is simple awareness, even without a click, so this means measurement and KPIs have to evolve. Instead of tracking page views and rankings, you monitor product usage driven by AI referrals, API calls from answer engines, and completion rates for tasks initiated through search queries. Success means your product is actually being used to solve problems, not just being mentioned. You can monitor prompts, but there's no need to go too deep in building your prompt list. Only track the prompts that you know your real users would search.
Team building
Just as Product-Led SEO requires collaboration among product, engineering, and marketing teams in ways traditional SEO never did, Product-Led AEO does too. You’re not optimizing for a search engine; you're optimizing a product itself. Engineers need to build with the AI visibility searcher in mind, product managers need to prioritize features that align with common prompts, and marketers need to ensure the product is discoverable and understandable to both humans and machines.
Keep the focus on the user
Product-led AEO is really only effective if you have operationalized the key component of Product-Led SEO: users are primary, the engines are secondary. If user empathy and buyer journeys are your north star, you are in the right place to extend your SEO efforts to LLMs' additional user interfaces. Regardless of whether LLMs are the future or a fad, your SEO playbooks should be guided by SEO and AEO best practices that help you be visible wherever your users are.
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