Optimizing for AI search has almost nothing to do with AI and everything to do with data. The brands that will win the next decade of search are not the ones who get best at prompt engineering for Google’s latest feature, but those who use AI as an internal tool to finally get their own house in order, starting with the goldmine of data they already own. The entire conversation around AI in search is focused on the wrong problem. It’s not about how to rank in an AI-generated answer box. The real issue is that search engines, supercharged by AI, have become ruthlessly efficient at identifying and rewarding the best, most engaging user experience. And most brands are failing that test.
For years, ecommerce stores have survived with messy product catalogs, a surface-level understanding of customer intent, and a content strategy based on chasing keywords. AI didn’t create this problem, it just exposed it. As Steve Zisk argued on The eCom Ops Podcast, this is why we need to shift our thinking from SEO to AI Engine Optimization (AEO). He makes the point that the idea that clean data is the real AI strategy is the most important concept to grasp. It’s not about optimizing for an external engine like Google, but about optimizing your internal business engines that drive customer engagement and sales. Your tech stack, your product data, your customer service logs, your on-site search—these are the engines. And right now, most of them are running on dirty fuel.
The common wisdom is to fight fire with fire, using generative AI to churn out endless blog posts. This is a trap. It leads to a sea of generic, soulless content that AI-powered search engines are specifically designed to devalue. Jeff Oxford has been talking about this on Ecommerce Conversations, explaining that the game has shifted dramatically from link-building to user engagement. AI gives search engines a much more nuanced understanding of whether a user’s query was actually satisfied by a page. Did they bounce back to the search results? Did they spend time on the site? Did they convert? These are the signals that matter now, and a mountain of low-quality, AI-generated content actively works against them.
From SEO to AEO: The Real Work
So where do you start? The work begins with your own data, specifically the insights hidden in your on-site search. On Ecommerce Coffee Break, Arv Natarajan gave the most practical advice I’ve heard on this: treat your on-site search analytics as your most valuable focus group. He points out that brands obsess over Google Analytics but ignore the literal questions customers are typing into their own website. Analyzing your "null search" reports—queries that returned no results—gives you a direct, urgent roadmap for new products, content, and categories. This isn’t about guessing what AI wants; it’s about listening to what your customers are telling you they want and can't find.
Once you know what users are looking for, your next job is to make that information as clear as possible for machines. This is where Structured Data becomes non-negotiable. For years, it was a "nice-to-have" technical task. Now, it's the primary way you spoon-feed AI models the unambiguous facts about your products: price, availability, size, color, and more. Miracle Inameti-Archibong put it perfectly on eCommerce MasterPlan when she said all large language models are "data consumers." Providing clean, organized, structured data makes you the most reliable and trustworthy source for them to consume.
This is also where you can strategically use AI tools, not for generic content, but to solve specific conversion problems. Steve Chou shared on The EcomCrew Ecommerce Podcast how he used AI to analyze thousands of product reviews to create better product descriptions and FAQs, and on The eCommerceFuel Podcast, he talked about using it to power a "vibe" search on his site. This is the correct application of AI: using it to create a genuinely better, more personalized user experience that leads to higher user engagement.
Your First 90 Days
A practical plan doesn't involve a radical, expensive AI overhaul. It’s about building a foundation.
Days 1-30: Audit your data. Take Arv Natarajan’s advice to heart. Export the last 90 days of your on-site search queries. What are the top 50 searches? What are the top 20 that returned zero results? This is your new content and merchandising strategy. Sit with your customer service team and ask for the top 10 questions they get every single day. These are your next FAQ pages and blog posts.
Days 31-60: Content and Cleanup. Pick the top five queries from your "null search" report and write an in-depth piece of content answering each one. Use AI to help with the initial draft if you must, but an expert human must edit it to ensure it has real voice and authority. While this is happening, have your developer implement robust Structured Data for all your products. Ensure every attribute is correct. This is a technical, one-time project with massive long-term leverage.
Days 61-90: Build Authority. Take a lesson from Miracle Inameti-Archibong and Dawid Naude on eCommerce Australia. The concept of "Search Everywhere Optimization" means you need to be where your customers are. Pick one community, whether it’s a subreddit, a Facebook group, or a specific forum, and start providing real value. Don’t sell. Just answer questions with genuine expertise. AI models are increasingly pulling from these conversational platforms to inform their answers. By building brand authority there, you are future-proofing your visibility.
Ultimately, the rise of AI in search doesn't change the fundamentals; it just raises the stakes. Good SEO has always been about a great user experience. As Joe Apfelbaum noted on The eCom Ops Podcast, you need a clear objective before you start using any new tool. Your objective isn’t to "optimize for AI." Your objective is to serve your customer so well that Google—and its AI—can’t help but notice.