5 Actually Helpful Tips for Your AEO Strategy

The consumer shopping journey continues to expand—from physical stores to websites to apps—and now increasingly includes AI-powered shopping assistants and agents.

For brands, navigating this new era of agentic commerce can feel like uncharted territory. To help you cut through the noise, we have distilled the top five questions and practical takeaways from our recent webinar, How to Evolve your Digital Shelf Playbooks for AI Commerce.

The expert Profitero+ panel plus guests from The Digital Shelf Institute and Opella unpacked the reality behind the hype of agentic commerce. They discussed the fundamental shifts in the consumer journey and shared exclusive Profitero+ research on the factors influencing recommendations from Amazon’s Rufus, the retailer’s AI-powered shopping assistant. Additionally, they explored how brands can adapt their organizational readiness and digital shelf fundamentals for AI without losing sight of core business goals.

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Question 1: What exactly is agentic commerce, and why is the urgency to adapt so high right now?

Answer: Agentic commerce includes AI agents and AI-powered shopping assistants that help consumers discover, evaluate and purchase products. Today, many of these tools function primarily as assistants—guiding shoppers, answering questions and recommending products. As the technology advances and consumer adoption grows, we expect to see more truly agentic behavior, where these systems can take actions on a shopper’s behalf, blurring the line between assistants and agents. For the purposes of this report, we refer broadly to AI agents because brands need to start preparing for the more autonomous future of commerce.

The urgency to adapt comes down to the sheer speed of consumer adoption and the limitations of choices from LLM recommendations. Currently, 60% of Americans use generative AI tools (Chat GPT, Gemini, Claude) for online shopping (Source: Omnisend study, August 2025, Joe Kaziukėnas, Similar Web, Dec 2025). While a physical store shelf might show a shopper 200 to 500 items, and a digital search page might display 10 to 60 items, an AI agent may only recommend just 7 to 8 options (Source: Digital Shelf Institute).

If your brand isn't optimizing to be one of those top 8 recommendations, you risk disappearing from the consumer's view entirely. 

Learn how to optimize your brand to be one of those top 8 recommendations in our new report Decoding Rufus.

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Question 2: If my product already ranks on page one of traditional search, will AI agents automatically recommend it?

Answer: No. Winning page one does not equal winning AI agents.

While SEO is the foundation of Answer Engine Optimization (AEO), the two algorithms process intent differently. In our new report, Decoding Rufus, we found that only 18% of products on page one of search were actually recommended by Rufus. Furthermore, 36 % of the products Rufus did recommend didn't even appear on page one of traditional search results.

AI agents highly personalize recommendations based on individual shopper context and behavior, meaning brands must optimize for a conversational, intent-based audience, not just a keyword-matching algorithm.

According to our Impact of Winning Search report, brands see an 89% sales lift when moving from page two to a top-five organic position on page one. Optimizing for Rufus starts with mastering the fundamentals of digital shelf optimization that already drive Amazon search success. 

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Question 3: What practical steps actually move the needle to get recommended by retail agents like Rufus?

Answer: AI agents prioritize authority and trust. Research from Profitero+ in the Decoding Rufus report highlighted a few conditional levers you can pull.

Start with conversion fundamentals. Rufus tends to favor products with strong sales velocity—particularly for broad, generic prompts. If your product isn’t converting, content refinements alone won’t elevate it into AI-driven results. You must first execute flawlessly on digital shelf basics: maintain in-stock status, consistently win the Buy Box and protect sales momentum. You should also maximize conversion through promotions, media support, compelling imagery and sampling campaigns.

Second, build trust signals. Top-recommended products consistently feature high review volumes—somewhat unexpected given the emphasis on sales velocity—as well as strong average ratings. Protect your star rating by ensuring your content clearly explains the product and sets accurate expectations, reducing disappointed returns and negative reviews. 

Third, optimize for relevance. Only after conversion and trust are established should you focus on AEO content. Update your product pages to answer natural language prompt questions clearly, making the trade-offs and benefits obvious to the AI. Amazon is a great place to test and learn AI-optimized content because the prompts are easier to research and the results are measurable.

Remember, not all prompts are created equal. Queries range from broad searches (“What’s the best sunscreen?”) to detailed, need-based questions (“What’s the best sunscreen for a 10-year-old boy with fair skin traveling to Hawaii?”). As AI adoption grows, brands must ensure their content is structured, specific and machine-readable so AI systems can accurately interpret, validate and recommend products—making the digital shelf truly SEO- and agent-ready across all query variations.

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Question 4: Reviews are critical for AI trust signals, but what if my brand is heavily regulated and can't easily solicit them?

Answer: If you are in a highly regulated category like consumer healthcare where syndicating reviews is difficult or blocked, our panelists emphasized that the key is not to see that as a dead end—but as a strategic constraint to work within.

Amy recommended  that when traditional reviews aren’t feasible, brands can explore alternative trust signals. For example, more conversational, well-structured FAQs may help provide authentic, AI-readable answers that support visibility in answer engines and LLM-powered tools. She also pointed to other forms of user-generated content, such as UGC-style video, as potential ways to build credibility and social proof where written reviews are limited.

There’s no universal workaround for more regulated verticals, but the takeaway from the discussion was clear: work collaboratively with your retail partners and think creatively about compliant trust-building signals that AI systems can understand and surface.

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Question 5: With so much hype, how should we prioritize our resources so we don't chase shiny objects?

Answer: Agentic commerce should amplify your existing commerce strategy, not replace it. Your North Star KPI, whether that is Category Growth, Sales or Market Share, remains exactly the same.

Instead of treating SEO and AEO as completely separate workstreams, synchronize your SEO and AEO optimizations together. Start by diagnosing and benchmarking your current digital shelf. For low-converting or new products, focus your budget on levers that will boost trial and sales before investing heavily in AI content creation.

And as AI-powered assistants like Rufus continue to reshape how shoppers discover products, understanding how your brand shows up in these new environments isn’t optional—it’s essential. That’s exactly why we created our new report created in collaboration with Mars United Commerce and Publicis Commerce: Decoding Rufus, here.