Search traffic is not dying. Search behavior is mutating.
Users stopped typing two-word queries and scanning ten blue links. They now hand AI a full brief: budget, constraints, priorities, use case. Then they wait for a shortlist with reasoning attached.
This is not browsing. This is outsourced judgment.
The shift happened fast. Research analyzing prompts from five AI-focused subreddits (ChatGPTCoding, ChatGPTPromptGenius, PromptEngineering, and related communities) found a pattern: product decision support dominates user requests.1
People are not asking "What is X?" They are asking "Which X fits my life, and why?"
What Purchase Delegation Actually Looks Like
The prompts reveal four distinct behaviors.
Product evaluation and recommendation. Users provide personal constraints and expect the AI to match products to their situation. Example: "Light running shoes for a marathon below $150." The AI becomes a specialist sales associate who knows the entire inventory.
One prompt goes further: "the perfect gift machine." It instructs the AI to interview the buyer about budget and interests, then calculate an ideal gift. That is a purchase decision framed as a guided interview.
Product selection and comparison. Users want cross-referenced data before committing. One prompt asks for AI that compares retail sites with eBay prices. Another requests extraction of raw specs: dimensions, net weight, whether batteries are included. This is an explicit request for structured product data. A shopping spreadsheet built on demand.
Quality assessment. Users ask AI to ingest reviews and produce a verdict. One person describes asking for a "no shit take" on a product after the AI researches and analyzes reviews. That phrasing matters. It signals what users think AI excels at: cutting through marketing language and summarizing real sentiment.
Agency and automation. Prompts move from advice to action. Users ask AI to scrape new products and deals from e-commerce shops. One sets a rule to check prices before quoting. Another wants to paste a shopping list, name a supermarket, and get a total cost. Another uploads pictures of a weekly flyer and has AI plan meals using sale items.
These are workflows, not queries. They require live data: price, availability, warranty terms. Then arithmetic and planning.
The common thread: AI is the interface to the market. Links are incidental. The output users want is a decision, a rationale, or a transaction.
Why Traditional Product Content Fails This Test
If users ask AI for shortlists and justifications, brochure-style content cannot support the answer.
The AI needs to name tradeoffs and defend recommendations. Marketing fluff provides neither.
Intent coverage must be explicit. When someone asks "best running shoes for sprint" or "lightest marathon shoes under $300," your page needs sections that directly answer those prompts in plain language, with numbers attached.
If AI needs to weigh price versus warranty, your warranty terms cannot be buried in a PDF or hidden behind a contact form. If AI needs to compare comfort versus performance, your content must explain what "comfort" means in your category. Use terms that work as reasons, not vibes.
Comparison material must be scannable. AI systems justify picks using structured data. That means tables, labeled specs, clear statements separating claims from measurements.
When prompts ask for dimensions and net weight, "compact and lightweight" is useless. "Shoe size: 42, Weight: 220g" is extractable and trustworthy.
When prompts ask where warranties "made sense," your coverage cannot stop at "3-year warranty." Define what is covered, what is excluded, what the maintenance plan changes. Use terms that enable comparison.
Content must be agent-ready. If users ask AI to check prices before quoting or scrape deals, AI needs clean, unambiguous data on your site.
Price, availability, shipping, returns, warranty. These need exposure in a way machines can pull without guessing. If values are inconsistent across pages or locked behind interactive elements that do not render cleanly, AI routes around you.
The solution: Schema Markup. Make your brand API-able.
The Trust Battleground Moved Off Your Site
Users now perform "review or analyze this product" prompts. Most LLMs are trained to prefer earned media over brand copy.
Your site is not the main battleground for trust anymore. The battleground is what third-party reviews say, how consistent they are, and whether AI can summarize them into a clear verdict.
If AI is being asked for a "no shit take," it will find harsh criticism as easily as praise.
What This Means for Product Marketers
The switch from SEO to AEO is not a new checklist. It is a consumer behavior change.
People moved from searching to assigning. The AI model becomes the buyer's proxy. Your content becomes a data source and a stack of usable reasons.
Three immediate actions:
Audit extraction posebilities - Can an AI pull your price, specs, warranty, and availability without human interpretation? If not, you are invisible to delegated purchase decisions.
Restructure content around decision support - Every product page needs sections that answer "Why this over alternatives?" with comparable data. Not claims. Measurements.
Monitor earned media - What third-party reviewers say about your product matters more than your product description. If reviews are inconsistent or absent, you lose the trust signal AI needs to recommend you.
The old model: rank for keywords, get clicks, convert traffic.
The new model: feed decision engines with usable facts, earn citations in AI-generated shortlists, win at the moment of delegation.
Your #1 ranking still matters. But only if the AI can use your content to recomend you product or service.
