Experts estimate that $750 billion in U.S. consumer revenue could flow through AI-powered search by 2028, per a McKinsey report.
Furthermore, half of consumers are already intentionally using AI-powered search regularly.
AI-powered search is quickly becoming the new front door to the internet, 50% of consumers already use it, and $750B in spending will flow through it by 2028. Is your brand ready for gen AI engine optimization? Explore the four plays to win. https://t.co/NaRyGkJsKk
— McKinsey & Company (@McKinsey) November 26, 2025
For product sellers, that changes what visibility can mean.
While traditional search often relied on users typing down keywords, modern shoppers can key in full-length questions, preferences, or use cases and ask AI what they should buy.
Amazon has taken numerous steps to optimize for this change in consumer behavior, most notably with the introduction of Rufus, its AI-powered shopping assistant.
The eCommerce giant says more than 300 million customers used Rufus in 2025, and that shoppers who use the assistant are more than 60% more likely to complete a purchase.
All of this means businesses relying on Amazon need to rethink how they approach their listings.
Alex Stoykov, Founder and CEO of Olifant Digital, works with brands on Amazon growth across PPC, listings, and conversion strategy.
And for him, relying solely on trending keywords simply isn’t enough anymore.
“A seller now has to think about whether the product information actually answers the questions a shopper is likely to ask,” he says.
“That’s why the next stage of Amazon optimization is less about finding new ways to repeat a keyword and more about making the product understandable when the shopper does not use that keyword at all.”
Optimize Listings Around the Questions Shoppers Ask
Instead of focusing only on what keywords a product should rank for, sellers need to consider what questions the product should be able to answer.
After all, that’s exactly what Amazon’s Rufus is designed to do.
For example, if a brand sells running shoes, its Amazon marketing team should take a closer look at what questions a potential buyer would ask:
- Is the running shoe waterproof or water-resistant?
- Is the shoe designed for road running or trail running?
- What kind of structural support does it offer?
The answers to the questions don’t need to be forced into conversational copy.
But the listing needs to be clear and specific enough for an AI system to understand when the product fits a particular shopper's needs.
“What problem is the customer trying to solve? What are they comparing? What could stop them from buying? Those questions can reveal the information a listing needs to provide,” Stoykov adds.
Build Listings for Conversational Discovery
Amazon sellers don’t need to rebuild all their listings, but they do need to tighten the connection between customer needs, product attributes, and the reasons someone would choose one product over another.
Specifically, experts at Olifant Digital advise sellers to:
1. Identify the questions behind the search
Sellers need to understand the purchase intent behind search queries if they want to anticipate the kinds of questions consumers are likely to ask AI.
Sellers can find those signals in places such as:
- Customer reviews, particularly recurring complaints or praise
- Amazon's product questions and answers
- Category research
- Customer service conversations
- Product comparisons and common objections
2. Give products more decision-making context
A strong listing should help answer six basic questions:
- Who is the product for? Define the customer or situation the product is designed to serve.
- What problem does it solve? Explain the practical need rather than relying on broad claims about quality or performance.
- When and how should it be used? Give enough detail to distinguish the intended use from adjacent use cases.
- Which features actually matter? Identify the specifications or attributes that affect the buying decision.
- What do those features mean for the customer? Specifications like material, size, or ingredient matters because of what it allows the customer to do.
- Why choose this product over an alternative? Give shoppers a concrete basis for comparison rather than another round of generic superlatives.
This turns a listing from a bullet list of generic marketing language into a product knowledge source.
“There's a temptation to think AI optimization means writing copy that sounds conversational or inserting more natural-language phrases into a listing,” Stoykov says.
“That's looking at the problem from the wrong direction. The better the product information reflects the actual buying decision, the more useful that information becomes to both the customer and the systems helping them choose.”
That approach is particularly useful in categories where several products can appear relevant to the same search.
If the product information does not explain the distinctions that matter, an AI system has less material to work with when deciding which option fits a particular shopper.
Lessons to take home from Beauty By Earth
Beauty by Earth, an eight-figure beauty brand with more than 100 ASINs and thousands of reviews, came to Olifant Digital with an Amazon advertising operation that was still being managed by a founder.
The key problem was that the account lacked a clear campaign structure, performance tracking across the catalog, and a systematic way to identify products that were worth scaling.
Olifant Digital approached the problem as an account-wide optimization effort:
- Audited the full advertising account for structural gaps and wasted spend
- Restructured campaigns by ASIN, match type, and targeting
- Added keyword, competitor, and category targeting
- Introduced video advertising across priority ASINs
- Built weekly TACoS reporting across the catalog
- Shifted budget toward products demonstrating stronger performance
The result was a 27% increase in revenue within the first 30 days, along with five new ASINs launched and profitability tracking across more than 100 ASINs.
The case matters here because it illustrates what disciplined Amazon optimization actually looks like.
There was no single keyword that unlocked the account. The improvement came from creating a system for understanding products, targeting, advertising performance, and profitability across a large catalog.
Make Amazon Listings AI-Ready
To be clear, sellers shouldn’t abandon the SEO fundamentals that drove their existing Amazon visibility. Those fundamentals still matter.
But as AI takes on more of the shopping journey, Amazon sellers need to improve something that has always mattered but is easy to overlook: how clearly a product explains itself.
And with AI-powered search projected to influence $750 billion in U.S. consumer revenue by 2028, sellers have a reason to prepare before conversational discovery becomes an even larger part of how people shop.
