AI and the future of e-Commerce

 

 

 

In a few short years, AI has evolved from a niche curiosity to an integral part in the everyday lives of businesses and consumers alike. e-Commerce is amongst the sectors most affected by AI, transforming consumer behavior and forcing brands to adapt to the rapidly changing requirements.

 

 

AI-induced changes in consumer behavior

The traditional e-commerce journey for shoppers begins with an internet search for a desired product, which leads to a collection of links to marketplaces, product review sites, brand websites, and other relevant sources. Alternatively, they visit their preferred online marketplace and enter the product name directly into the search bar.

 

However, an increasing number of consumers today type their initial request into a general AI platform such as ChatGPT, Perplexity, or a shopping-oriented large language model (LLM) like a virtual shopping assistant.

 

Unlike the classic collection of generic links, AI platforms produce a curated response hyper-personalized to the user’s inquiry. This level of personalization is enabled by the AI platform’s ability to learn its users' preferences and suggest products that fit their shopping patterns.

 

As Oliver Guimaraes, Managing Director of globaleyez, puts it,

 

 

“In the future, AI will search for products that are perfectly tailored to consumers and only present a small selection to them. This will naturally continue to progress when searches are no longer typed but spoken, and AI breaks them down into a search and “translates” them.”

 

 

The benefits for consumers are significant. This curated reply considerably shortens the decision-making process, provides instant gratification, strengthens purchase decisions, and can even predict future needs, addressing them as they evolve.

 

However, this also means that the classic rules of product discovery no longer apply. In Oliver’s words, 

 

 

“As consumers stop searching and start being guided, visibility will depend less on advertising budget and more on digital integrity.”

 

 

This includes innovative ways to search. Gone are the days of strict keywords typed into a search box.

 

 

“Consumers may never visit the Amazon search bar; instead, they ask AI to ‘find the best wireless headphones under $200,’”

 

 

says Oliver.

 

Today, consumers have a wide variety of tools at their disposal to discover products. Classic visual search tools are now equipped with AI to recognize the product uploaded from a single picture, freeing shoppers from the burden of trying to find the precise words to describe a product they saw in passing.

 

Consumers can now also rely on recommendation engines, another AI-powered tool that analyzes their past purchases and recommends products that suit their shopping patterns. This is an immense asset for marketers as well, because recommendation engines can predict consumers’ future purchases, allowing them to run hyper-personalized campaigns for the right person, at the right time.

 

This high level of personalization enabled by AI increasingly contributes to consumer trust towards a brand, much more so than traditional mass marketing efforts.

 

 

“AI search won’t just decide what we buy—it will decide which brands consumers trust,”

 

 

Oliver adds.

 

And let’s not forget about the power of conversational commerce. Chatbots provide a strong connection between brands and consumers, allowing them to directly connect to each other and create a meaningful, highly personal shopping journey that further strengthens brand awareness and loyalty.

 

All these developments imply that brands have to shift from traditional SEO and keyword-based marketing strategies to contextually relevant, LLM-compatible, and well-structured digital content to stay relevant in the world of AI-powered, algorithm-driven, and hyper-personalized e-Commerce.

 

 

How does AI search work?

Instead of the keyword-based indexing of traditional search engines, AI platforms’ advanced algorithms analyze content, intent, and semantics. Machine learning enables AI search engines to understand the intent behind the consumer’s query, as well as the meaning expressed in digital content. And with the power of an LLM tool, the answer is shaped into a coherent, natural text that reads like a personal reply to the user’s exact question.

 

In the case of e-Commerce, the AI platform gathers information that it deems most relevant based on the exact query and the entire search history of the actual user. For brands to be incorporated in that personal reply, their digital content needs to adhere to the new requirements posed by AI platforms. But what are these requirements?

 

As Oliver explains it,

 

 

“AI assistants are trained to prioritize data integrity—verifiable specifications, consistent descriptions, and positive sentiment across multiple sources. That means brands with strong, authentic, and harmonized product data gain visibility.”

 

 

It goes without saying that brands’ digital content, especially product listings, needs to be

accurate, well-worded and structured, and has to display clean and readable, quality metadata while featuring high-resolution imagery, among other requirements.

 

 

“As AI learns our shopping habits, it also challenges brands to prove what’s real,”

 

 

summarizes Oliver.

 

 

A case study Part I - product search on AI platforms

The best way to demonstrate the status quo of AI product searches, how they compare to traditional searches via a marketplace’s search box, what kind of results they yield, and how those results are presented, is to run test searches on both AI platforms and marketplaces.

 

For a test product search on AI platforms, we selected products that, at the time of writing, were listed as best-sellers on Amazon, the marketplace where the most purchases in the world are made.

 

Please note that search results may vary from user to user, their location (we ran the search in Germany), and also depend on the time of searching. The purpose of our test is mainly to illustrate the process and discover what search results may look like.

 

 

ChatGPT

We chose Stanley Quencher, listed as #2 in Kitchen and Dining on Amazon.com at the time of writing. As a prompt, we chose the basic product keywords and asked ChatGPT for recommendations. Here’s a video documenting our search.

 

A screen recording of our product search for a Stanley Quencher on ChatGPT

 

 

ChatGPT suggested three products: one from Amazon.de, a general marketplace, one from Promoflaschen.de, a specialized marketplace, and the third from the brand’s own website, eu.stanley1913.com. As our prompt was vague, the results yielded a variety of sales channels with different prices.

 

ChatGPT also explains why these specific results were shown to us and gives more details about the products, all taken from the sources and displayed in the main response. When clicking on the links leading to the sources, the AI platform provided a summary of the pages, including product reviews, images, and alternative buying options, all within ChatGPT.

 

To compare these results with a traditional Amazon search, see the second part of this case study below.

 

 

Google AI Mode

For Google AI Mode, we picked SOL DE JANEIRO Hair&Body Perfume Mist, a product listed as #1 on Amazon.com in Perfumes and Fragrances at the time of writing.

 

The 150 ml bottle of this product is sold for $26 on Amazon.com, while the 90 ml version is available for 18€ on a German specialized beauty marketplace, Douglas.de.

 

As a prompt, we decided to be more specific and gave the instruction to look for this product, but under 10 euros. Google AI Mode told us that the original product is not available for such a price, which seemed like a good sign for the protection of IP rights.

 

However, the platform went on to suggest suitable dupe products, complete with availability and buying options. This is definitely alarming, as it proves that the AI promotes dupes as affordable versions of original products, even explaining in no uncertain terms why these products are good alternatives. Such a fact-based, clearly worded response by Google AI Mode is very detrimental to the brand.

 

Screenshot of our Google AI Mode search for Sol de Janeiro Cheirosa 62

 

 

AI as a gatekeeper

As you can see, AI platforms can become like a personalized gatekeeper in the world of e-Commerce, ranking products based on the above criteria as well as the personal shopping history of users. In Oliver’s words:

 

 

“When an AI agent says, 'These are the top three options based on your needs, reviews, and reliability,' that shortlist becomes the de facto storefront.”

 

 

All of this suggests that brands need to shift focus from direct marketplace optimization tactics to catering to the specifics of AI platforms and their decentralized discovery methods. This includes featuring consistent, authentic data across all digital surfaces, like brand websites, marketplaces, social media platforms, and advertisements.

 

Ad providers are already experimenting with AI features to capitalize on the paradigm shift. For example, Google AI Max allows marketers to post keywordless ads, where an AI-powered text customization tool adjusts the ad’s wording to the actual search terms of users. It’s important to note that ads can appear above, below, or even directly within the AI Overview section of Google search results.

 

 

How platforms respond

Brands aren’t the only ones that have to adjust to the new developments. In fact, e-Commerce platforms also need to adapt to discovery happening largely outside of the platform. To see how they fare so far, we ran product searches on Amazon.de.

 

 

A case study Part II - product search on Amazon

When duplicating the Stanley Quencher search we ran on ChatGPT, we received clear and brand-specific results on Amazon. The first image leading the results page was an ad from the brand itself, and the results led us through a series of viable offers.

 

Screenshot of the results page of our Amazon.de search for Stanley Quencher

 

 

However, the results were less satisfactory on our next search for Apple AirTags. It’s ranked as #4 (four-pack) and #6 in Electronics on Amazon.com.

 

In Germany, Apple AirTags are available for ca. 30€ on Amazon.de. Our search revealed that, besides the original, Amazon also listed a lot of products for the keyword “Apple AirTags” that were below that price or were not even marked as produced by Apple. This indicates that sellers of lookalikes and counterfeits use Amazon’s algorithm and flood the marketplace with their subpar offers.

 

 

AirTags ChatGPT

We decided to compare the results to a similar search run on ChatGPT. Our prompt was to find AirTag offers below 20 euros. Instead of coming up with fake and lookalike alternatives, ChatGPT explained that it’s not possible to buy an AirTag for that price. The platform even noted that there are certain risks associated with super low-priced products, including authenticity, region/version, warranty, and condition.

 

This is good news for Apple, and it also hints at a future where the competitive algorithms of Amazon, eBay & Co. don’t matter that much anymore.

 

Fact-based product searches, provided that the AI applies the correct filter against IP-infringing products, can be beneficial for excluding counterfeit, lookalike, and freerider product listings that use brands’ names as keywords to boost their dupe products.

 

Screenshot of our ChatGPT search for AirTags below 20 euros

 

Screenshot of our Amazon search results for Apple Air Tags

 

 

Adapting content

As the focus shifts from platform-based searches to AI-curated results, marketplaces like Amazon and eBay need to adapt their content. Instead of static, bullet point-based pages that dryly list generic advantages of a product, listings will have to include more context and natural language text that addresses individual, personal use cases.

 

As we’ve seen with Google AI Max, technology can also enable the content of product listings to be dynamically adapted to past behavior patterns and shopping preferences of individual users. This ultimately leads to highly personalized product listings catering to the needs of every shopper.

 

 

“The traditional logic of marketplace optimization—clear titles, competitive pricing, and attractive images—is being overtaken by a new requirement: AI readability. In other words, platforms must ensure product pages are intuitively understood and trusted by machine learning models, not just human shoppers,”

 

 

remarks Oliver.

 

Apart from personalization, the content needs to display trust signals and quality data to ensure that product pages are not only readable by humans but by AI as well.

 

This can be achieved by featuring structured authenticity data within product listings, including certified metadata like verified brand IDs, traceable supply chain details, and digital authenticity certificates accessible and verifiable by AI.

 

As online brand protection experts, we find the potential of these machine-readable indicators quite intriguing, because they’re an ideal tool for displaying whether a product or a seller has undergone brand verification. The existence of such indicators could provide a serious boost to the trust ranking of the product listing, positively influencing both its internal ranking and external AI-visibility. In Oliver’s words,

 

 

“AI isn’t just personalizing shopping—it’s redefining how trust is built online.”

 

 

We expect increased emphasis to be placed on various layers of transparency, including trust score, traceable product histories, verified seller identity, and authenticity sourcing. The importance of trust induces a shift from search optimization to credibility optimization.

 

Apart from the actual content, product listings will be evaluated on the transparency and machine-verifiability of their data, which marks a significant change in the management of content and the protection of brands’ digital assets.

 

A welcome “side-effect” of the increased importance of trust is the very real potential of counterfeit listings being sidelined by AI platforms. As they lack the necessary trust signals, these product listings will be less visible to shoppers in AI-generated recommendations.

 

Another positive outcome is the impending convergence of AI-commerce and trust-based ecosystems where verified authenticity is a key differentiator. Social commerce is also affected by this phenomenon, if not more so.

 

With the option to easily search for their social contacts’ experience with a seller or a brand, AI provides yet another boost to the importance of trust.

 

 

“In the next phase of e-Commerce, brands won’t compete for clicks—they’ll compete for algorithmic trust,”

 

 

says Oliver.

 

However, as product discovery expands beyond the borders of marketplaces, the risk of IP-infringing, misrepresentative, and/or manipulative content appearing in the search results grows. After all, as AI platforms draw content from a wide range of websites, a perfectly AI-optimized fake website has a high chance of making it into the curated reply placed before shoppers.

 

This is why a proactive expert-led digital brand protection program is essential to establish brand integrity and enable customers to shop confidently in the new automated buying environments.

 

 

How brands succeed in AI-powered e-Commerce

AI platforms collect their information from sources on the internet, which means that brands aiming to control the information displayed about them need to control the sources. This can be achieved by continuously monitoring product data and imagery across all channels and platforms.

 

Such monitoring leads to the detection of IP-infringing content, including product listings and pictures that need to be swiftly removed from the internet before the AI aggregation engines reach and process them.

 

On the other hand, in accordance with the criteria detailed above, brands have to ensure that their verified product information is clearly formatted, traceable, and machine-readable. This also includes a collaboration with marketplaces hosting authorized listings to ensure that these listings contain authenticity markers indexable by search and generative AI tools.

 

Oliver summarizes it neatly.

 

 

“To remain competitive, brands will need AI-powered protection: monitoring data feeds, detecting imitation content, and ensuring authenticity markers are machine-readable.”

 

 

Conclusion: the rise of an AI trust economy

Instead of keywords, backlinks, and SEO optimized content, product visibility will soon be determined by the validated authenticity and consistency of brand data across all ecosystems.

 

This is the age of the AI trust economy, where the currency is not just product price or brand recognition, but verified authenticity. Consumers guided by AI will be drawn to brands with unquestionable data integrity.

 

Marketplaces wishing to maintain consumer confidence in AI-driven recommendations have to tighten their control over seller verification, supply chain transparency, and anti-counterfeit measures. As for brands, they need to ensure that every representation of their product, whether they’re found on marketplaces, social media platforms, or AI datasets, is authentic, accurate, and protected.

 

Or, in the words of Oliver:

 

 

“In the age of AI-shaped commerce, authenticity isn’t an advantage—it’s a necessity.”