Google AI Mode, AI Search, and the Significance of High-Quality Product Data

The New Google AI Mode – Paradigm Shift in Product Search

The internet search as we know it is on the brink of a radical change. With the new AI Mode, Google introduces a system which no longer banks on classic link lists but generative answers – created by Large Language Models (LLMs). Users directly receive comprehensive, context-sensitive information directly on the search results page without the need to visit a website.

This paradigm shift does not only change user behavior but also poses new challenges to both e-commerce companies and brands. This is because the product search, too, is undergoing massive changes. Those who want to stay afloat in the light of AI-assisted product searches must grapple with structured data provision and hyper-personalization. Google AI Mode makes one thing clear: Only high-quality, machine-readable product data stays visible.

In this article, you learn what impact Google AI Mode has on the topic of product search and why product data management advances to the strategic key discipline.

 

Google AI Mode vs. Google AI Overviews – what’s the Difference?

Even if the concepts are often used interchangeably, there is a clear-cut difference between Google AI Mode and Google AI Overview:

• Google AI Mode is the overarching search mode that users can activate (currently only in specific regions such as the US and India). With this mode, you get the full AI-based search experience – with interactive answers, context-related follow-up questions, and extended functions like product comparison, travel and tour planer, or even shopping promotions.
• Google AI Overviews, on the other hand, stands for the visible, AI-generated answer fields displayed directly in the regular Google search results – even without AI Mode being activated. They deliver compact summaries, are based on generative models, and supplement the classic search results.

And how do LLMs such as Gemini and ChatGPT differ from this?

While Google AI Mode and Google AI Overview represent concrete functions within Google Search itself, Gemini (Google) and ChatGPT (OpenAI) are so-called large language models (LLMs) – that is to say, they constitute the technological foundation on which all such functions are based.

• Gemini is Google’s own LLM utilized for a great many applications – among others Google Search, Workspace (Gmail, Docs, etc.), and even AI Mode itself. It delivers the intelligence for generative answers and interactions within the Google ecosystem.
• ChatGPT is the LLM-based dialog system by OpenAI which is also based on its own language model (GPT-4). With the ChatGPT Search Extension, you can also access real-time data from the web and integrate it in answers.

Therefore, the difference lies in the application:
Gemini and ChatGPT are “motors” (models), while the likes of AI Mode, AI Overview, or ChatGPT Chatbot are “automobiles” (user interfaces / functions) into which this motor is built.

 

Google AI Mode vs Classic Search Engines: What Will Change?

The classic times of users inserting term-based search queries into Google, click on results, and tediously work through displayed websites is a thing of the past. Over the last few years, Google has already found a competitor in Amazon when it comes to product searches. In particular when it comes to product searches for making a concrete purchase decisions. At the same time, TikTok has revolutionized the search behavior of generation Z: here, people search for experience reviews, inspirations, and trends – fast, visual, and emotional.

While classic SERPs (Search Engine Result Pages) are a combination of ads and organic links, Google now – that is, since May 2025 regionwide in all of Germany – provides direct contextualized answers (source: Sistrix). This changes the customer journey drastically: it is shorter and much more shaped by AI.

 

Google AI Mode, the New Generation of Search

The actual paradigm change, however, starts now: With Google AI Mode, a new generation of Google Search appears on the scene. Product recommendations are no longer provided by classic SEO rankings or paid advertisement on page 1, but through a smart, AI-generated answer with only a single reply. There no longer are any classic link lists. Instead some well-selected links pop up as part of the answer text – why click on them, though, if you have already been provided with what you have been looking for?

AI Mode has been in use in the US since May and has recently also been rolled out in India. When it will be introduced in Europe or Germany is currently unknown. Possible candidates include a release within this year still or perhaps even an implementation at a later date.

Parallel to the changes in search results, AI assistants such as ChatGPT by OpenAI or Google’s Gemini do also gain in significance. Both technologies are based on Large Language Models (LLMs) that do not only answer search queries but also interpret and contextualize relevant content in order to deliver direct, personalized results – without users having to click actively.

For e-commerce specialists, this means the following: Those who do not follow the trend will lose visibility – not only in Google applications but everywhere where AI systems dominate access to information.

 

This is How Generative AI Changes Google Search – and what this Means for Companies

The integration of generative AI in search systems revolutionizes digital retail – this is not a vision of a distant future but the here and now. The more affinity your target group has for technology, the more you can feel the first changes as early as today. Especially large language models (LLMs) such as ChatGPT and Gemini deeply influence the customer journey. The following examples show in more concrete terms what this means:

Informational Content Loses in Reach

LLMs answer simply questions directly – often times before a user even gets to take a look at the listed websites.

Example: An online shop for household devices which previously benefitted largely from organic traffic thanks to tips-and-tricks articles like ‘5 household solutions against stains’ are now experiencing a collapse in page visits. The reason: Google AI presents the answers directly in the search.

Keyword Data Loses in Meaning

Thanks to AI, search requests become more individual, context-related, and dialog-oriented. Classic keyword tools are currently hitting a wall. In the future, content ought to be centered more around search intentions and thematic relations.

For example, a sporting goods retailer who previously banked on keywords such as ‘running shoes woman’ must now come to the realization: new search requests go like ‘Which running shoes are suitable for marathon training despite knee problems?’ – a highly specific longtail question answered directly by the AI system with personalized recommendations.

AI as the Purchaser: Who will Make the Purchase Decision in the Future?

With the release of AI Mode in the US, Google advances more and more to the position of an active shopping assistant. While users used to do their own research, AI now takes care of this proactively – including the processing of the purchase for the vendor directly via Google Pay.

When AI systems prepare or even make purchase decisions, the supply chain changes down to its very core. Here, however, many questions are still left open!

What will happen with classic cross-selling measures? Today, users browsing online shops see notifications such as ‘Customers also bought…’ or ‘Buy in bulk to save money.’ Will an AI to which the task is given to procure a specific product, however, also take these additional offers into consideration?

Or maybe the development will take the opposite turn – and AI systems may actively ask whether you need something else? Be it Google, OpenAI, or another provider: with increasing integration into various end devices, AI assistants may very well soon be able to make hyper-personalized purchase recommendations that transcend anything today’s product searches are capable of.

And how will all this be affected by legal regulations? The legal perspective – in particular from the point of view of European courts – is still open. One thing, however, is clear: providers such as Google have a strong economic interest. With ad revenues of 265 billion US dollars in the year of 2024 (source: Statista) the stakes are high.

What about monetization of AI searches in the future? Will classic ads continue to be relevant – and if so, where will they be displayed? And how will retailers pay so that the Google AI assistant will make purchases in their shop given that there will be no more costs per click or target CPAs (cost per acquisition)?

The logical answer would be the following: An AI assistant should be geared towards maximizing sales, from Google’s viewpoint. While it takes five clicks to buy a product today, AI can do so in a single click – this is efficient but potentially less profitable for Google. Could a transaction-based payment model be more practical for AI? If so, then AI would turn from a pure buyer to a strategic seller – with the goal of generating more purchases and more sales.

What courts will say about this remains to be seen – this also counts for the question of whether AI systems will even be allowed to make autonomous purchases in the first place and who will be held accountable should legal conflicts arise. Here, one thing is for sure: these questions will come to determine both the legal and ethical frame of AI in e-commerce.

 

Product Data as the Foundation of AI Search

As we have seen: Many questions revolving around the implementation of AI in product searches remain unanswered for the time being. There is one point, however, that is certain as early as today – and it is of utmost importance:

Only those who deliver machine-readable, up-to-date, and high-quality product information will be taken into consideration by AI systems. The quality of product data will decide whether a product can be found, parsed, and consequently recommended.

This is relevant because AI systems such as Gemini or ChatGPT do not function like classic search engines that merely link to websites. They analyze and interpret content semantically – they must understand what the product is about, which properties it has to offer, how it differentiates itself from the competition, and into which use case context it fits.

This can only be successful when product data is…

• complete (e.g., technical details, measures, materials, use case applications),
• structured (e.g., in the form of well-defined attributes, hierarchies, and categories),
• consistent (across all channels),
• and updated regularly (such as for prices and availability).

Without this data foundation, an AI cannot build a valid product understanding – so that the product will simply not be displayed or recommended. On top of that: The higher the data quality, the better the AI system’s hyper-personalization, product recommendations, and automatic content generation will function.

Last but not least, the following applies: Product data is not only the information source but also a strategic asset. Those who neglect it in the world of AI search will not only lose visibility but also their competitive edge in the markets in the long run.

 

How Companies Should Prepare Right Now

We stand before the beginning of a foundational change – and it will soon become reality, sooner than most may expect. With its shopping assistant, Google has delivered a first foretaste, more functions by ChatGPT, Gemini, and others will soon follow. The development is proceeding at a rapid pace.

Now is the time to act. Companies ought to prepare as well as possible for the upcoming changes:

• Observe the current developments revolving around AI and Search – in particular Google, OpenAI, and other relevant players.
• Check your existing product information for completeness, structure, and up-to-date-ness.
• Invest into a modern PIM system, like ATAMYA, to manage data in a central, efficient, and sustainable manner.
• Optimize your product data specifically for AI processing, including semantic structures and relevant attributes.
• Enrich your information as much as possible – from technical specifications to context-relevant descriptions.
• Keep taps on the legal developments on how to handle AI in retail in order to prevent risks well ahead of time and take legal precautions.

Only those who act at an early stage will be visible and stay both competitive and relevant in the AI-driven commerce world.