The common phrase ‘Google it’ could soon be getting replaced by ‘ask my agent’, as more people begin to migrate over to an AI-only search-engine lifestyle.
With generic results being less fundamentally useful than personalized, contextual responses, traditional search engines are at risk of becoming a secondary research tool.
As more users default to AI platforms for retail experiences, what does the future of e-commerce look like?
In a CX Today interview with Jessica Keehn, CMO of SAP CX, she argues that 2025 would be the final year when most shoppers will default to ‘Google it’ when researching what to buy:
“This last year will be the last year where the majority of people started a search engine.”
Traditional VS AI-Powered Retail Experiences
With the scales tipping further in the direction of AI-assistants, this may lead shoppers to external AI platforms such as ChatGPT or Claude, or simply using the built-in AI assistants within retail websites.
Traditional Retail Search Experiences
These search engines rely on keyword matching, treating each customer query as a separate event and expecting the user to know exactly what to type to get its desired results.
As a result, incomplete, vague, or contextless searches may return with limited or irrelevant results to the customer.
And whilst it does heavily support valuable product discovery through structured input, results are typically ranked by popularity, global or national relevancy, or recency of when it was released, meaning results may end up untailored to a customer’s shopping needs.
AI-Powered Retail Search Experiences
AI-powered search engines work differently, using natural language processing to understand customer intent, context, and behavior without needing excessive and accurate context.
The system interprets what the shopper is trying to find from the information given and adjusts results accordingly, tailoring results based on previous history, location, behavior or preferences.
Functioning more like digital shopping assistants, they guide discovery, recommend alternatives, and help customers refine choices to reduce friction, improve relevance, and support faster decision-making.
“Product discovery is moving from search engines to AI recommendations,” Keehn continues.
“When you’re shopping with an agent, that agent actually knows you and your preferences, your result will be different than mine.”
The Rise of AI Search Engines
The development of AI search engines in the retail infustry has only really gained momentum in the last few years; however, its expanded capabilities have been one driver in this increased popularity, no longer experimental but fully functional for everyday shopping.
One of the most prized advantages of AI search engines is their ability to drive personalized results quickly, a capability that traditional search platforms such as Google have historically led in.
In the early 2010s, Google had begun introducing personalization into the search engine, incorporating machine learning into ranking systems.
This led to the launch of RankBrain, one of Google’s first major AI-based search components, which helps users interpret unfamiliar queries and better understand intent.
Similarly around the same time, computer developers such as Apple had began introducing the early versions of voice assistants, such as Siri, increasing the popularity of conversational queries of keyword indexing.
This pushed forward natural language, and researching began to get faster.
As search engines continued to improve natural language understanding, interpreting context became more common that matching isolated keywords.
Towards the end of the decade, Google introduced the capability BERT into its search engine, significantly improving its ability to understand user context without needing excessive information or complete queries.
By 2022, generative AI search became more accessible to users after the release of LLMs, with OpenAI launching the earliest versions of ChatGPT, demonstrating basic conversational, context-aware responses instead of lists and links.
“Large language models have now sort of taken over the shopping experience,” highlights Keehn.
“Large language models are becoming a legitimate shopping channel.”
Despite its capabilities, traditional search engines continued to dominate the retail market, due to its familiarity and trust with customers over the past few decades.
However, to remain ahead of the game, search engines such as Bing began introducing AI into their search engines, combining both familiarity and automated summaries into search results.
In retail, many brands have begun introducing their own AI capabilities within their sites for customers to use, allowing them to research and discover new products without consulting traditional search engines first.
Now having had a four-year presence within modern-day research, AI search has evolved gradually from algorithmic ranking to machine learning-driven interpretation, then to generative, conversational systems.
Given the accessibility of AI today, it is plausible that users will see its eventual takeover of traditional search engines.
Will 2026 Be the Year That Shoppers Ditch Traditional Search Engines
SAP experts claim that 2025 could have been the final year that the majority of consumers used search engines as the default starting point for shopping research.
AI tools are being increasingly embedded within platforms to provide direct answers, summaries, and recommendations, with users being able to ask complex, natural questions with synthesized responses.
Even without AI, the past decade has seen an increase in younger users defaulting to social media apps such as Instagram to get product recommendations and reviews instead of search engines.
Furthermore, predictive systems have reduced the need for customers to search at all, as algorithms recommend content, products, and services based on previous searching behavior, with discovery happening passively through feeds and suggestions.
Keehn argues that there is a dramatic transformation in the consumer behavior, pointing out the decline in traditional search relevance.
“This paradigm shift is not small. It is so significant,” she explains.
“Search engines will go away or become so much less useful for shopping experiences.”
Whilst SAP doesn’t yet expect AI to take over traditional search engines completely in retail, the dominant entry point to the internet may shift from manual keyword search toward AI-driven, conversational, or recommendation-based interfaces.
Why This Might Be True
According to SAP’s AI in Retail Global 2025 Report, many consumers are already using AI in shopping-related tasks that have arguably been the role of traditional search engines, as 55% of respondents say that AI makes shopping easier.
Furthermore, 34% of respondents are using it to search for products to buy, cutting out traditional search engines altogether and going straight to AI.
Outside of AI, many users are beginning their searches within platforms such as Instagram or Amazon for product research and discovery, fragmenting the traditional search entry point.
The starting point for the customer journey is clearly shifting; by slowly replacing search engines jobs like product discovery and deal-finding, AI is switching the output users are clearly craving, a personalized recommendation or shortlist.
Why This Might Not Be True
Traditional search engines like Google are still deeply embedded in daily behavior, generating billions of searches every day.
With its long-lasting presence within our shopping journeys, habit, default browser settings, and mobile integration reinforce its continued use.
These traditional services can also provide multiple sources, allowing users to compare websites, product reviews, and prices.
And with advertisements recently being introduced into popular AI platforms such as ChatGPT, these answers may raise concerns over bias, accuracy, or lack of source visibility.
Retail businesses are also still depending on sites such as Google Analytics to review their search visibility, as this method continues to support a large digital economy.
Furthermore, traditional search engines are now beginning to incorporate AI features into their rankings, summaries, and shopping tools, allowing users to continue beginning with search engines if the experience is both conversational and similar to older versions of researching.
This outcome depends on how quickly AI interfaces replace habitual search patterns rather than simply enhancing them.
What Is The Retail and CX Landscape in an AI-Driven Search Era
If most brands stop beginning journeys with traditional search engines, the impact on customer experience and retail may be structural rather than incremental.
If brands began shifting their efforts primarily toward AI search platforms, customers would have to rely more on AI assistants, embedded platform search, and recommendation systems for their shopping needs.
To keep up with this change in expectations, Keehn urges brands to adapt to agent-driven commerce.
“Retailers have to be thinking about future proofing,” she argues.
“[Brands must] ensure that your retail commerce strategy includes capabilities that ensure that you continue to show up… now also through ChatGPT and Google AI.”
Discovery would then shift from typing keywords into a browser to asking conversational AI systems for suggestions, meaning CX would need to focus on how well brands surface inside AI-generated responses, rather than on ranking in Google search results.
Fewer website links would mean retailers will need to compete to be mentioned in those summarized outputs, with visibility depending on structured data quality, reputation signals, API integrations, and real-time inventory feeds.
Customer expectations would also noticeably increase, with AI-driven entry points being context aware, customers would be expecting recommendations tailored to individual preferences, research history, and budget.
Keehn adds:
“When you have all of that at your fingertips, the data that’s accurate, actionable, and insightful, you can actually go ahead then and build that retail intelligence application.”
As a result, generic product listings will be less effective, meaning customer-focused teams will need to use strong customer data integration and real-time personalization capabilities to meet customer demands.
Conversational AI can ensure reduced friction in the buying journey, guiding customers from discovery to checkout within one interface, shortening decision cycle and reduce drop-off points.
To ensure competitiveness within AI search engines, brands will need to create stronger brand identities to avoid by standardized in recommendations, offering exclusive products and services, loyalty programs, and direct customer relationships to ensure visibility and customer preference.
Once AI becomes the primary starting point for customer journeys, transparency and reliability will become critical, meaning retailers will be required to adopt clean data practices, ensure accurate product metadata, and deliver consistent fulfillment performance to remain a trusted brand within AI systems.
According to Keehn, that “depth of loyalty is going to continue to be super important.”
CX would likely shift retail practices toward more predictive, conversational, and platform-integrated commerce.
Whilst customer reports cannot currently confirm the outcome of traditional search engines, the increase in AI search engine activity for retail and product discovery is strikingly noticeable, meaning brands will have to adapt to this new era of shopping online to remain competitive.
This shift doesn’t mean that research is ending; it is simply changing to fit this new age of discovery, where customers are beginning to expect personal, contextual, and actionable responses to their shopping queries, that which traditional research cannot provide.
