The worlds of commerce and AI are becoming increasingly intertwined, and consumer behaviour is following. Retail giants are introducing more technology to enable shoppers to find and buy products directly through AI platforms like ChatGPT or Copilot.
Demand itself hasn’t changed: people still shop to solve a need or achieve a desired outcome, they will always search for products and continue to take product and service recommendations from those around them.
But what is changing is the interaction between brands and shoppers – and brands must take note. What used to be a short, product-led search in a text box is evolving into a broader, more detailed conversation that explores wider motivations for a purchase – this requires a shift in how brands earn visibility and relevance.
Instead of simply typing in keywords, people are now turning to conversational interfaces to explain what they wish to achieve and then browsing a set of AI-generated recommendations. Product discovery is becoming more contextual and intent-led, with consumers looking for guidance and being receptive to suggestions, rather than feeling restricted to searching for a single product.
Imagine someone is preparing for a marathon. They know they might need a new outfit or the right gear, but don’t necessarily know what to buy yet – they just know what outcome they’re looking for. So, what might once have been a basic search for “trainers” is now a fuller prompt – “I’m running the London marathon and need new running shoes, what should I look for?”.
By providing chatbots and conversational AI models with a clear picture of what is driving the purchase, consumers are giving brands richer insight into their intent. This is a huge opportunity. The brands that use this data to deliver relevant and timely recommendations will be best placed to influence purchase journeys and remain relevant to today’s shoppers.
Consumer behaviours are changing
This shift is already reshaping shopping habits in the UK. Kantar research shows that a quarter of British consumers have used AI to seek product recommendations, highlighting how quickly these tools are being adopted as a natural part of the discovery journey.
Consumer reliance on AI is far from uniform though, and varies across sectors as shoppers require different levels of support with their purchases. Recent research suggests that consumers reach for AI guidance most when facing complex decisions, multiple options or are seeking reassurance. As consumers look for help navigating recommendations and masses of choice, categories such as beauty and skincare see higher levels of AI referral traffic.
Large Language Models (LLMs) and AI-led shopping tools are enabling these changes in product discovery. By securely collecting contextual data, resources like Mastercard’s Shopping Muse make shopping feel like chatting with a friend, with recommendations shaped by context, preferences and behaviour rather than keyword searches alone.
Richer data for brand growth
For retail brands, this is an exciting opportunity. As AI becomes a key part of the shopping journey, brands need to rethink how they show up. Being visible in a search result is no longer enough; they must also be relevant.
Fuelling this is a rich layer of data insight. The data generated through digital and AI-enabled shopping experiences provides merchants with a more detailed understanding of shopping habits, personal preferences and insight into how transactions are completed. Armed with this, brands can see exactly where, why and how their outreach efforts are successfully translating engagement into conversion, and where there is opportunity for improvement.
Used responsibly, data creates compelling new opportunities for brands to deliver personalised content and offers at the very moment they will resonate most.
For example, as that marathon runner peruses options for a new pair of trainers, an AI agent could suggest a new running vest too or perhaps electrolytes to help with recovery. Capturing consumer attention when intent is at its highest has the potential to drive conversion at new rates, with initial use cases for these models showing a return on ad spend of up to 22 times.
Building trust for a competitive edge
As such journeys become more seamless and transactions become faster, confidence and trust will be the differentiator.
Recent research from dentsu indicates that while appetite for AI-powered shopping experiences grows, there remains a trust gap when it comes to fully automated purchases, with nearly two-thirds of British consumers feeling uncomfortable in allowing AI to purchase for them autonomously.
In a world of technological novelty, trust cannot be assumed. To consistently earn and re-earn loyalty, brands must address these hesitancies head on and pair new capabilities with relevance, transparency and responsible data use.
Ethical AI-led personalisation will be key. This means using AI to create relevance in a way that is transparent, consent-based and provides clear value to the consumer. AI should feel assistive, rather than intrusive, and the consumer should always feel in control of their data and understand the reason why they are seeing a recommendation or offer. When personalisation is designed with these principles in mind, it strengthens trust and long-term loyalty, rather than undermining it.
Trust and brand matter more, not less, in AI-led commerce – when it comes to acting on recommendations, consumers need confidence that the transaction is secure and their data protected.
While AI may change how consumers discover products, the fact remains: consumers buy from brands they trust. The brands that combine relevance with trust – from first prompt to purchase delivery – will have the competitive advantage.
Lidewij van den Ham
Lidewij van den Ham is Senior Vice President of Consumer Acquisition & Engagement for Mastercard Europe. Based in The Netherlands, she has worked at Mastercard for over a decade, covering numerous roles including Head of Loyalty Sales EMEA. Lidewij’s background is in financial services – prior to Mastercard she spent five years at ABN AMRO Bank as Head of Strategy, Business & Portfolio Management, and nine years at PwC before that.
