How AI Is Rewriting the Rules of Shopping
A new kind of shopping journey
A new kind of shopping experience is beginning to take shape. Retail giant Walmart first introduced generative AI–powered search in early 2024 and has continued to expand its capabilities across platforms. Most recently, the company has rolled out conversational agents like “Sparky” that interpret life-scenario prompts such as “football watch party” or “family gathering” and deliver curated product recommendations across categories. This is more than just a user experience (UX) improvement. It signals the early stages of AI-native commerce, where shopping becomes more conversational, contextual, and eventually, anticipatory.
From smarter search to autonomous agents
Natural language search is just the beginning. As AI continues to evolve and integrate into our daily lives, autonomous agents could start managing parts of the purchasing journey. These systems may eventually anticipate needs based on behavioural patterns and handle simple transactions on behalf of the user. From renewing household essentials to managing routine seasonal purchases, AI has the potential to play a much more active role in commerce.
In that kind of environment, the familiar moment of choice could fade. Traditional marketing levers such as ads, promotions, and product placement may have less influence when a machine is executing the purchase. What matters most is trust. If an AI agent already knows which brand its user prefers, there may be no opportunity for alternatives to enter the conversation. That puts growing pressure on brands to cultivate loyalty and preference long before the point of sale.
Why retail media is poised to win
The platforms best positioned to benefit from this shift are those with rich behavioural and transactional data. Retail media networks like Amazon and Walmart Connect have a head start, not just due to their scale, but because of the depth and variety of their customer data. Walmart, for instance, draws insights from both in-store and online activity, offering a holistic view of shopper behaviour that few digital platforms can match.
That said, this advantage is not limited to the biggest players. Mid-sized or niche retail networks with loyal audiences can offer highly specialised behavioural signals that are just as valuable in certain contexts. The key is not size, but quality of signal, what unique insights your data can offer to help AI systems deliver better, more relevant outcomes.
This data opportunity is already beginning to translate into measurable business value. A recent Bain report noted that “beyond trade” revenue streams like retail media now account for roughly 15 percent of sales and 25 percent of profit for typical US and European retailers. As personalisation grows in importance, retail media’s role will likely deepen, powered by first-party data.
The next frontier in attribution and privacy
As AI platforms start integrating more deeply with retail systems, we may see the emergence of more sophisticated attribution models. API-level connections could eventually enable brands and retailers to understand how AI-driven interactions lead to transactions with greater clarity.
However, a warning. As AI continues to evolve and integrate into our daily lives, the ethical and legal implications of its deployment are drawing increasing scrutiny. One of the most pressing questions they raise is how consent should work in this new environment, specifically how individuals give permission for their data to be used in training and operating AI systems.
Consent has long been a foundational principle of data protection law. Under the General Data Protection Regulation (GDPR) in the EU and similar frameworks worldwide, consent as a legal basis ensures that individuals retain control over their personal information. It fosters trust between users and organisations and is one of the clearest signals of respect for individual rights. But the application of consent in the context of AI, where transparency obligations are key, presents unique challenges that must be overcome.
Other players in the digital ecosystem will also need to adapt. Publishers, who were once heavily relied upon for training content, may see AI shift its attention toward commerce signals. To remain essential, they will need to focus on content types that are harder to replicate, such as investigative reporting, expert analysis, and community insights that bring real context. Social platforms may also need to evolve their value proposition as consumers increasingly turn to AI-powered interfaces for product research and discovery.
Preparing your brand for AI-native commerce
For brands, the most urgent challenge is visibility. With more adults turning to platforms like ChatGPT, Perplexity, Google’s AI Overviews, and Amazon Rufus to explore products, many brands are still unsure how they’re represented within these emerging environments. This has given rise to a new area of focus: Generative Engine Optimisation (GEO). Early solutions in this space are helping brands monitor how they appear across AI systems, analyse the kinds of queries being asked, and optimise content accordingly.
Looking ahead, the brands that succeed will be those that prepare now. That means building real consumer trust, investing in first-party data collection, ensuring their infrastructure is ready for API-based integration, and forming smart partnerships with retail media networks. Transaction data should be seen not only as an operational tool, but as a strategic asset in the age of AI.
The shift toward AI-native commerce is already underway. While mainstream adoption may take time, the complexity of building the right foundation means that early movers will gain a clear advantage. The question is no longer whether AI will reshape shopping, but whether your organisation will be ready when it does. This is not the time to hold back.
