The world is awash with the changes AI is bringing. Just this year OpenAI forecast AI will be able to start and run entire companies within the decade.
While fascinating — and more than a little scary — this isn't actionable for most brands here and now. Instead, let's focus on the immediate: What concrete changes will AI bring to the retail experience in the next year? And more critically, what will differentiate the brands that win?
1. Hyper-personalized experiences
Personalization is the most tangible AI benefit consumers will notice. Already, the most sophisticated product pages are tailored per user. No Amazon front page is the same for two people. If web 1.0 brought brands online, and web 2.0 offered interaction, the next iteration will be wide distribution of context-specific UX, across all retail
The brands that succeed will operationalize their customer data. Take J.Crew: If the brand knows I favor blue linen shirts, they'll promote those in my feed, emails, and ads. Nike, by contrast, where I have no purchase history, will be out of the loop and reduced to guessing about my preferences. In this new environment, personalization isn't a bonus—it's the cost of entry.
2. LLM-powered product discovery
Search is shifting. The traditional keyword model, where consumers hunt via simple strings like "running shoes, size 11," will give way to conversational, deeply contextual discovery.
LLMs (large language models) enable this deeper context. Instead of keyword juggling, users will describe needs in plain language: "I need shoes for running." The LLM will increasingly have context for providing a hyper-personalized example, including the climate in my location, previous purchase history, favorite brands, size, and even running habits.
To win, brands need to structure their product metadata and knowledge in a way LLMs can access and interpret cleanly. In search, conflicting signals can route users to the same generic item. In LLM-powered discovery, the nuance of user intent becomes paramount. Brands that provide rich, structured product data will see their goods surfaced more often, and more accurately.
3. AI-evolved Ad targeting
Privacy shifts (notably Apple's App Tracking Transparency) killed deterministic retargeting. Ad giants like Meta and Google pivoted hard, building AI-driven targeting systems (Advantage Shopping Plus and Performance Max) trained on conversion data rather than identity. These systems use AI to optimize placement, format, and creative at scale, learning from aggregate user actions. This means users see highly effective, targeted advertising. A change in the post-privacy world.
For brands, the new playbook is to feed the platforms with the best possible data signals. No longer are you uploading user lists to the ad giants and waiting for them to funnel you customers. Now brands need to track the full conversion path, from ad click to sale, and share it back with platforms using CAPI (Meta) or Enhanced Conversions (Google). Doing this well isn't easy. It requires tight alignment between your data layer, measurement stack, and marketing ops. Brands that surface clean, timely conversion signals are rewarded with significantly better campaign performance.
4. Cross-channel experience consistency
Consumers are no longer bound to a single touch point. They browse on mobile, research on desktop, stream on CTV, and now ask LLMs for product suggestions. The challenge: brands must offer a coherent, seamless experience across all channels. This demands unified customer profiles, integrated media strategies, and cross-channel measurement. Imagine being able to surface an ad on a product based on the contents of a person's mobile shopping cart.
Success here requires infrastructure. Brands must pipe data between CDPs, ad platforms, LLM integrations, and measurement systems in near real-time. And they must do so while respecting user privacy and consent frameworks.
AI is not a distant abstraction; it's reshaping consumer experiences today. From personalized product recommendations to a holistic channel experience, the sophistication is daunting but the opportunities for a brand to prepare are extraordinary.
The brands that win are:
- Using their customer data as an asset to increase optimization.
- Investing in rich, structured product data for AI discovery.
- Building systems to capture and return conversion signals across the funnel.
- Unifying customer experiences across channels and platforms.
This isn't about chasing hype. It's about building the data and infrastructure foundation that will define retail leadership for the next decade.