From Data Systems to Digital Shelves: How AI Is Reshaping Retail

A few years ago, ChatGPT made Generative AI feel personal and we began to explore what it could do. We were fascinated by this new technology and used it to ease the mental load in small areas of our lives. We had it help us write resumes, plan birthday parties, and yes, you may remember two years ago we used it to find the perfect oddball gift for quirky Uncle Wayne.

While we approached this technology with a cautious curiosity, today new questions are being asked of AI at a much bigger scale by the world's largest retailers. In today’s day and age, we are starting to wonder what technology could enable retailers to do better, smarter, and faster, beyond copy editing emails and helping us be more organized. While some forms of AI have been quietly powering retail for years (those "customers also bought" modules didn't write themselves), the past 18 months have moved the conversation from "where could we sprinkle in some AI?" to "how do we re-architect our business around AI?".

From conversational shopping assistants to fully agentic checkouts inside third-party AI surfaces, retail is becoming one of the most ambitious proving grounds for what this technology can do.

Where Leading Retailers Are Already Acting

In particular, we've seen large retail brands like Walmart and Target, among others, applying AI to their businesses in three core areas spanning conversational and agentic commerce, hyper-personalized experiences, and operational and associate enablement.

In conversational & agentic commerce, retailers are building branded shopping assistants that can answer questions, surface products, and actually complete transactions from start to finish. The frontier here is "agentic commerce," where AI agents transact on the shopper's behalf, including inside third-party surfaces like ChatGPT and Gemini. Walmart's Sparky assistant, powered by its proprietary retail-tuned LLM family Wallaby, is one of the most prominent examples. In October 2025, Walmart partnered with OpenAI to enable purchases directly inside ChatGPT and announced a Google partnership to bring Walmart shopping into Gemini. Target followed suit, launching a Target app inside ChatGPT for the 2025 holidays. The storefront, in other words, has evolved past a first-party website or app. Today, it lives wherever your customers happen to be talking to AI.

Retailers can also use AI to render a different, hyper personalized shopping experience for every shopper. Walmart's AI-powered Content Decision Platform creates a unique homepage per customer, the foundation of its "Adaptive Retail" strategy. Lowe's is rolling out Pinterest-style visualization that lets shoppers snap a photo of their kitchen and reimagine it in real time, with AI-driven curation tailoring each session. The common thread is that every shopper sees something different, and the system gets smarter with every interaction.

AI is reshaping both what customers see and how associates work. Walmart is also a first-mover in this space with their “Ask Sam” application, an associate-facing voice assistant, that is used by roughly 900,000 employees fielding more than 3 million questions a week. Kroger announced an expanded Google Cloud relationship in January 2026 to deploy Gemini Enterprise nationwide, integrating Meal Assistant and Shopping Assistant flows, and is using Customer Experience Agent Studio to analyze inbound store calls and resolve issues proactively.

Retail brands are taking big bets as they imagine both a redesigned digital shopping experience and enhanced ways of working for their associates, and it all starts with AI.

How to Get Started with AI

Some retailers, however, may feel overwhelmed by the sheer pace of change. We hear it in our client discussions and weekly meetings. When pioneers in the retail space are launching agentic commerce experiences and rebuilding their tech stacks around proprietary LLMs, it's easy to feel behind before you start.

Our advice is the same as it was a few years ago, to brainstorm broadly, start small, and keep testing. As my father reminds me often, it’s not “trial-and-error”, rather “trial-and-discovery”. The biggest wins often come from somewhere you weren't looking, whether it is an underperforming PDP, a clunky service interaction, a merchandising workflow that eats up hours every week.

If you're a retailer looking for ways to integrate AI into your work, start with focused use cases that improve discovery, content, service, personalization, and conversion.

The most accessible starting point for many retailers is AI-enabled site search. Activate built-in AI search capabilities that are likely already a part of your tech framework. This is perhaps one of the highest-value, lowest-effort, changes a brand can make today. From our experience, one client saw a 10-15% revenue lift by adopting Adobe’s Live Search and Adobe Sensei to improve its internal search results. The data ingestion, pattern recognition, and continual refinement done by Adobe’s AI and ML tools provided personalized search results that continue to yield best-in-class customer experiences, the results of which are seen in their year-over-year order growth.

For brands managing large product catalogs, generative product content is another natural entry point. AI can scale and audit product descriptions, alt text, and digital-shelf assets, especially where manual content writing is a bottleneck. This matters even more for brands whose products live on multiple retailer or third-party platforms. Think about the undertaking for Coca-Cola, for example, to ensure every grocery store, retailer and restaurant has consistent, updated product information.

When it comes to conversational shopping assistants, start with a contained use case (e.g. gift finder, project planner, troubleshooting) before expanding to a general-purpose concierge to see if the lift is worth the effort.

On the experience side, retailers can take a page from Walmart’s playbook and move beyond out-of-the-box "recommended for you" placements toward truly adaptive, personalized homepages and category pages. Meanwhile, giving store and contact-center teams an AI assistant that knows your policies, inventory, and product catalog pays dividend quickly. These tools can help employees answer questions, find information, and serve customers more effectively. Is this avocado oil good for cooking? Let’s see together, and oh, here’s a suggested recipe that looks delicious, too.

The same shift is happening in visual and AR experiences. While sometimes we think of this as AI of the future, it’s becoming clearer how this technology fits into the customer journey today. Clothing try-ons, room furnishing visualization, and "show me how this looks" tools convert browse intent into purchase intent.

Lastly, on the data side, loyalty and audience segmentation tools can separate your high-value members from your price-sensitive ones in real time, then serve each cohort a marketing message and custom experience that meets their needs.

Where Retail Goes from Here

The gap between retail leaders and laggards is going to widen, and fast. The companies treating AI as a strategic re-platforming, not a series of pilots, are pulling ahead. The ones still asking, "should we?" will spend the next two years asking, "how did we fall so far behind?"

As AI continues to evolve, retailers should watch for and address gaps in their data foundations, content operations, and customer experience workflows, then test AI applications that align with their business goals.

Our perspective is simple. Retailers who win the next five years won't be the ones with the biggest AI budgets or most intrusive “Is it AI Enabled?” banners hung in the office. They'll be the ones who treat AI as a way to know their customers better, serve their associates better, and show up wherever shopping happens next. The rest is just performative.