Multimodal AI is transforming how we interact with machines. By processing not just text, but also images, audio, video, and sensor data, these systems can interpret and respond in ways closer to human communication. When combined with extended memory, which stands for an AI’s ability to remember context across interactions, the result is a leap toward highly personalized, continuous, and adaptive experiences.
What this means for retailers
For retailers, these two breakthroughs are the missing piece that unlocks AI’s full potential. Until now, adoption often fell short because AI couldn’t connect the dots between visual merchandising, customer conversations, product data, and sales trends. Perhaps even more importantly, it couldn’t remember interactions across time. Without that, personalization was shallow, omnichannel experiences felt disjointed, and valuable insights were trapped in silos. Multimodal AI with extended memory changes that, enabling retailers to truly capitalize on AI with seamless, intelligent, and context-rich customer engagement.
Understanding customers like a human would
So far, AI had relied solely on text or numerical data, and its capabilities were limited. Now it has gained new senses which allow it to understand the world and interact with it in new ways. Multimodal AI can process and interpret product photos, in-store camera feeds, voice notes from customer service calls, and online reviews side-by-side. These human-like comprehension and capabilities mean AI can identify patterns, such as analyzing in-store camera feeds to detect where shoppers pause or handle items, matching that with voice interactions from sales associates, and linking both to online browsing patterns to uncover why certain products spark interest but don’t convert to sales.
Remembering every customer interaction
Extended memory allows AI to track and recall customer preferences, past purchases, and even the questions they asked months ago. For retailers, that means no more repetitive “starting from scratch” moments. Whether a shopper is returning online after a long absence or speaking to an associate in-store, the AI can bring forward relevant context instantly.
Personalization at scale
When multimodality meets memory, personalization moves from “people who bought X also bought Y” to truly tailored experiences. A shopper’s journey, their whole relationship history (what they clicked on, what they asked about, what they liked in-store, and what they purchased) becomes the foundation for dynamic recommendations, targeted offers, and adaptive customer service responses.
A unified, omnichannel customer journey
These capabilities help retailers integrate online and offline touchpoints into a single, seamless journey. From recognizing a customer’s voice on a service line to connecting it with the image they uploaded for a return request, AI can create continuity across channels, reducing friction and boosting loyalty.
Implementation and use cases
The promise of multimodal AI and extended memory is enormous, but its impact depends on how retailers put it to work. Many successful implementations start small, targeting use cases that can quickly prove value, then expand to more complex, cross-departmental applications. Here are some of the use cases that forward-thinking retailers are already testing:
Enhancing merchandising decisions
Retailers are leveraging heatmap technology to optimize store layouts and product placement. For instance, brands like Samsonite have used heatmaps to analyze foot traffic and adjust store layouts, while Sephora has utilized them to understand how customers interact with products. If a store layout in one location boosts conversion rates, AI can flag it for replication across similar sites.
Elevating customer service
AI systems can instantly retrieve and process all relevant customer data like order history, prior interactions, and outstanding issues, within the model’s active context window at the moment of engagement. This capability enables the AI to surface the right information to either a human associate or directly to the customer in real time, ensuring faster resolutions, consistent service, and minimal need for customers to repeat information. Temple & Webster, an Australian online furniture retailer, has significantly expanded its AI use across customer service. Now, over 80% of customer interactions are partially or fully handled by AI, reducing customer care costs and improving resolution efficiency.
Optimizing inventory and supply chain
By connecting sales trends with in-store sensor data and supplier lead times, AI can forecast demand more accurately, reducing overstock and stockouts while adapting to local preferences. Major retailers like Target, Walmart, and Home Depot are deploying AI to optimize inventory and reduce stockouts. Target’s AI-powered Inventory Ledger now helps forecast demand and monitor misplaced stock, covering over 40% of its assortment. Walmart uses AI to tailor inventory to regional demand patterns.
Marketing campaigns and content creation
Instead of generic promotions, AI can tailor campaigns to each shopper’s preferences and history, even adjusting creative formats like video, text, and images based on how everyone engages. It can automatically generate campaign imagery and copy that align with brand guidelines, pulling from product photos, past campaign performance, and even seasonal trends to produce store and customer-specific creative. AI-driven personalization has yielded 10–25% increases in return on ad spend, powered by real-time content generation, richer customer profiles, and decision engines.
Dynamic pricing strategies
By combining visual merchandising data, competitor pricing scraped from the web, and real-time sales performance, AI can recommend price adjustments store-by-store or even hour-by-hour. Dynamic pricing is gaining momentum across retail sectors. Boohoo and PrettyLittleThing (now part of the Debenhams Group) are deploying AI-based pricing models to manage real-time pricing based on consumer demand and competitor pricing.
Product discovery and visual search
Shoppers can upload a photo of a product they like from social media, another store, or an old favorite, and the AI can identify matches or similar items from the retailer’s catalog instantly. This is how visual search tools are transforming product discovery. ASOS, for example, implemented visual search that allowed users to snap street-style photos and find similar products, resulting in increased engagement and sales.
Sustainability and waste reduction
By combining expiry dates from inventory data, visual monitoring of shelf stock, and sales velocity, AI can suggest markdowns or promotional pushes to prevent waste, especially in grocery retail. In the grocery sector, AI startups like Wasteless and Kimaru.ai offer markdown optimization solutions. Wasteless uses AI-powered dynamic pricing to reduce fresh product waste by analyzing sell-by dates and demand patterns.
Balancing personalization with privacy
The shift toward hyper-personalized, context-aware retail experiences brings undeniable value, but it also raises significant privacy and security concerns. To deliver on the promise of multimodal AI and extended memory, retailers will need to collect and store more customer data than ever before. The data will need to be accessible across channels and touchpoints in near real time.
With this expansion of data collection comes heightened risk. Larger, richer datasets become more attractive targets for cyberattacks, and as analysis tools become more powerful, the potential for misuse grows. This makes it easier than ever to sift through sensitive information and potentially weaponize it.
For retailers, the mandate is clear:
- Maintain robust data security to prevent breaches and unauthorized access.
- Implement strict privacy and consent policies so customers understand how their data is used and can opt in with confidence.
- Embed trust into AI interactions, making transparency a core feature of every personalized experience.
The brands that thrive on AI will be those that can blend deep personalization with uncompromising trust.
