How AI is Changing Digital Commerce in 2025

1. Customer-facing AI opportunities

Machine learning (ML) and natural language processing (NLP), both components of AI, are already well established in traditional areas like site search, personalized merchandising, and promotions, customer service chatbots and fraud detection. But AI is not limited to these functions. 62% of IT leaders are actively exploring AI for customer-facing applications. Within ecommerce, this includes:

AI shopping assistants replacing traditional search

More merchants are adopting AI shopping assistants powered by fine-tuned model chat interfaces integrated directly into ecommerce storefronts. Unlike traditional chatbots, these models understand nuanced queries, support natural language and respond with dynamic product listings pulled from real-time catalogs. They’re increasingly multi-modal—able to interpret both text and uploaded images (e.g., “Find me shoes that look like this”).

As consumers become more comfortable with interfaces like ChatGPT, traditional search boxes for product discovery will get enhanced by SLM powered chat equivalent interface. They will reduce the number of clicks shoppers need to take through their journeys and help them make buying decisions faster. Shoppers can tell the searchbot what their buying criteria are and ask questions in a conversational, human way.

Embedded generative Q&A

Like shopping assistants, AI-powered ask-and-answer tools are popping up on more product detail pages. They typically show suggested questions related to the specific product being viewed by default, but users can ask any free form question they want.

While they function the exact same way as shopping assistants served through a chat widget, user testing suggests that online shoppers often confuse chat assistants with traditional live help and may find these widgets disruptive when they cover content or “pop up” while browsing. Embedding conversational AI components directly into page templates provides a more streamlined and user-friendly experience.

Visual search

Computer vision paired with well-trained AI models enables customers to search with images or explore visually similar items to any given product. For example, shoppers can:

• Snap a selfie, upload a photo or link to an image on the Web to search the catalog
• Click a “shop similar” link within category and search list results to filter the grid by visually similar items
• Click hotspots on banners, product images and embedded social media posts to search by specific visual attributes (such as a piece of an outfit, or a specific neckline)

These experiences make digital shopping more human by eliminating the need to type or think of how to describe products through a search query. Like showing a product to a helpful salesperson, visual search can match customer intent and shopping context, fast.

Product images and videos can also help enrich product data by recognizing and auto-tagging products with specific attributes. These attributes can be used for more precise search matching, categorization, filtered navigation, product finders and cross-selling.

Voice search

Through speech recognition and advanced NLP, voice assistants can listen and speak back like a helpful human salesperson – even asking qualifying questions to guide shoppers to the right products.

With every mobile device equipped with a microphone, it’s easy to support voice features, or extend them to smart speakers and similar IoT devices.

And voice can be used for more than just search. Consider enabling it for appointment and delivery bookings, submitting product reviews, engaging with live chat and updating account information and preferences. We may soon see speech-directed checkout and payments through “voice ID.”

Responsive merchandising

GenAI has powerful potential to customize shopping experiences to the individual user. “Responsive merchandising” adapts product content to a specific context or user setting. For example, a customer can select their body type, skin tone or clothing size to swap thumbnail images within a product list and persist this preference across all browsed categories and searches.

GenAI makes this possible by creating virtual models and replicating photo shoots at scale and even generating new images on-the-fly. Walmart’s Virtual Try On is one of the first such implementations. Shoppers can preview an outfit on their own selfie or choose from one of hundreds of virtual models.

Google Shopping uses AI to call out “top features based on reviews” for select products in its search results. For example, “slim fit,” “good water resistance” and “great insulation” appears with “ski jackets.” These annotations replicate what a knowledgeable salesperson could tell you in-store.

Other merchants use AI to analyze reviews and returns data and dynamically inject fit data into product pages, for example “fits small, order a size up.”

Hyper-channel personalization

Commerce doesn’t happen within siloed channels – today’s shopper is hyper-channel, bouncing across digital, physical, and social touchpoints (and back again). Capturing as much data within a customer intelligence platform is critical to achieving the “360 degree customer view” that’s needed to persist AI-driven personalization across complex buying journeys.

The intelligence cloud can be enriched with third party data for even more precise personalization. Through customer tagging and dynamic auto-segmentation, AI can generate new, data-driven micro-segments based on observed behavioral attributes marketing teams never even think of and apply them to shopping experiences in real time.

Agentic AI

Agentic AI will start playing a pivotal role in improving customer experience in any ecommerce solution. Brands will utilize the tools provided by ecommerce platform to build AI Agents for solving problems like Dynamic Pricing and Promotions based on competitive trends, automatic Product Categorization and attribute generation, Choosing best fulfillment options to reduce costs, Hyper personalized micro marketing campaigns, Fraud detection and prevention, Autonomous Returns and Refunds handling and create newer business specific agents which today require their Service Integrator to build using code development.

Whether you build-your-own or bring third party AI point solutions into your stack, it’s critical to have flexible commerce technology to enable fast and robust integrations. A headless and composable commerce platform is ideal to support AI transformation.

2. Operational AI opportunities

Operationally, AI enables faster, leaner, and more intelligent execution across ecommerce functions from development to delivery. Here’s where that impact is most visible:

Campaign management

Marketers are increasingly using AI for multichannel campaign execution. Large Language Models can generate campaign concepts, headlines, and creative assets tailored to specific audiences, while machine vision tools ensure visual consistency across banners, emails, and ads. Teams can test 50+ creative permutations with minimal lift, using AI to pre-score messaging based on tone, clarity, and expected CTR.

AI is also dramatically redefining campaign delivery. Rather than planning months in advance, campaigns can be continuously deployed and refined based on live shopping behavior. AI tools segment users, generate tailored offers, and deploy creative variations optimized for ROAS and CAC across Meta, Google, TikTok, and email.

And to optimize spending, AI models enable dynamic budget allocation to adjust spend daily based on margin targets, inventory constraints, and conversion trends. For media buyers, this means moving from manual adjustments to managing strategy while AI handles channel mix, bid strategies, and audience expansion in real time.

Infosys Equinox Digital Commerce and Marketing Operations team leverage AI and automation across a variety of activities, such as design, digital content, digital merchandising, SKU management, searchandising, and email marketing.

Supply Chain

AI is making ecommerce supply chains much smarter. Machine learning models power demand forecasting down to the SKU, channel, and even ZIP code level, incorporating real-time signals like web traffic, cart abandonment, social mentions, and competitive pricing. These inputs feed into dynamic replenishment engines that adjust purchase orders and safety stock buffers.

Warehousing and fulfillment are also being optimized end-to-end. Warehouses can use AI to predict which SKUs will be needed where, down to the local level, enabling better pre-positioning of inventory. This reduces the last mile costs and enables faster delivery without relying on costly express shipping. AI systems can also decide where to route orders based on shipping speed, cost, and available stock while automatically flagging supply disruptions and suggesting alternatives.

Revenue Growth Management (RGM)

For CPG brands and manufacturers, AI-powered RGM platform, Infosys Equinox Strategic Pricing enable strategic pricing and eliminate the traditional error-prone manual processes involved with revenue generation management. Self-learning algorithms paired with predictive modeling enable you to run multiple pricing and promotion simulations simultaneously, and auto-deploy the optimal strategies at the brand, product and SKU levels across markets and channels.

Advanced RGM enables CPGs to capture maximum consumer surplus without harming market share and develop more profitable trade and consumer promotions.

To summarize, the AI genie is not going back into the bottle – it’s here to stay, and offers exciting opportunities to enhance customer experiences, accelerate growth and get the most out of your people and platforms. How quickly and extensively you adopt AI within your organization depends on many factors including budget, internal expertise, and the readiness of your technical architecture. If you need guidance on how to get started on your AI-driven commerce journey, reach out to us at contactus@infosysequinox.com.