Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
Google AI defines ‘AI shopping’ as the integration of artificial intelligence technologies into the online and offline buying experience to personalise and enhance the customer journey, that includes using AI-powered real-time assistance such as chatbots, personalised recommendations, smart search features, virtual try-on options—all of these to streamline the purchasing process, create a more efficient and enjoyable shopping experience, and even optimise logistics.
Artificial Intelligence (AI) is rapidly transforming the shopping experience for both B2B and B2C customers. In the B2B space, businesses are leveraging AI to forecast product demand, optimise inventory, and streamline supply chain operations. Meanwhile, in the B2C segment, AI enhances the customer journey by making transactions easier, more efficient, and deeply personalised.
AI has the ability to make online shopping as joyful as going to a physical store, which attracts online shoppers to return to brand and become loyal customers. A February Capgemini report found that roughly two-thirds of millennials and Gen Z, closely followed by Gen X, have replaced traditional search engines with tools like ChatGPT for product recommendation. An Adobe report added to the Capgemini’s findings, which found that consumers are purchasing more from Generative (Gen) AI’s suggestions. Google defines Gen AI as a subset of AI that focuses on creating new content, including text, images, audio, video and code, based on data it has been trained on. It uses AI to generate new and fresh outputs that resemble the patterns and styles found in the training data.
In the era when many retailers and e-commerce players are shifting to AI, ChatGPT launched two key AI-powered products in 2025, which represent new ways of shopping online.
Shopping on ChatGPT
Adding a thrust to the AI boom, Open AI announced buying of products through ChatGPT in April-end, the shopping button for which is now open to all, be it signed-in user or not. The tool lets users search and compare products. The product recommendations shown to prospective shoppers are based on what ChatGPT remembers about user’s preferences and product reviews pulled from across the web. The shopping on ChatGPT is seen as an organic development because ChatGPT users are reported to be already running over a billion web searches per week and people are using the tool to research a wide breadth of shopping categories. The new user experience of buying products inside of ChatGPT shares many similarities to Google Shopping. The interfaces of both tools, when shopper clicks on the image of the product, list multiple retailers on the screen along with buttons for completing the purchase. However, unlike Google Shopping, ChatGPT shopping experience is personalised and conversational and not just keyword-focused. It stores the information about a particular product, which a shopper prefers to buy from a retailer, in its memory for future recommendations that will align with shopper’s tastes. As for the reviews that ChatGPT features for products, they are pulled from a blend of online sources, and the users can also tell ChatGPT which types of reviews to prioritise when curating a list of recommended products. It must be noted that one of the ChatGPT’s competitors Perplexity—an AI-powered searcher, had launched ‘Buy with Pro’ late last year where users can shop directly inside of the app.
ChatGPT Checkout
ChatGPT also launched the Checkout system this year which allows shoppers to purchase goods directly through conversational AI. Instead of browsing through endless product pages and adding everything into the shopping cart, customers can just talk to ChatGPT-powered bots and avoid jumping between multiple websites and tabs. They can now ask tool to search across different platforms for product recommendations and also add specific requirements to refine the results. When they go to specific site for purchase, an AI-powered bot leads them through the payment process, apply discounts and provide shipping information.
The AI assistants handle every aspect of the buying process starting from product discovery, queries resolution and recommendations to applying for discounts, making payment and even providing post-purchase support. At discovery stage, instant recommendations are provided matching the description given by the customers. The AI chatbot provides recommendations on sizing, availability, fit, shipping times and return policies etc based on customer preferences, purchase history and current trends, which may also include suggestions on complementary products or upgrades that may add value to the purchase. The Checkout system can automatically detect and apply available discounts and promotional offers during the conversation, and once the customers confirm their selections, they are guided through a secure payment process within the chat interface. The payment details are entered in chat and bot confirms the purchase. Once the payment is made, the AI sends order confirmations, shipping updates and helpful care instructions.
AI fundamentals
The ChatGPT launches are just one of many examples of how AI is disrupting the online shopping. The immense potential which AI holds can make anyone interested in knowing its fundamentals and types, and how it helps elevating the shopping experience. So, for starters, AI is of five types:
Reactive AI: This AI type reacts to specific inputs and pre-programmed actions, and can quickly answer customer questions, provide product information and help with basic troubleshooting. It is commonly used in online shopping for chatbots, AI shopping assistants and other automated customer service tools. As a real-life analogy, this type of AI is useful for a customer asking for a specific dress. It can not only answer questions about the dress, but also recommend matching accessories to enhance overall dressing look. Reactive AI is equally proficient in managing the basic customer service needs like order tracking or returns.
Limited memory AI: Also known as Machine Learning (ML) and Limited Memory AI, this type of AI learns from the past data and experiences. It can forecast customer behaviour and use collected customer data to create an engaging AI-powered personalised experience that will likely result in conversations. The e-commerce giant Amazon uses Limited Memory AI for product suggestions which are inspired by shopper’s previous purchases and browsing activity.
Theory of Mind AI: Essential to conversational commerce, this AI type provides an experience as good as human communication. It truly understands shoppers’ emotions, beliefs and intentions, and provides them with more sophisticated and intuitive interactions. However, it is still under development. Its use case analogy: By examining a shopper browsing a selection of blazers, pants and office shoes in search of a new workwear, Theory of Mind AI can suggest products that are stylish yet office-friendly.
Agentic AI: Another AI option in conversational commerce space is AI Agents—the digital assistants that can work autonomously and use Large Language Models (LLMs), NLP (Natural Language Processing) and ML to not only carry out tasks but also reason and learn to optimise their processes. Additionally, it can hyper-personalise search results by analysing every click, search, preference, purchase and even return. Adapting marketing campaigns in real time across all channels, it can empower marketing teams to autonomously create campaigns in a fraction of the time, besides revealing actionable insights and identifying key opportunities. When a specific goal is assigned to the AI Agent, it determines the most effective way to achieve it without requiring constant manual oversight, thereby allowing teams to focus on strategic, high-value tasks rather than getting bogged down in the details.
Self-Aware AI: This is an AI with consciousness and can adapt to the world around them, learn independently and grow more insightful over time. Like Theory of Mind AI, this is yet to make noticeable presence in AI-powered shopping. Once fully unleashed, everyday customers will be able to use it in the form of personal shoppers who will take directives from humans and perform all the research and price-checking needed to facilitate an informed purchase.
Irrespective of the type of AI being used, the objective remains the same— to create and provide personalised shopping experience to the shoppers.
Personalisation
In AI-powered personalisation, every click, pause and scroll is a data point which ML systems use to transform how customers shop. As soon as a shopper lands on an AI-powered site or app, he finds the homepage filled with styles that he has browsed, items he has saved or looks similar to the ones he loves. Here the search bar is beyond just keywords and understands the intent as well. The product recommendations are curated to seem genuine advices, instead of irritating advertisements. Thanks to AI technology, the e-commerce-aiding platforms can now combine a selfie and personal selection and convert it into a full ‘shoppable’ look, from head-to-toe, suggesting a styling that matches shopper’s features, taste and vibe. This look can then be saved in smart phone, added to lock screen and shared with friends. Since this happens before shopper can even enter an e-commerce app, the buyer journey starts with an inspiration and not on a homepage.
Adding to the personalisation, AI can tap into micro-moments of the shopper as well. It ensures that user feels seen, understood and excited by a discovery. These micro-moments result in shopper clicking ‘Add to wishlist’ when the style is apt, or ‘Download look’ when saved for later use or try-ons, or ‘Buy now’ clicks when purchase decision is executed. In either situation, the result is driven by emotional and not rational evaluation.
In today’s e-commerce, personalisation has become the new standard because a brand which is not personal can be easily forgotten. The shoppers expect the brand to know what is trending, for which AI is a must. The AI shopping learns real-time from the user behaviour, delivers content and recommendations which users may not know they need, and blend inspiration, utility and action in one seamless journey.
Mind of the machine
AI has been designed to work like magic, thanks to the bundled technology of code, data pipelines and deep neural networks that work secretly inside the machine. What looks like a simple recommendation is actually the output of millions of calculations, and the intricate output of pattern recognition, inference and optimisation. AI is a wholesome technology stack comprising foundational AI models to retail-specific tools like Gen AI, ML and Predictive AI, which has reshaped e-commerce from the ground up. GenAI like OpenAI’s GPT model enables creation of custom fashion looks from a selfie by blending style datasets, personal cues and prompt engineering to generate fashion visuals that resonates with the user. On the other hand, ML tracks what shopper clicks, skips, saves and shares, and then uses that data to continuously refine shopper’s shopping journey, eventually resulting in a hyper-personal experience. This experience does not require filling out a profile. Unlike GenAI and ML, Predictive AI needs no input. It rather anticipates shopper’s next action, surfacing products before he even searches for them, thereby reducing decision fatigue and shortens the path to purchase. Together, they have turned simple browsing into intelligent discovery.
In addition to what one types, AI is sensitive to what one sees and says too because of visual AI, voice interfaces and multimodal commerce. Visual AI allows users to shop the look as in the uploaded picture instantly by identifying clothing items, finding matches and recommending complementary pieces. Amazon and Pinterest are known names that have already implemented visual search. While voice AI interfaces like Amazon Alexa, Google Assistant and Apple Siri are becoming transactional, the multimodal AI combines text, images and voice to create a fully conversational, visual shopping experiences.
Thus, leveraging the power and capabilities of AI technology and the tools which can be carved out of it, many fashion brands and ancillary support services have created unique shopping experience for the shoppers. Some of the world’s top players in the segment have designed AI-powered services and solutions which are worth mentioning.
AI use cases
Amazon Price Optimiser solution is a tool that adjusts product prices several times a day, and this has led to a 5 per cent increase in sales and 2 per cent improvement in profits for Amazon. This solution has provided Amazon a competitive edge. While maximising profits, the Price Optimiser takes into account factors like customer demand, competitor pricing, sales volume and product availability.
Amazon uses GenAI to create personalised recommendations. The e-commerce giant is able to customise its homepage for each customer. It uses AI advanced analytics to collect transaction and historical sales data, along with information on their purchasing behaviour, preferences, wishlists and items in their cart. It gains valuable insights into customers’ preferences by analysing historical and real-time data, which allows Amazon to analyse transaction patterns and create highly personalised marketing campaigns. This enhances the overall customer experience and satisfaction levels. No wonder that McKinsey reports of 35 per cent of Amazon purchases being driven by personalised recommendations.
Likewise, Zalando uses complex AI algorithms to analyse vast amount of valuable customer data, their past purchases, browsing behaviour and saved items. This enables the fashion platform to personalise search results for each customer and display more products that are most likely to interest them based on their unique preferences. Additionally, it uses dynamic filters which evolve based on customer behaviours. For instance, when a user consistently searches particular size or colour filters, the same are pre-selected or highlighted in his future searches. In addition to keywords, its search bar also understands natural language (NL) queries. If a customer describes desired styles, materials or even occasions, the AI can interpret the intent and recommend relevant product suggestions.
Using ML and AI algorithms, ASOS offers a Style Match feature—a visual search technology, on its app. When the customer snaps a photo of an item or uploads an image from his library and initiates the search process, the visual information such as colour and patterns are matched with catalogue images so that personalised recommendations can be made.
H&M has implemented ‘Cherry’— an AI software that creates product descriptions for H&M’s online store. The system generates descriptions by images of clothing items and using NL processing, which are then reviewed and edited humanly. This has helped Swedish fashion brand streamline its content creation process by providing consistent and accurate product descriptions for its customers.
eBay ShopBot is an online shopping chatbot, a virtual assistant, that quickly responds to shoppers’ queries by providing instant replies, eliminating tiresome scrolling through eBay or ticking boxes. It offers friendly conversations and direct links to products customers are interested in.
However, in exuberance of fast evolving AI-shopping space, one must not overlook the perspective of target audience i.e. consumer, for which all these services and solutions are being implemented. Therefore, it is equally important to take into account how consumer sees AI.
Consumer perspective
Consumers are excited but also wary of AI at the same time. A Pew survey found four-fifth of consumers worry that AI companies use their data in ways they did not intend. Likewise, KPMG found that 63 per cent shoppers fear GenAI could compromise their privacy, while two in five (around 40 per cent) are hesitant about GenAI analysing their personal online data. These findings point at consumers’ expectation of an ‘ethical’ AI that understands them more and track them less. The desired ethical AI need to have transparent on-boarding of a consumer, validated by a visible consent and acceptance of EULA (End User License Agreement) before AI comes into play; no dark patterns marked by clear CTAs (Call-To-Actions) and no manipulation of choices; and, on-device processing that reduce reliance on central data collection and keep personal data more secure. Ethical AI must not work on biased data or stereotype the preferences such as defaulting to western fashion styles for all body types or assuming and recommending certain colours or styles for particular skin tones and body shapes. These biases usually emerge when training datasets lack diversity—a technical drawback, while using Gen AI for visual personalisation. In simple words, AI should celebrate individuality and not conformity. Ethical AI needs to be emotionally intelligent too, providing user experience (UX) in the form of right product for the right person with timing, tone and discretion.
This indicates that consumers expect AI to be not only smart but also transparent, respectful, culturally aware and adaptable to their evolving identity. They expect an environment of trust, wherein ethics is a default feature and not compelled by any compliance. By doing so, brand loyalty can achieve higher scores which no algorithm can fake.
AI shopping is growing fast and the platforms in this space will have to anticipate how consumers will want to shop even before shoppers know it themselves. Moving beyond text-only search, the future of shopping is going to be multimodal where shoppers can describe a look in voice, upload mood board or selfie, swipe through AI-generated visual interpretations or add looks directly to their cart or lock screen. AI shopping is going to be all about interactivity, and virtual try-on features will become more mainstream, with AR (Augmented Reality) enabling real-time preview of apparel and accessories or home décor shopping using camera overlays to visualise products in real-world environments. AI will be even more relevant in post-purchase journeys: returns will be managed more efficiently with predictive sizing and expectation-setting, customer service will elevate to another level leveraging GenAI-powered chatbots offering contextual and helpful replies, or loyalty programmes being driven by behaviour-based nudges and re-engagement triggers. To cater to a diverse market comprising different languages, climates, cultures and fashion aspirations, AI is expected to expand hyper-localise offers, understand related and trending fashion styles and use seasonal demand fluctuations while updating shopping feeds. A key focus area, voice-first commerce is destined to become a crucial component of AI shopping experience—just orate your desire and AI will work to search, recommend, provide for and even deliver your need.

AI is reshaping retail by offering personalised recommendations, smart search, virtual try-ons, and conversational shopping. It optimises both B2C experiences—making them seamless and enjoyable—and B2B processes such as demand forecasting, inventory optimisation, and supply chain management.
Unlike traditional keyword-focused e-commerce, AI shopping is conversational and intuitive. It remembers user preferences, refines recommendations in real time, applies discounts automatically, and streamlines checkout within a single interface—reducing browsing fatigue and enhancing loyalty.
While consumers value personalisation, they are wary of privacy risks. Surveys show concerns about data misuse, lack of consent, and algorithmic bias. Shoppers expect ethical AI—transparent, diverse, culturally aware, and emotionally intelligent—ensuring trust and fair treatment in recommendations.
Global players like Amazon, Zalando, ASOS, H&M, and eBay are pioneers. From dynamic price optimisation to visual search, personalised homepages, and AI-generated product descriptions, these innovations demonstrate how AI enhances customer journeys and strengthens brand competitiveness.
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