The future of shopping: AI knows what you want before you do

Last week, my younger cousin and I were in the Albert Heijn snack aisle trying to choose between two different brands of crackers. The 15-year-old immediately pulled out a smartphone and said, “Hey AI, which one of these should I buy?” AI quickly suggested a cracker that he would most likely prefer based on his earlier prompts. This is the modern definition of shopping.

These days, turning to artificial intelligence in moments of uncertainty has become second nature. From catching up on the news and verifying facts to crunching numbers, planning outfits, picking a movie, or even deciding what to cook for dinner… AI is our go-to guide, offering reliable answers in the blink of an eye. So, it’s only natural that ecommerce is also harnessing the power of AI to enhance the shopping experience.

What exactly are “enhanced” shopping experiences? Think personalization, but to the extreme. Nearly three out of four (73%) of your customers expect you to understand their individual needs and preferences, while 62% want you to anticipate and remember them. Managing this level of personalization across millions of in-store and online shoppers can quickly become overwhelming. That’s where artificial intelligence-powered shopping steps in, helping you deliver seamless, data-driven customer experiences with efficiency and ease.

So, what is AI shopping?

AI shopping refers to the use of artificial intelligence to enhance, personalize, and automate the shopping journey. It spans everything from search suggestions and product recommendations to conversational commerce via chatbots and voice assistants.

The recent move by OpenAI to integrate shopping capabilities into ChatGPT marks a turning point. Through partnerships with platforms like Shopify, Instacart, and Klarna, users can now discover products, compare prices, and make informed decisions—all within an AI-powered conversation. ChatGPT doesn’t just answer product-related questions—it leverages product data feeds in real-time to serve personalized recommendations based on user intent.

Source

This isn't just a new interface—it's a new way to shop. Instead of navigating menus and filters, users can ask, "What’s a good birthday gift for a 10-year-old who loves science?" or "Find me a vegan leather backpack under $100." AI understands context, pulls live data from partner feeds, and responds conversationally, creating a more natural, frictionless buying experience.

For ecommerce brands, this shift means product data must be accurate, enriched, and feed-ready. It’s no longer enough to simply list products online; those products need to be structured in a way that AI can read, interpret, and recommend seamlessly. Today, around 84% of ecommerce businesses prioritize AI technology in their operations.

Amazon, the ecommerce company with the highest revenue globally, has been utilizing AI tools to enhance its product selection and optimize logistics based on customer demand, creating a valuable user experience. By integrating artificial intelligence in ecommerce, Amazon has seen a 25% advancement in revenue generation, cost reduction, and customer satisfaction.

How AI predicts what customers want

AI doesn’t operate on guesswork; it learns from data. Through machine learning and deep learning algorithms, AI systems can analyze vast datasets to anticipate what shoppers are likely to want next. Here’s how:

1. Behavioral analysis

AI tracks user behavior across sessions—what they click, how long they stay, what they abandon, and what they return to. These signals help AI understand preferences in real time. By mapping out digital shopping behavior, AI can infer whether a user is price-sensitive, brand-loyal, or exploring alternatives, and adjust recommendations accordingly.

2. Historical purchase patterns

By analyzing previous purchases (both individual and collective), AI can surface recommendations aligned with seasonal trends, usage cycles, or complementary needs. If a customer bought hiking boots last month, AI may now recommend moisture-wicking socks, trail gear, or guided treks—all rooted in learned context.

3. Search and query understanding

Natural language processing (NLP) enables AI to interpret user queries, even when they’re vague or conversational. For example, a prompt like “Show me something cozy for my home office” might translate into throw blankets, slippers, or ergonomic chairs, depending on context and past behavior. This allows AI to bridge the gap between human language and structured product data, ensuring users see relevant items even from open-ended or typo-prone inputs.

4. Product feed matching

Well-structured product data feeds fuel these predictions. When feeds contain detailed attributes like description, color, style, price, stock levels, and customer reviews, AI has a rich foundation to match user intent with relevant products. Inaccurate or sparse data can derail recommendations, while enriched feeds empower AI to serve tailored results across various digital touchpoints, as seen below.'

Source

5. Collaborative filtering and user segmentation

AI can group similar shoppers into cohorts and predict preferences based on group behavior, much like how streaming services recommend shows based on what "people like you" are watching. These insights help brands build micro-targeted campaigns and product bundles that resonate with specific audience segments. This prediction power is only as strong as the data feeding it. For brands and retailers, that means curating high-quality, enriched, and channel-ready product content is not just good practice but essential for AI success.

The impact of AI shopping on the customer experience

AI shopping directly affects how consumers interact with your brand and how they feel during and after the buying journey. Here’s how it elevates the experience:

1. Hyper-personalized discovery

Shoppers no longer browse through generic listings. Instead, they see curated suggestions based on their style, budget, and behavior. It’s like having a personal shopper available 24/7. This makes customers feel seen and understood, fostering loyalty and increasing the chances of repeat purchases.

Source

2. Frictionless navigation

AI-powered search tools reduce clicks and time-to-cart by instantly serving up what customers are looking for, even before they finish typing. AI shopping minimizes frustration, speeds up decision-making, and helps customers reach the checkout page faster.

Source

3. Conversational commerce

AI chatbots and virtual shopping assistants support customers in real time by helping them find the right products, answering FAQs, suggesting sizes, and guiding them through checkout. This not only lowers bounce rates but also enhances satisfaction by making the shopping experience seamless.

By offering instant, personalized help, they turn your online store into a reliable, 24/7 support hub that builds trust and confidence. Here’s one quick example from Sephora.

Source

4. Smarter promotions

Dynamic pricing and personalized discounts can be tailored to individual behavior. Someone who’s a frequent buyer might get a loyalty-based incentive, while a cart abandoner might see a timely discount to re-engage. AI ensures promotions feel like value-adds, not gimmicks, leading to better engagement and reduced churn.

5. Omnichannel continuity

Whether a shopper starts browsing on mobile, switches to desktop, or walks into a store, AI can maintain context and ensure consistency in recommendations and offers. This seamless omnichannel experience builds brand credibility and removes the silos between online and offline shopping behaviors.

How to introduce your ecommerce business to AI shopping

With the right tools and a solid foundation of product data, your business can start integrating AI into the shopping experience and drive real, measurable results.

Step 1: Optimize your product feed

A clean, enriched, and categorized product feed is the backbone of AI shopping. Ensure your product data includes:

• Complete titles and descriptions
• Standardized attributes (e.g., size, color, material)
• High-quality images and videos
• Customer reviews and ratings
• Availability and pricing details

Platforms like Productsup help you centralize and standardize product feeds, making it easy to syndicate them to AI tools, marketplaces, search engines, and social platforms.

Step 2: Integrate AI tools and APIs

Start small with AI-driven plugins or APIs, such as:

• Product recommendation engines (like Nosto, Dynamic Yield)
• AI-powered site search tools (like Algolia or Searchspring)
• Chatbots and conversational tools (like Drift, Zendesk, or even ChatGPT APIs)

Ensure these tools can pull real-time data from your feed or PIM to serve accurate results.

Step 3: Use behavioral data to train models

The product content journey is a continuous loop, with data flowing back and forth between suppliers and buyers. Gather and leverage consumer behavioral data from your ecommerce platform, like:

• Most viewed products
• Time spent on product pages
• Cart abandonment triggers
• Search terms and queries

This information can be fed back into AI models to improve targeting and recommendation accuracy.

Step 4: Test, refine, and personalize

Use A/B testing to evaluate the impact of AI-driven features. Track metrics like:

• Conversion rates
• Average order value
• Bounce rates
• Customer retention Personalize based on real data—not assumptions—and iterate continuously.

Step 5: Build a resilient tech stack with scalable infrastructure

As AI shopping grows, your backend needs to keep up. A scalable feed management and syndication platform like Productsup allows you to manage, enrich, and distribute product content at scale, across all channels, including those driven by AI.

It offers direct connections to all types of sources, such as Google, SAP Commerce Cloud, Pimcore, Akeneo, as well as end channels, including social media platforms like TikTok and Reddit, and digital product passport providers like Protokol.

Final thoughts

From predictive recommendations to AI chatbots that can handle your entire shopping experience, artificial intelligence is enabling brands to meet the rising expectations of modern consumers. But here’s the catch: AI is only as smart as the data it’s fed. If your product information is scattered, outdated, or incomplete, even the most advanced AI can’t deliver relevant experiences. That’s why structuring and optimizing your product feeds is not just important—it’s foundational.

Whether you’re a retailer trying to compete with giants or a brand looking to meet shoppers where they are, AI shopping opens new possibilities. And with the right tools, you can turn complex product data into personalized, AI-ready experiences that convert. Because in the future of shopping, success belongs to those who can predict what the customer wants, before they even ask. Book a demo with us to see how we can help you turn that future into reality.

Explore more related resources here:

• [Overview] Productsup AI and automation features
• [Webinar] From bad bots to AI agents: Navigating the future of web traffic
• [Guide] The state of smart commerce: AI’s impact on buying and selling