What is an AI Personal Shopper?

An “AI personal shopper” is technology that uses AI and machine learning to help customers find products in digital spaces. It generally manifests as a conversational chatbot or voice assistant (think: Apple’s Siri, Amazon’s Alexa, and Google Assistant) that is a combination of search engine and chatbot. 

AI personal shoppers use a combination of various AI technologies and Data to provide personalized product recommendations, guided assistance, and customer support.

How does AI personal shopper technology work?

There’s a lot to the secret sauce that makes AI personal shoppers help shoppers find products. They work by: 

1. Using (lots of) data as the foundation for personalization

AI personal shoppers gather data from multiple sources, including browsing history, search queries, purchase behavior, cart activity, and external interactions (e.g., engagement with marketing emails, chatbots, call centers, etc). The data is centralized in a single location to create a comprehensive customer profile. 

2. Interpreting patterns and predicting behavior

Machine learning algorithms analyze the collected and unified data, then identify patterns and predict what a customer is likely to want. For example, if Jill frequently looks at workwear for gardeners, the AI personal shopper may show her new overall styles that just came in or gardening gloves that are on sale. Predictive analytics also help forecast future buying behaviors, such as when Jill’s likely to abandon her cart or be interested in the new gardening sheers that just dropped.  

3. Making recommendations

An AI personal shopper is basically a recommendation engine. It uses techniques like collaborative filtering that suggests products based on what similar users have purchased and content-based filtering that recommends items with similar attributes to those a user has shown interest in. This is an incredibly effective way to drive purchases. Amazon’s AI-powered recommendation engine, for example, is estimated to drive about 35% of the company’s total sales. 

4. Adapting experiences in real time

To be effective, AI assistants need to adjust to what a shopper wants in the moment. These tools can adapt recommendations in real time as users interact with a site or app. If Jill shifts from looking at work clothing to gardening tools, the system immediately updates its suggestions. This process is dynamic and runs on a steady diet of data which is processed by rapid machine learning algorithms. 

5. Understanding and chatting with humans

AI personal shoppers use natural language processing (NLP) to understand what shoppers are asking for in their own words. They use generative AI (GenAI) to respond naturally to queries and searches. This is the technology that makes it possible for Jill to type or say something like “show me waterproof gardening shoes under$50” and get relevant results. These systems can interpret questions, make sense of context, and respond in a way that feels natural. 

6. Getting better with every interaction

AI personal shoppers are designed to learn from the data they ingest. That means they get better over time. Clicks, purchases, abandoned carts, and search queries are all fodder for the machine, giving it the information it needs to better understand what shoppers want. When Jill ignores recommendations for watering cans, but clicks on protective sun gear, the system takes note and adjusts its recommendations. This is how AI personal shoppers become good at showing customers the products they’re most likely to buy.

AI Personal Shopper Examples

Monetate has some powerful examples of personal shopper technology in action from our portfolio of client success stories. Our personalization platform uses the same technology that AI personal shoppers use to match products to shoppers. 

Though these aren’t AI chatbots, they demonstrate how AI personal shopper technology like product recommendations and real-time personalization helps people find what they need.

1: Highbourne Group’s Smart Product Recommendations

Highbourne Group, a market leader in trade tools and electrical products, implemented AI-powered product recommendations across their website and brand-specific landing pages. It was a move that resulted in 52% year-over-year growth in recommendation-driven sales. Highbourne also achieved an impressive 18.3% conversion rate from recommendation interactions, with 1.8% of total website purchases directly attributed to these smart suggestions.

2: Nespresso’s Interactive Coffee Quiz

Nespresso created an AI-powered Coffee Quiz using Monetate’s Ecommerce Product Finder to help customers discover new coffees based on their preferences. By asking a few questions about intensity, flavor notes, and machine type, Nespresso delivered tailored recommendations that increased conversion rates by 18%. Over 50,000 customers have interacted with the quiz, with a 42% completion rate, proving that personalized product discovery drives both engagement and sales.

3. Helly Hansen’s Context-Aware Recommendations

This outdoor apparel brand uses AI to deliver personalized shopping experiences across 19 countries and 7 languages. They use Monetate Orchid AI intelligence engine to analyze data points like shopper location, weather conditions, browsing history, and past purchases to recommend relevant products. This smart approach increased revenue per session by 28% in the men’s section of the site and generated 50% more clicks than their previous recommendation system.

Benefits of AI Personal Shoppers

Shoppers want speed, something AI is exceptionally good at. According to Walmart’s 2025 Retail Rewired Report, 69% of consumers say the speed of their shopping journey is a top priority when choosing where to shop. AI personal shoppers make virtual shopping trips faster, but also more personal and successful. Top benefits of AI personal shoppers include:

• Tailoring recommendations to each person within the context of a given shopping journey. This helps shoppers cut through clutter and pinpoint what they want, fast.
• Reducing decision fatigue. When AI is embedded into shopping assistants, search, and browsing experiences, it reduces choice overload by refining search results and guiding shoppers to the items that are most relevant to them.   
• Increasing shopper trust. One of the most surprising findings in the Walmart report is that 27% of shoppers now prefer AI recommendations over influencer endorsements—so AI-driven recommendations are finding their stride alongside human recommendations.  
• Being available 24/7, so shoppers get support and suggestions whenever they need them.
• Providing scalable, cost-efficient personalization options for retailers, allowing them to meet customer expectations with less hassle (and resources). This also frees up marketing, sales, and customer service teams to do more high-value work.

Industry-Specific Use Cases

There are an abundance of use cases for AI personal shoppers and the personalization technology that drives them. AI shopping tools can be customized and applied to different industries to help customers get from the tire kicking phase to the final point of purchase. Here’s how different retail sectors are putting this technology to work:

1. Outfit Building in Fashion Retail

AI personal shoppers can suggest complete outfits based on a shopper’s preferences, past purchases, current trends, your available inventory, the season, the location, and much more. Let’s say it’s May and Sarah, located in New York, is looking at jeans. AI tools like Monetate’s Dynamic Product Bundling feature might recommend pairing the jeans with a peasant blouse, sandals, and a lightweight bag to complete the look. If Sarah’s looking at jeans in October, the recommendations  may include a wear, light jacket, boots, and a weatherproof bag.

2. Recommending Complementary Electronics Products

Let’s swap Sarah out for Natasha, a tech engineer who needs a powerful laptop for remote work. When Natasha adds the laptop to her cart, AI-powered product recommendations can display relevant accessories—like a case, mouse, or extended warranty—based on what similar customers have purchased. This is an effective way to increase average order value while providing a useful service to customers by making it easy for them to add important accessories to their order. 

3. Recurring Grocery Order Optimization

Chris is a busy dad who orders groceries online every week—he has zero time to spare between his full-time job, his kids’ packed schedule, and the endless chores he needs to address every weekend. AI personal shoppers make Chris’s life easier by analyzing past purchases and automatically suggested a recurring order for staples like cereal, milk, bread, eggs, and coffee. The system also knows that Chris consistently buys certain snacks and drinks for the kids, and prompts him to add these items to his recurring order ( saving him an extra trip to the store). 

4. Cross-selling & Upselling in Checkout Flows

Kiki, a busy marketing professional, needs a new smartphone. She’s added it to her shopping cart and is nearly ready to finish her purchase when she sees recommendations for a screen protector, a pair of wireless earbuds, and a fast charger—all tailored to the specific phone model she’s purchasing. As a loyal customer, she also gets an offer of free expedited shipping, plus a discount on her next purchase. These motivate Kiki to finish her transaction and make her more likely to return to this same store the next time she needs a phone upgrade. 

AI Personal Shopper vs. Traditional Personalization

AI personal shoppers—and the various AI-driven features that support hyper-personalized experiences—can tailor digital shopping experiences to each shopper. This makes them much more relevant than traditional rule-based personalization. 

With rule-based personalization, customers are grouped into broad categories (e.g., segments) based on factors like demographics or past purchase behavior. Think about Chris, the busy dad from our earlier example. With rules-based segmentation, he might be grouped into a broad “parents” category and shown generic ads for diapers even though he has older kids.

This doesn’t happen with AI personal shoppers because experiences are tailored to each shopper in real time.  An AI personal shopper would recognize that Chris has consistently purchased Goldfish crackers and Lunchables as well as eco-friendly cleaning supplies. Chris gets recommendations for the snacks he already buys plus similar snacks his kids like and sustainable products that align with his values. 

AI systems use dynamic, continuous learning models. They constantly refine recommendations based on new data. Static, rule-based systems simply can’t match that level of relevance.

Challenges & Considerations

While AI personal shoppers offer tremendous potential, there are some challenges to consider when implementing the technology. Data privacy is the big one, here. You need to be transparent about how you collect, use, and protect customer data. 

According to Walmart’s Retail Rewired Report, data privacy is non-negotiable for shoppers, who expect brands to provide transparency, minimal data collection, and clear controls over their information.

Trust and transparency go hand in hand with privacy. Nearly half of respondents in the Walmart report were hesitant to let an AI agent handle an entire shopping trip. This is especially true for high-stakes or emotionally significant purchases. Shoppers want to know how recommendations are made. They want to feel confident that AI is working in their best interest.

Algorithm bias is another key consideration. If AI models are trained on incomplete or skewed data, they can reinforce existing biases and deliver less relevant—or even unfair—results. You can get ahead of this by monitoring and auditing AI systems regularly.

Finally, AI personal shoppers are only as good as the data they’re fed. Clean, accurate, and up-to-date data is essential for delivering relevant recommendations. If the data is messy or outdated, the customer experience will suffer.