The numbers tell a compelling story. Artificial intelligence (AI) is becoming essential in ecommerce. Experts expect the global AI-powered ecommerce market to hit $8.65 billion in 2025 and climb to $22.6 billion by 2032—a 14.6% annual growth rate that shows no signs of slowing.
AI models can’t yet replicate human intelligence yet, but they’re getting remarkably good at mimicking how we think and respond. Take ecommerce chatbots that read a customer’s message and provide detailed, helpful support almost instantly.
These AI systems are doing tasks that used to require human attention. You’ve probably already interacted with one while shopping online or even checking your email without even realizing it.
Here’s how different types of AI models—from simple to sophisticated—are transforming ecommerce and helping businesses like yours serve customers better.
What is an AI model?
An AI model is a computer program trained to spot patterns in data. Think of it as software that learns to think like humans do—through a combination of machine learning, deep learning, natural language processing (NLP) and statistical modeling.
Computer scientists train these models using input and output data. Input data is the raw information AI systems analyze. Output data becomes the predictions or classifications the model makes based on what it learned.
Take classification models, for example. These are trained to sort data into specific categories. An email spam filter analyzes incoming messages and decides whether they’re legitimate or junk based on features like sender address and message content. The spam classification depends on these independent variables—the model only flags emails when it detects suspicious patterns.
But AI models can handle much more complex decisions. An autonomous vehicle processes multiple data streams to decide whether to brake, accelerate, or change lanes based on road conditions, traffic patterns, and safety protocols.
How do AI models work?
AI models process data through mathematical formulas called algorithms to learn patterns and relationships. This lets them make predictions or decisions without being explicitly programmed for every scenario. Most models function as artificial neural networks—layers of interconnected nodes (like neurons in your brain) that process input data, extract features and generate predictions.
Training
Training means feeding data into an AI model so it can learn. During this process, the model constantly adjusts itself based on the information it receives.
Think of a student preparing for an exam. The more practice problems they solve, the better they understand the material. Similarly, the more data an AI model processes, the better it gets at recognizing patterns and making accurate predictions.
Applications
Once training is complete, data scientists deploy AI models for real-world use. This might mean integrating a model into software (like recommendation systems on shopping websites) or using it to analyze data in real time (such as fraud detection systems). Even after deployment, scientists can continue training these models with new data.
“Think small, iterate fast, then scale,” advises Alex Pilon, senior developer at Shopify. “When you’re working with big data, testing your prompts on specific cases or running AI-based processes on small batches makes it easier to spot-check and battle-test your process.”
This approach works especially well with AI machine learning models. As they encounter new data, these models refine their understanding and adapt to changing circumstances.
Types of AI models used in ecommerce
Ecommerce stores use many different AI models and tools, each designed for specific tasks or data types. Here’s how the main categories break down:
Machine learning models
Machine learning models learn from labeled data—information where each data point already has a known answer or classification. This is called supervised learning. These models identify patterns in data and use them to make predictions on new information.
A few of the most common types include:
- Linear regression model: Companies use these for continuous predictions and forecasting, like estimating product demand based on independent variables like seasonality or marketing spend.
- Logistic regression model: Perfect for binary classification tasks, such as predicting whether a customer will complete a purchase or abandon their cart.
- Decision trees: These models make predictions by following a tree-like structure of yes/no questions about the data. They’re particularly useful for customer churn prediction—helping you understand which customers might stop buying from you.
- Support vector machines (SVM): These powerful models handle classification tasks with complex, high-dimensional data by finding clear separation lines between different categories.
- Linear discriminant analysis (LDA): Similar to SVMs but simpler, LDA works best with well-understood data, while SVMs offer more flexibility for complex data analysis.
- K-nearest neighbors (KNN): This model classifies data points based on similarity to their closest neighbors in training data. Companies often use KNN for image recognition tasks.
- Random forest: This ensemble model combines multiple decision trees, improving accuracy and reducing the risk of becoming too specialized to training data.
Ecommerce platforms use machine learning models for personalized product recommendations, dynamic pricing, and customer segmentation. By analyzing user behavior, purchase history, and browsing patterns, these models help you tailor shopping experiences to individual preferences—leading to higher conversion rates and happier customers.
Deep learning models
Deep learning models are a specialized type of machine learning inspired by how the brain works. These neural networks contain multiple layers, letting them learn complex patterns from massive amounts of data.
These models power advanced ecommerce capabilities like fraud detection, chatbot automation, and demand forecasting. They can process vast amounts of unstructured data—customer reviews, clickstream data, social media mentions—to uncover hidden patterns and improve your business decisions in real time.
Foundation models and LLMs in ecommerce
Large, pretrained AI models like foundation models and large language models (LLMs) are transforming ecommerce by enabling more intelligent, scalable, and personalized digital experiences. Trained on enormous datasets, these models can handle a wide range of language and vision tasks with minimal additional training. In ecommerce, they power automated product description writing (Shopify Magic can help with this), intelligent customer support chatbots, and personalized marketing content.
Here are the most important deep learning models for ecommerce:
- Convolutional neural networks (CNNs): These excel at image and video recognition. They’re designed to understand spatial relationships between pixels, making them perfect for visual search and product categorization.
- Recurrent neural networks (RNNs): Built for sequential data like text or speech, these models process information by considering order and context—making them ideal for analyzing customer service conversations.
- Generative adversarial networks (GANs): These involve two competing neural networks: one creates new data while the other tries to distinguish real from generated content. GANs power tasks like image generation, style transfer, and data augmentation.
- Large language models (LLMs): These advanced models trained on massive text datasets can generate content, translate languages, create different kinds of creative material, and answer questions intelligently.
- Foundation models: These versatile AI models are designed to handle a wide range of tasks by learning foundational patterns from vast amounts of data, making them especially effective for natural language processing and video analysis.
Choosing the right AI models for ecommerce
As AI becomes more accessible, ecommerce businesses of all sizes can use specialized models to tackle specific challenges. “Lowering opportunity cost means more people can participate in the economy,” notes Alex. “Tech has had a high cost of entry for some time. However, AI really opens the door for anyone of any technical background to bring an idea to life.”
The challenge is finding the right AI model to move the needle on your ecommerce goals. Here’s how to match AI models to specific use cases:
- Convolutional neural networks (CNNs): Perfect for visual search and image recognition. CNNs help customers find products by uploading photos and improve product tagging and categorization at scale.
- Large language models (LLMs): Best for content generation—from writing product descriptions and SEO metadata to automating customer service with conversational chatbots.
- Recommendation engines (collaborative filtering, matrix factorization, etc.): Power personalized product recommendations by analyzing browsing behavior, purchase history, and customer preferences.
- Generative adversarial networks (GANs): Useful for creating synthetic media, like generating new product images or enabling virtual try-ons.
- Decision trees and random forests: Effective for customer segmentation, churn prediction, and dynamic pricing strategies based on user behavior and demographics.
AI models in ecommerce applications
AI models are transforming every part of the ecommerce customer journey. Here’s how businesses are using AI to boost sales, cut costs, and create better shopping experiences.
Personalization and recommendation engines
Smart recommendation systems analyze what customers browse, buy, and click to serve up products they actually want. These AI models—collaborative filtering, decision trees, and neural networks—learn from every interaction, making suggestions more accurate over time.
Take Parachute, a home essentials brand that transformed its customer experience using Shopify’s headless setup and HubSpot integration. The company built detailed customer profiles that let them send personalized messages referencing past conversations and suggesting complementary products. The result? A five-times jump in buy-online-pickup-in-store revenue and more than $1 million saved on operations.
“The data we access by having everything unified through Shopify lets us truly understand our customers and tailor communications to their needs,” says Ariel Kaye, Parachute’s founder. “This personalization sets us apart in a crowded market.”
For smaller teams, AI levels the playing field. “AI will reduce the cost of entry to marketing and ad campaigns,” Alex explains. “Having an AI assistant help you set up bidding strategies, landing-page experiments, and conversion tracking is massively helpful.”
Chatbots and virtual shopping assistants
Modern AI chatbots don’t just answer basic questions—they guide customers through complex purchases, understand context, and feel genuinely helpful. Powered by large language models and natural language processing, these systems handle customer support at scale while maintaining a personal touch.
The latest AI agents go far beyond simple chatbots. “Purpose-built agents are like a specialized configuration of AI,” Alex explains. “You have system prompts tuned for specific tasks, plus the agent connects to tools like product knowledge bases or your business training materials. These additional pieces let us create highly specialized agents that actually understand your business.”
Peter Sheppard Footwear, a luxury retailer, integrated chatbots into its Shopify site to match the high-touch service customers expect from their physical stores. The upgrade improved its digital shopping experience and helped drive a 30% revenue increase.
Inventory and pricing optimization
AI takes the guesswork out of inventory management and pricing. Machine learning models analyze sales history, seasonal patterns, and customer demand to automatically adjust stock levels and prices. Advanced systems respond to real-time signals—if demand spikes for a product, prices adjust instantly.
Incu, a Shopify merchant curating cutting-edge fashion across 10 Australian retail locations, shows how powerful AI tools for ecommerce can be. By implementing AI-driven automation for inventory management, the company streamlined operations and achieved a 300% year-over-year sales increase.
Fraud detection and prevention
AI fraud detection detection systems work like digital security guards, scanning every transaction for suspicious patterns. These models—anomaly detection algorithms and classification systems—analyze transaction patterns, user behavior, and device data to catch fraud in real time. They learn continuously, staying ahead of both known scams and new tactics fraudsters develop.
Privacy and data best practices for AI models
AI models in ecommerce depend on customer data—browsing history, purchase behavior, engagement patterns—to work effectively. But using this data comes with serious privacy responsibilities. Get it wrong, and you risk losing customer trust and facing regulatory penalties.
Here’s how to use AI responsibly while protecting your customers:
- Use anonymized and aggregated data: Train AI models on datasets that remove personally identifiable information whenever possible. This protects individual privacy while still letting models spot useful patterns. For example, you can analyze purchasing trends by age group without storing specific customer birthdates.
- Be transparent about data use: Tell customers exactly what data you collect, how you use it, and how it improves their experience. Give them control—let them opt into personalized features or turn them off entirely. Clear communication builds trust, while hidden data practices destroy it.
- Choose AI tools with built-in privacy protection: Platforms like Shopify Magic and Shopify Flow operate within Shopify’s privacy and security framework, letting you use AI responsibly without managing compliance yourself.
- Control data access strictly: Only essential systems and team members should access customer data. Avoid sharing information with third parties unless absolutely necessary—and always disclose when you do.
- Stay compliant with regulations: Follow privacy laws like GDPR, CCPA, and Australia’s Privacy Act. These frameworks guide data handling, consent, and consumer rights. Non-compliance isn’t just risky—it’s expensive.
Future trends in AI models for ecommerce
AI capabilities are advancing rapidly, and smart ecommerce businesses are adopting new models to stay competitive. Seventy-eight percent of organizations were using AI for at least one business function as of July 2024—and that number keeps climbing.
Here are the AI trends that will shape ecommerce’s future:
- Multimodal models: These AI systems process multiple data types—text, images, video—simultaneously, creating richer shopping experiences. Imagine customers uploading a photo of shoes they like and instantly getting style matches, reviews, and fit guidance in one interface.
- Retrieval-augmented generation (RAG): This technology combines generative AI with real-time information retrieval. Ecommerce chatbots can now generate current, relevant answers by pulling from live product catalogs, help centers, and policy documents instead of relying on potentially outdated training data.
- Edge AI for retail devices: As AI processing moves to mobile devices, in-store sensors, and smart mirrors, brands will be able to offer faster, more personalized interactions without depending on cloud processing. This means instant responses and better privacy protection.
- Real-time hyper-personalization: Future recommendation engines will learn from customer interactions as they happen, adapting instantly to deliver relevant offers and content that feels truly one-to-one. No more waiting for batch processing or overnight updates.
- Emotion and sentiment analysis: Advanced NLP models now detect tone and emotional context in customer feedback and chatbot conversations. This lets you tailor responses, escalate concerns appropriately, and optimize messaging for different customer moods and situations.
AI models FAQ
How are AI models used in business?
AI models automate repetitive tasks, improve decision-making, enhance customer experiences, and optimize processes across marketing, finance, operations, and customer service. They help business owners work smarter, not harder.
What marketing tools use AI models?
Personalized recommendation engines, predictive analytics platforms, customer engagement chatbots, and sentiment analysis tools for social media monitoring all rely on AI models to deliver better marketing results.
Is ChatGPT an AI model?
Yes, ChatGPT is an AI model—specifically a large language model (LLM) developed by OpenAI that generates human-like text based on the prompts it receives.
Which AI models power product recommendation engines for ecommerce?
Product recommendation engines typically use machine learning models like collaborative filtering, content-based filtering, and hybrid approaches. Advanced systems employ deep learning models, including recurrent neural networks (RNNs) and transformers, to analyze user behavior, preferences, and purchase history for highly personalized suggestions.
How can I integrate AI models into my ecommerce store?
Start by identifying your goals—whether that’s product recommendations, chatbots, or inventory management. Then choose APIs, plug-ins, or cloud-based platforms like Shopify that offer pre-trained models or customizable AI solutions that work with your existing tools. Many platforms make integration straightforward without requiring deep technical expertise.
What AI models are used for fraud detection and security in ecommerce?
Fraud detection systems use supervised learning models like decision trees, random forests, and logistic regression for anomaly detection. Advanced systems also employ autoencoders and neural networks to identify suspicious patterns and prevent fraudulent transactions in real time.
How much data do I need before implementing AI models?
Data requirements vary by model complexity and task. Simple models can work with a few thousand data points, while sophisticated models may need tens of thousands or more. If you're starting with limited data, pre-trained models or third-party tools can help you implement AI effectively without waiting to collect massive datasets.
Which AI models offer the fastest ROI for small ecommerce businesses?
Recommendation engines, chatbots, and dynamic pricing models typically deliver the quickest returns. You can implement pre-trained models from platforms like OpenAI or Google Cloud with minimal data and setup time. These models boost conversion rates, improve customer service, and automate routine tasks—making them perfect for small businesses focused on efficiency and growth.
