AI models are the engines already powering the artificial intelligence tools you use to run your business. It’s the software trained to generate content, detect patterns, and make predictions.
Analysts predict artificial intelligence (AI) platform-driven ecommerce sales will exceed $144 billion by 2029. And with developers constantly updating their AI models, Alex Pilon, Shopify senior developer and AI advocate, says, “AI really opens the door for anyone with any technical background to bring an idea to fruition.”
If you’re curious about how AI models work, this guide covers the types of AI models businesses use, with practical use cases and insight on how Shopify’s AI models support store owners.
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 AI machine learning, deep learning, natural language processing (NLP), and statistical modeling.
AI models are different from rule-based programming. An AI model uses the data it’s given to “learn,” rather than follow predetermined rules.
AI models can’t fully replicate human intelligence yet, but they’re getting remarkably good at mimicking how they think and respond.
Ecommerce chatbots are an example. They read a customer’s message and provide detailed, helpful support almost instantly—so much so, Salesforce’s 2025 State of Service report projects 50% of all support tickets to be handled by AI by 2027.
How AI models differ from algorithms
Algorithms follow a predefined set of rules. They’re fixed; every trigger results in the same response.
AI models build on algorithms to learn patterns and relationships. It has a machine learning layer that applies logic, or learns from its own data. This is what allows AI models to make predictions or decisions without being explicitly programmed for every scenario.
Here’s the difference showing machine learning in an ecommerce context with product recommendations:
- An algorithm might display bestsellers based on the category a shopper is viewing.
- An AI model might use what they already know about that individual shopper to display a waterproof coat they predict they’ll be interested in.
How AI models learn
AI models build on their initial training data with user input. In ChatGPT, for example, there’s a toggle to “Improve the model for everyone.” If ticked, OpenAI says it may use your data to train AI models.
There are three components in play as AI models learn:
- Training. This involves feeding data into an AI model so it can learn. These datasets are vast: ChatGPT trained its first model on 300 billion words scraped from books and articles on the internet. The more data an AI model processes, the better it gets at recognizing patterns and making accurate predictions.
- Testing. Before releasing it to the public, data scientists test the model and fix errors to improve its knowledge. Claude, for example, recently trained its AI model Claude Opus 4.5 to block jailbreaks and harmful system prompting.
- Deployment. This means 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 deploying AI models, scientists can continue model training with new data. This is known as reinforcement learning from human feedback (RLHF).
“AI has human-like reasoning and decision-making skills, but it’s important to remember that at its core it is still an algorithm,” says Alex. “It can produce reasoning and decisions that look perfectly reasonable on the surface yet are actually wrong in a specific context.”
Types of AI models every business owner should know
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
Attentive’s 2026 report shows 68% of shoppers want brands to learn from their shopping habits over time. Almost three-quarters are more likely to buy when suggestions feel relevant.
Machine learning models help ecommerce brands do this. They identify patterns in data—like user behavior, purchase history, and browsing patterns—and use them to make predictions on new information.
There are three ways machine learning happens:
- Supervised learning. The AI model is trained on labeled data where inputs are paired with the correct output. For example, an AI fraud detection tool would learn based on uploaded transactions labeled as fraud or legitimate.
- Unsupervised learning. The model finds patterns in unlabeled data. The AI fraud detection tool flags outliers in new data, like orders placed from different accounts but shipping to the same address.
- Reinforcement learning. The model continues to learn based on feedback on whether it was correct. If the AI fraud tool flags a transaction as fraud that was actually legitimate, for example, it might be less reliant on the signal it detected in future transactions.
Deep learning and neural networks
Deep learning models are a specialized type of machine learning inspired by how the brain works. They use artificial neural networks—layers of interconnected nodes (like neurons in your brain) that process input data, extract features, and generate predictions.
In ecommerce, deep learning models can process vast amounts of unstructured data—customer reviews, clickstream data, social media mentions—to uncover hidden patterns. They also power fraud detection, chatbot automation, and demand forecasting AI tools.
Deep learning models you might encounter in ecommerce include:
| Deep learning model | What they do | Ecommerce use cases |
|---|---|---|
| Convolutional neural networks (CNNs) | Understand spatial relationships between pixels | Visual search and product categorization |
| Recurrent neural networks (RNNs) | Process information by considering order and context | Analyzing customer service conversations |
| Generative adversarial networks (GANs) | One neutral network creates new data while the other tries to distinguish real from generated content | Image generation, style transfer, and data augmentation |
“The combination of real-world data plus big, meaty questions given to a thinking model is a totally underutilized resource,” says Cat Goetze, founder of Physical Phones, in a Shopify Masters interview. “People think of AI as just a chatbot. Give it the harder problems.”
Large language models (LLMs) and foundation models
Large language models (LLMs) learn patterns from vast amounts of data. These models power automated product description writing, customer support chatbots, and personalized marketing content.
Shopify Inbox AI, for example, gives AI-generated reply suggestions for customer conversations using classification and NLP models.
“Shopify Inbox is a powerful tool,” says Rennie Wood, owner of Wood Wood Toys. “It helps me rescue sales after spending time, money, and energy getting a customer to that point. It pays huge dividends.”
Generative AI models
Generative AI models use machine learning to create new content. They’re trained on large datasets to spot patterns between phrases, words, concepts, and meaning. AI models use this to predict what comes next.
Generative models are the point of entry for many ecommerce businesses; A 2025 Shopify Merchant survey found content generation is the most popular use case of AI. For example, you could use GenAI tools like Shopify Sidekick to:
- Write product descriptions, email campaigns, or subject lines
- Create product image variations
- Answer FAQs in customer support tickets
How AI models already power your ecommerce store
A 2025 Shopify survey of store owners found three-quarters of established business owners already use AI tools. The top two AI benefits reported: improved efficiency on repetitive tasks (55%) and help with brainstorming and creativity (55%).
Here’s how the models powering those AI tools work in practice:
Product recommendations
Attentive’s 2026 report found 68% of shoppers are more likely to purchase when they receive product recommendations based on what a brand knows about them.
AI product recommendations do this through machine learning and collaborative filtering. The AI model analyzes 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, the home essentials brand that transformed their 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 (BOPIS) 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.”
Demand forecasting and inventory prediction
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.
“We used to have three people in inventory planning and buying,” says Sean Frank, CEO of Ridge. “Now I have one inventory director handling everything. We feed our sales data, trends, launch calendar, and forecasts into an AI model that does the complex analysis. That one person reviews it, applies judgment, and makes the calls.”
Read: Managing Inventory in Shopify Just Got Easier
Customer service automation and chatbots
Modern AI chatbots guide customers through complex purchases, understand context, and feel genuinely helpful. They’re powered by NLP (which understands the query) and LLMs (which generate the answer).
SurveyMonkey’s 2025 CX study found better availability, speed, and more accurate information are the top reasons why customers prefer AI to humans.
“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.”
Visual search and image recognition
Visual search uses a computer vision model to detect patterns in pixels. It’s trained on labeled data—for example, a photo of sneakers labeled as “sneakers”—to detect what an image is. It compares this against a brand’s product catalog to surface visually similar products.
Google Lens is a popular example that processes 25 billion queries per month. Ecommerce businesses can install the Google & YouTube sales channel and have their Shopify product catalog appear in these visual search results. It helps capture the one in five Lens searches that show commercial intent.
AI models built into Shopify
Among merchants not using AI, 29% aren’t sure what AI tools can do, 29% don’t know where to start, and 26% don’t know which tool to use.
“There’s a total misconception that you need 50 different tools,” says Cat Goetze. “We use one or two on a regular basis, and we just know how to use them really, really well.”
Shopify merchants have an advantage with pre-trained AI models built into the ecommerce platform:
Sidekick: Conversational AI for store management
Sidekick is an AI assistant built into every Shopify plan. It uses LLM and agentic AI to help merchants execute bulk store changes, interpret analytics, and manage campaigns via natural language.
Sidekick has already powered nearly 100 million merchant conversations since January 2025. It helps store owners:
- Create custom reports using Shopify Analytics data
- Build customer segments and write tailored email campaigns
- Write website copy, including product descriptions
- Edit product photos and create variations
- Generate custom apps
- Redesign websites
- Complete admin tasks, like creating a discount code or editing a collection
- Create to-do lists inside the Shopify admin
- Get personalized tech support
“I’ve used it for our product descriptions,” says Bree Copeland, store manager at Darla’s Downtown. “It’s very convenient just having it right there, and it definitely saved us a lot of time and energy.
“We love how you can change the tone. We’re trying to be a very retro, lively, bubbly kind of shop so being able to pick the tone is really nice.”
Personalized recommendations on Shop
Shop app is a free digital shopping app that lets customers browse products from Shopify brands, complete checkout in one click with Shop Pay, and track their packages. It’s used by more than 250 million verified buyers worldwide.
Shop app helps with product discovery through smart product recommendations. The app uses store data and ML models to detect connections between products, such as similar descriptions and common combinations.
How to choose the right AI model for your business
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,” says 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:
Match the model type to the task
The output you want from an AI tool influences the model you use.
Nathan Davis, product marketing lead at Shopify, explains this in the context of AI analytics: “LLMs are really good at writing code and SQL—the primary language for basic analytics, dashboarding, and custom reports.
“With traditional analytics, you’re constrained to building dashboards in graphical user interfaces, or you need to write SQL code or Python to get your output,” Nathan says. “With AI analytics, you can simply ask your question and get the answer.”
Other examples include:
| Task | AI model |
|---|---|
| Product description copy | Generative AI |
| Smart product recommendations | Machine learning and collaborative filtering |
| Visual search | Computer vision |
| Chatbots | LLMs and NLPs |
| Inventory predictions | Predictive machine learning |
Build vs. pre-built
AI platforms like Shopify Sidekick, ChatGPT, Claude, and Gemini handle training and deployment of their own AI models. This lowers the barrier to entry: you don’t need to handle the technical foundations to use AI for ecommerce.
If you do want to build your own AI model, consider:
- Costs. A basic rule-based chatbot can cost between $5,000 and $30,000 to develop, while a custom AI agent can set you back up to $120,000 and five months to build.
- Training. Complex algorithms learn from data, but just 7% of brands say their data is completely ready for AI. Once you build the initial model, you’ll need continuous data for training to improve it.
- Data security. Failing to keep customer data safe can result in fines. But IBM found 13% of organizations have reported breaches of AI models or apps. The vast majority didn’t have AI access controls in place.
Best AI models for ecommerce in 2026
The best AI model for your ecommerce business depends on the use case, budget, and tools you’re already using.
Here are some options to consider:
| AI model | Platform | Use for | Input token cost (per million) | Output token cost (per million) |
|---|---|---|---|---|
| Claude Opus | Anthropic | Complex reasoning, long-form research, or building AI agents | $5 | $25 |
| Claude Sonnet | Anthropic | Content and code generation | $3 | $15 |
| Claude Fable | Anthropic | Advanced AI agents and long-horizon problems | $10 | $50 |
| Claude Haiku | Anthropic | Instant responses for everyday tasks | $1 | $5 |
| GPT-5. series | OpenAI | Complex tasks, advanced reasoning, and coding | From 75¢ | From $4.50 |
| GPT-Image | OpenAI | Image generation | From $5 | From $10 |
| GPT-Realtime | OpenAI | Voice interactions and translation | From $4 | From $16 |
| Gemini Flash | Written content, coding, and agentic workflows | From $1.50 | From $9 | |
| Gemini Live Translate | Real-time speech translation | $3.50 | $21 | |
| Gemini Flash Image | Image generation | From 50¢ | From $60 for images | |
| Veo | Video generation | Not available | From 5¢ per second, depending on video output quality |
Not all AI tools have their own models; some build on pre-trained models inside their own software. Microsoft Copilot, for example, uses OpenAI’s GPT-5.5 model for everyday tasks, image analysis, and STEM-related tasks.
AI models FAQ
What are the top 5 AI models right now?
The top AI models as of June 2026 are:
- Anthropic: Claude Fable 5
- Anthropic: Claude Opus 4.8
- Google Gemini 3.1 Pro Preview
- OpenAI GPT-5.5
- Anthropic: Claude Opus 4.7
What are the four major types of AI models?
There are four major types of AI model: reactive machines, theory of mind, limited memory, and self-aware.
What’s the difference between AI, machine learning, and deep learning?
Machine learning is how AI systems learn by spotting patterns in existing data. Deep learning goes further by using larger datasets and deep neural networks that mimic the human brain.
Do I need technical knowledge to use AI models in my business?
Ecommerce platforms like Shopify handle the training and development of AI models, meaning you don’t need any technical expertise to use them in your business. “I know AI it sounds intimidating, but the way Shopify has it set up it’s going to make your job a lot easier,” says Mikey Moran, CEO of Private Label, in a Shopify Masters interview. “And as a business owner, it saves them a lot of time, which saves me a lot of money.”
