Ecommerce businesses of all sizes have access to several types of AI, or artificial intelligence, like chatbots for customer support or complex algorithms that forecast demand. These tools range from limited-capability AI tools (like virtual assistants) to sophisticated AI systems that perform market analysis.
What all of these AI variants have in common is that they try to mimic human intelligence. They use skills like image recognition, problem solving, and the ability to apply previous knowledge to new tasks.
“We’re living through unprecedented technological change,” says Alex Pilon, senior developer at Shopify. “All the software development we did over the past 25 years is now accessible at your fingertips, in real time, to solve problems and boost efficiency.”
With AI advancing so rapidly, it helps to have a refresher on the different types of AI and how each one works. This guide covers how AI is classified, why it matters, and how to use each one for your ecommerce business.
What is AI and how is it classified?
AI is a type of technology that teaches machines to think like humans. They can talk to themselves, teach themselves new things, and handle complex challenges.
There are two main ways to classify artificial intelligence, by capability or function.
| AI classification | Capability | Function |
|---|---|---|
| What it describes | What the AI technology can do | How the AI model behaves |
| Examples | Narrow AI, artificial general intelligence, and artificial super intelligence | Reactive, limited memory, theory of mind, or self-aware |
Why AI types matter
This classification of the types of AI helps you:
- Pick the right tool. See whether a solution is narrow AI for one task (e.g., product recommendation engines) or a broader platform that can learn across multiple datasets.
- Estimate effort and ROI. Capability levels signal implementation complexity, while functionality levels hint at data requirements.
- Future-proof your tech stack. Knowing where the technology is headed (e.g., from limited memory systems to theory of mind AI) lets you adopt tools that can scale as AI advances.
Types of AI by capability: narrow, general, and super AI
Here’s a breakdown of three main types of AI, grouped by capability:
| Type of AI | Does it exist? | Ecommerce examples |
|---|---|---|
| Narrow AI (NAI) | Yes | Product recommendations, smart on-site search, customer segmentation, and website analytics |
| Artificial general intelligence (AGI) | Not yet | Creating original work, solving complex problems, and responding to complex questions |
| Artificial super intelligence (ASI) | Not yet | Predictive product development and autonomous marketing campaign management |
Narrow AI (NAI)
Narrow AI is the only type of AI that exists today. Also known as weak AI, think of it as a single-minded tool that’s very good at what it’s designed for but is unable to apply its knowledge to entirely new situations.
Ecommerce examples of narrow AI include:
- Product recommendations. Suggests items based on what shoppers view or add to their carts.
- Smart on-site search. Reorders search results on your website if a query (e.g., “black sneakers”) isn’t getting clicks.
- Customer segmentation. Groups buyers by behavior (like customer lifetime value or churn risk), which powers personalized ads and SMS campaigns that target each customer segment.
Artificial general intelligence (AGI)
Artificial general intelligence (AGI) is still theoretical, but it’s expected to match (or even surpass) human intelligence. AI experts estimate AGI to become reality as soon as 2028.
Unlike general AI, computer scientists envision AI machines that can learn, reason, solve problems, and adapt to new situations just like a human can.
This type of AI might be capable of:
- Creating original works. An AGI system could process human language to write a news article, compose a musical piece, create visual art, or even design a building.
- Understanding and responding to complex questions. Machines with human-like intelligence could analyze vast amounts of information to answer questions in a comprehensive and informative way, even if the answers require reasoning or making judgments.
- Solving complex problems. AGI neural networks could analyze data on matters of global importance—such as water scarcity—and they could work with humans to develop effective solutions or propose solutions without human intervention.
In the context of ecommerce, AGI might be able to handle everything from strategy to execution as a single system, without switching between different tools.
Artificial super intelligence (ASI)
Artificial super intelligence (ASI) is a hypothetical future form of AI that exceeds human capabilities. These machines would be able to feel human emotions and have their own beliefs, instead of being programmed by people.
IBM says several existing AI foundations—machine learning, large language models, neural networks—need to evolve further before ASI becomes a reality. Experts predict this could happen as soon as the 2030s.
Types of AI by functionality
AI functionality groups technology depending on how the model processes information. There are four categories here:
Reactive machine AI
Reactive AI machines respond to their environment with pre-programmed scripts. They don’t store information about past experiences and can’t adapt their behavior based on new situations.
Use cases of reactive AI in ecommerce include:
- AI customer service chatbots. These ecommerce AI chatbots are designed to handle routine queries (like, “Where’s my order?”). If you’re using Shopify, start by activating Shopify Inbox and loading your FAQs into its canned-response library.
- Inventory threshold alerts. Use Shopify Flow to set up automation rules that trigger an email or SMS to the purchasing team whenever a product’s inventory falls below a set threshold.
- Basic product recommendations. Attentive’s 2026 report found relevant product suggestions make 53% of customers more likely to continue shopping with a brand. Shoppers might see a “Customers also bought” carousel of related products similar to the one they’re viewing.
Limited memory AI
Limited memory AI systems can store information about past experiences and use that information to inform their current decisions.
Examples of limited memory AI in ecommerce include:
- Personalized recommendation engines. Some 93% of shoppers are more likely to stay loyal to a brand that offers personalized experiences. The free Search & Discovery app has “Dynamic recommendations” which update in real time as customers browse.
- Dynamic pricing. The Shopify Smart Pricing app uses intelligence to automatically suggest price changes and markdowns. Review the suggestions and run A/B tests to monitor the impact on conversion rate.
- Demand forecasting. Limited memory AI can use historical data, consumer behavior trends, and market dynamics to predict future demand. More than 90% of supply chain leaders plan to use this type of AI to improve forecasting accuracy.
- AI-powered fraud detection. Machine learning models can flag suspicious orders before you process them. Shopify Protect, for example, was trained on more than 10 billion transactions to defend your online store against fraudulent and unrecognized chargebacks.
Theory of mind AI
Theory of mind AI is still conceptual, but it would be a significant leap in capability. It would be able to process present-moment data (like facial expressions) to interpret the thoughts, intentions, and emotions of others.
These emotional learning capabilities would allow the technology to anticipate how others might behave and react accordingly.
Here are some of the concepts theory of mind AI research is building toward:
- Emotion-aware support bots. These ecommerce chatbots can detect tone in a customer’s message and adapt their replies. For example, using a calmer tone or escalating the issue more quickly with an agitated customer.
- Adaptive storefronts. They might be able to change layout, copy, or visuals based on real-time user behavior. If a shopper slows down while scrolling, the interface might soften colors or simplify layout to reduce friction.
- Empathy-driven pricing. It could adjust offers based on emotional cues. If a customer seems frustrated or price-sensitive (based on abandoned carts or chat tone), the system might serve a subtle discount or offer buy now, pay later options, while skipping the discount for more confident buyers.
Self-aware AI
Self-aware AI extends beyond the deep learning and machine learning algorithms that power today’s AI systems. This type of AI would have consciousness and self-awareness. It would understand its own existence and its place in the world.
Computer scientist Lenore Blum told the BBC this level of “AI consciousness is inevitable.”
Modern AI types you’re already using in ecommerce
A 2025 Shopify survey found that 75% of ecommerce businesses already use AI tools.* Here are the types of AI technology that power those tools:
Generative AI
Generative AI uses machine learning to recognize patterns and generate new content. Output from generative AI tools can be in the form of written text, images, video, audio, or code.
“This is going to allow you to be able to scale yourself and do some of the things that you’ve always wanted to do,” says Miqdad Jaffer, director of product at Shopify. “[Allowing you to] spend more time in building your expertise instead of filling your time with redundant tasks.”
Shopify Sidekick uses this type of AI. Ecommerce businesses can use it to:
- Write copy such as product descriptions, ad copy, or customer service responses
- Create and edit images, like replacing the background of a product photo
- Design your website with AI
- Get personalized advice on how to grow your online store
“We take the personal knowledge of the brand and the industry and use it for copywriting, brainstorming, and iterations of successful campaigns,” says Jin Chon of Coop Sleep Goods in a Shopify Masters interview.
Agentic AI
Agentic AI is a type of AI that can make decisions and act upon them. Alex explains: “An agent is a purpose-specific configuration of AI—not just text in, text out. You have a system—or instruction—prompt that is tuned for a particular task or workflow.”
In an agentic commerce context, you could use AI agents to:
- Raise a purchase order with suppliers if the agent expects a spike in demand that currently stock levels can’t handle
- Answer repetitive support queries and perform specific tasks, like giving order updates
- Create a weekly intelligence briefing with last week’s sales data for further analysis
Agentic AI can also act as a virtual shopping assistant that lets customers find, compare, and buy products without leaving an AI search engine.
This presents an opportunity for ecommerce brands: Orders to Shopify stores from AI search are up 13 times year on year, and AI-referred shoppers convert at almost 50% higher rates. They also spend 14% more when they do.
Shopify’s Agentic Storefronts makes your product catalog available to AI search agents in ChatGPT, Google AI Mode, and Copilot. Combine this with the Shopify Knowledge Base app to structure information so shopping agents represent your business accurately.
Polywood, for example, uses Shopify’s AI sales channel integrations to track orders and revenue deriving from AI search.
“When the orders come in, I get the same level of data I would get as if they buy it on the website,” says Benjamin Spiegel, chief digital officer.
“I have a flag in there that tells me it came from AI, but that’s it. Everything else for us is the same. It makes it extremely easy to operationalize because it’s not in a different order management system. Our customer service teams don’t even have to worry about where the order was placed.”
Read: Agentic Commerce on Shopify: How It Works in 2026
Multimodal AI
Multimodal AI processes and generates content across multiple formats, including text, images, audio, and video. If you were writing product descriptions, for example, a multimodal AI tool might write text to describe a product’s use and combine it with an illustration of how to use it.
Tinker by Shopify is an example of multimodal AI. It creates AI-generated images, videos, and text using tools from OpenAI, Google, Anthropic, Black Forest Labs, Kling, and xAI.
“Tinker’s image just always comes out the best,” says Lena of jewelry brand Loire. “It always takes my feedback. I normally get the picture I want in the first, if not second, try.”
AI technologies powering it all
All types of AI use some combination of machine learning, natural language processing (NLP), speech recognition, computer vision, and/or robotics powered by artificial neural networks that mimic the cognitive functions of the human brain.
Here’s how those AI technologies work:
Machine learning (ML)
Machine learning (ML) uses huge datasets to teach machines pattern recognition to predict future outcomes. These AI predictions happen through:
- Supervised learning, where the model uses structured data with the answer already included. For example, if you were creating an AI model to recommend products to online shoppers, you might upload past data—like customer profiles and sales history—and label patterns between the two.
- Unsupervised learning, where the model learns with minimal human input. It forms conclusions on its own, like finding trends in the behavioral and sales data you haven’t specified.
- Reinforcement learning, where the model learns through trial and error. ChatGPT includes humans in this feedback loop.
Deep learning
Deep learning uses neural networks that simulate the human brain to process information. They have interconnected nodes that transmit data between themselves to form connections and detect patterns.
A deep learning model can weight information and alter the strength of each artificial neuron. It uses feedback to change how the AI model responds to new information.
Natural language processing (NLP)
Natural language processing (NLP) teaches machines how to replicate human language. It picks up on connections between letters to understand and generate text.
NLP is also found in speech recognition tools. Voice mode in Shopify Sidekick uses it to interpret what you’re saying and translates that into an actionable command for the AI assistant to do inside your Shopify admin.
Other use cases of NLP in ecommerce include:
- Writing product descriptions or marketing copy
- Translating copy
- Auto-labeling topics in Shopify Inbox
Computer vision
Computer vision allows machines to understand visual information from images or video. This training lets the machine identify objects, track movement, and detect facial expressions.
Computer vision powers AI ecommerce tools like Google Lens, which Google reports as one of the fastest-growing types of search. The tool lets customers upload a photo to the search engine. Computer vision identifies what’s in the image and matches it to relevant products.
*Based on a 2025 survey of 500 Shopify merchants conducted in English across Australia, Canada, the United Kingdom, Ireland, New Zealand, and the United States. Respondents were established merchants with two or more years on the platform. Results reflect the experiences of this specific sample and may not be representative of all merchants.
Types of AI FAQ
What are the 4 types of AI?
AI is often grouped by capability and functionality, creating these four categories:
- Reactive AI
- Limited memory AI
- Theory of mind AI
- Self-aware AI
What type of AI is ChatGPT?
ChatGPT is a form of narrow AI, specifically falling under the category of conversational AI, capable of generating human-like text-based responses within a predefined scope
What are the 5 types of artificial intelligence?
The five types of AI are:
- Artificial narrow intelligence
- General AI
- Artificial super intelligence
- Reactive AI
- Limited memory AI
Is narrow AI the only type that exists right now?
Narrow AI is the only type of AI that exists right now. Deep learning, machine learning, and computer vision are AI technologies that fall under this category. Experts estimate that AI general intelligence, the next phase of AI capable of creating original works and solving complex problems, will become reality as soon as 2028.
What’s the difference between machine learning and AI?
Machine learning falls under the broader category of artificial intelligence and describes how computers learn to simulate the human brain.
