Artificial intelligence (AI) is technology that lets machines learn, reason, and make decisions the way a person might. Machine learning is the part of AI that makes this possible. It’s how systems get better at a task by learning from data rather than following rules someone programmed in ahead of time.
Worldwide spending on AI is expected to hit $2.52 trillion in 2026, a 44% jump year over year, according to business and tech firm Gartner. Here’s look at the core differences between the two terms and what they mean for your business.
What is artificial intelligence?
Artificial intelligence (AI is a branch of computer science focused on building systems that can reason, learn, and act with limited human input. AI research aims to create algorithms, often organized as artificial neural networks, that process data, recognize patterns, and respond the way a human brain would.
Most AI in use today is narrow AI (also called weak AI): systems built to handle specific tasks, like recommending products or flagging fraudulent orders. General AI, sometimes called strong AI or artificial general intelligence (AGI), is a theoretical AI system that would match human intelligence across any task rather than just one. That said, a 2023 survey of 2,778 AI researchers put a 50% chance on AI outperforming humans at every task by 2047.
What is machine learning?
Machine learning is a subfield of AI in which machines learn from data instead of following explicit, rule-based programming. A machine learning model works by training on a dataset. It uses statistical algorithms to detect patterns in that data, and applies what it found to make predictions or decisions on new information it hasn’t seen before. The more data the model processes, the more accurate it gets over time.
The global machine learning market was valued at $48 billion in 2025 and is projected to reach $432 billion by 2034, according to Fortune Business Insights.
How does machine learning work?
Machine learning works through a repeatable loop: collect data, train a model on that data, and use the model to make predictions. Each cycle refines the model and uses new data to make future predictions more accurate over time.
The loop breaks down into three steps:
- Collect data. Gather relevant data for the task, like past purchase history for a recommendation engine or transaction records for fraud detection.
- Train the model. Feed the data into a learning algorithm, which adjusts its internal parameters to find patterns and relationships within it.
- Make predictions. Apply the trained model to new data to generate an output, like a recommended product or a fraud risk score. Results feed back into the loop to retrain and improve the model.
Different tasks need different learning algorithms. The three most common are:
- Supervised learning. Typically, the model trains on labeled data, where each example is paired with the correct answer, then learns to predict labels for new, unlabeled data.
- Unsupervised learning. The model finds patterns or groupings in unlabeled data without being told what to look for.
- Reinforcement learning. The model learns by trial and error, receiving a reward or penalty for each action and adjusting its behavior to maximize future rewards.
Machine learning vs. AI: What’s the difference?
Machine learning is a subset of AI. AI is the broader field of building machines that mimic human cognition, while machine learning is the specific technique of learning patterns from data instead of following explicit rules.
Both fields rely on data and algorithms to do tasks that would otherwise need step-by-step human instructions, and both fall under the broader umbrella of data science.
AI covers any technology that lets machines mimic the cognitive aspects of humans, including reasoning, planning, and language processing. Machine learning is one way to build AI. Models train on data to learn and improve their performance on a specific task rather than following pre-programmed rules for every scenario.
Not all AI uses machine learning. There are different approaches to achieve AI. Some artificial intelligence techniques involve symbolic reasoning and logic without necessarily involving machine learning algorithms.
| AI | Machine learning | |
|---|---|---|
| What it is | The umbrella term for building machines that mimics humans | A subfield of AI that focuses on learning patterns from data |
| Scope | Includes reasoning, planning, language processing, and more | Focused on pattern recognition and prediction from data |
| How it works | Multiple approaches, including symbolic reasoning and rule-based logic | Trains statistical models on data instead of following fixed rules |
| Example | A virtual assistant that plans a route and answers voice commands | A recommendation engine that predicts products from purchase history |
AI vs. machine learning vs. deep learning
Deep learning is a subfield of machine learning, which is itself a subfield of AI. AI is the broadest field, covering any technology that mimics humans. Machine learning narrows that to systems that learn from data instead of following fixed rules. Deep learning narrows it even further. It’s machine learning built on artificial neural networks with multiple layers, which is what allows it to process large amounts of unstructured data, like images, audio, and text.
All deep learning is machine learning, and all machine learning is AI, but plenty of AI and machine learning doesn’t involve deep learning at all.
Generative AI vs. machine learning
Generative AI is a type of machine learning that creates new content, like text, images, audio, or code, instead of just analyzing or sorting existing data. A typical machine learning model learns to predict an outcome, like whether an email is spam or what a customer might buy next. A generative model goes a step further and learns the patterns in its training data well enough to produce something brand new that fits those patterns.
Much of generative AI today runs on deep learning, using neural networks trained on huge datasets to write product descriptions, generate marketing images, or draft customer service replies. So think of it as one more layer in the nesting doll: generative AI sits inside machine learning, while deep learning may be what connects them.
Types of machine learning
Here’s more detail on the types of machine learning categories:
Supervised learning
In supervised machine learning, the algorithm is trained using labeled data sets, which consist of inputs and outputs, and the algorithm learns the relationship between the two in order to predict the outputs using future data. Common applications that end users might be familiar with include image recognition, spam filtering, weather forecasts, and predicting house prices.
Unsupervised learning
Unsupervised machine learning deals with unlabeled data, and the algorithm’s goal is to uncover hidden patterns.
Common applications include:
- Market segmentation
- Anomaly detection: especially useful in cases of fraud detection
- Dimensionality reduction: compressing large datasets for faster analysis
Semi-supervised learning
This type of machine learning involves both labeled and unlabeled data, especially when labeled data sets are scarce and expensive to acquire. One example is the case of developing a machine learning model to diagnose rare diseases because such data is sensitive, expensive to obtain, and rare.
The algorithm may leverage labeled data sets to learn a model, and then use unlabeled data to refine and improve the model’s performance.
Common uses include:
- Text classification: categorizing documents
- Image segmentation: dividing an image based on pixels that share similar characteristics, sometimes used for analyzing medical scans
- Sentiment analysis: determining the emotional tone of text
Reinforcement learning
With reinforcement learning, the algorithm learns through trial and error in a simulated environment, receiving rewards for desired actions as well as penalties for undesired actions. The point is that the algorithm learns to take actions that maximize its rewards.
Common applications include:
- Training bots to play games
- Training self-learning robots
- Optimizing resource allocation in complex systems: such as coordinating the flow of materials across multiple stages of supply chain production and distribution
Examples of machine learning
Machine learning is used to find patterns in data and make predictions from it. Businesses use it to automate tasks like product recommendations, fraud detection, and demand forecasting. Machine learning applications already show up across many areas of everyday life, including:
Image recognition
One example of machine learning is image recognition. AI systems can identify objects and scenes in images with high accuracy. This is used in facial recognition software, self-driving cars, and image tagging on social media platforms.
Natural language processing
Machine learning can be used in natural language processing to understand and process human language. Natural language processing is used with virtual assistants, translation tools, and social media sentiment analysis.
Recommendation engines
Machine learning can also be deployed for generating recommendations. AI algorithms power such systems on ecommerce platforms and streaming services, suggesting products or content you might be interested in based on your past behavior and preferences.
How businesses can use machine learning
AI adoption reached 88% of surveyed organizations in 2025, according to Stanford University, which shows AI use in business is now mainstream. Businesses report real benefits, too. According to Deloitte’s 2026 State of AI in the Enterprise report, 66% of organizations reported productivity and efficiency gains from AI adoption, while 53% reported better insights and decision-making.
Agentic AI—systems that don’t just recommend an action but carry it out—is on the rise. In ServiceNow’s 2025 Enterprise AI Maturity Index, 33% of organizations were piloting or already running agentic AI use cases, and 43% planned to adopt it within the next year. In commerce, that’s evident in Shopify’s agentic storefronts, which let customers discover and buy products directly inside AI channels like ChatGPT, Gemini, Microsoft Copilot, and Meta.
Machine learning has many applications in business settings, including:
Data-driven decision-making
Machine learning can analyze data from across a store, such as sales, inventory, and customer behavior, to identify patterns and trends. This is the same technology behind the predictive analytics features already built into the tools store owners may be using.
Shopify store owners have Sidekick, an AI assistant with direct access to a store’s data that can run analytics queries, build charts, and proactively surface insights. Instead of building a report from scratch, you can ask a question in plain language, like which products are trending, and get an immediate answer.
Cat Goetze, founder of Physical Phones, uses AI to help with business decisions.
“I gave Claude a download of all of my business information, specifically expenses and revenue line items from the last year,” says Cat in an episode of Shopify Masters. “I said, ‘Here’s what 2025 looked like. How much money can I afford to spend on personnel in 2026?’ That was kind of the main question that I was wrestling with at the time.”
Improving the customer experience
Machine learning algorithms can analyze customer data, like purchase history and browsing behavior, to recommend products that match a shopper’s interests. AI already shapes how people shop: in a 2025 IBM global survey, 45% of consumers said they turn to AI during their buying journeys, and 41% use it specifically to research products.
Machine learning-powered virtual assistants can help answer customer questions around the clock and resolve basic issues without a human agent. Shopify Inbox, for example, connects to a store’s data to create AI-powered replies to customer questions in a tone and style set by the store owner themselves.
Improving business operations
Machine learning can help improve fraud detection and assess risk management by analyzing financial transactions to identify patterns indicative of unauthorized activity to help businesses protect themselves from financial losses. Machine learning can also improve supply chains, analyzing data to predict demand fluctuations and optimize inventory management. This helps businesses make sure they have the right products in stock at the right time, reducing costs and increasing efficiency.
Marketing and sales optimization
Machine learning can increase advertising efficacy by analyzing customer data and demographics to identify the ideal target audiences for marketing campaigns. This lets businesses make the most of their advertising budgets. Machine learning can also help with lead scoring and sales forecasting by analyzing customer interactions to predict which leads are more likely to convert into sales, helping sales teams prioritize their time and focus their efforts on more qualified leads.
Machine learning can also optimize pricing, generating recommendations that adjust for demand and market conditions rather than relying on static, one-time price rules. Shopify’s Smart Pricing app uses machine learning to generate price tips, including markdown and markup suggestions, to help optimize a store’s product prices and improve profit.
Product development
Machine learning can also help improve new product designs and upgrades based on customer feedback and usage data to identify trends and inform product development strategies. This enables businesses to create products that better cater to customer needs. Machine learning is used in predictive maintenance by analyzing sensor data from machines, which helps prevent equipment failures before they occur. This proactive approach avoids costly downtime and emergency maintenance repairs.
How to get started with AI and machine learning
If you’re not sure where to begin, you’re in good company. In a 2025 Shopify survey, 29% of store owners who haven’t yet tried AI tools said they’re not sure what those tools can actually do.* The businesses seeing real results usually don’t start with a grand strategy. They pick one problem, try a tool, and build from there. Here’s a simple path to follow:
1. Identify a use case
Look at where your team spends the most repetitive time, like writing product descriptions, answering the same customer questions over and over, or pulling weekly reports by hand, and start there. Content generation is a common use case, such as writing marketing copy or product descriptions.
For content generation specifically, automatically generating product descriptions shows how this looks in practice: Shopify Magic drafts a full product description from a prompt, matching whatever language the prompt is written in, since the feature works in every language Shopify supports. Store owners get a draft to edit rather than a blank page, though text generation currently requires a desktop browser rather than the Shopify mobile app.
2. Start with built-in tools
Skip custom development for now and try the AI features already inside the tools you use every day, like Shopify Sidekick. They’re built to work right out of the box, so there’s nothing to install, configure, or hand off to a developer.
3. Measure the results
Pick one specific number to track the before and after, like hours spent on reporting, response time to customer messages, or conversion rate on product pages.
4. Scale what works
Once a use case proves itself, expand it to other parts of the business. Maggy London’s four-person ecommerce team started small, asking Shopify Sidekick simple questions about sales performance. That habit grew into a company-wide AI practice, and the ecommerce team was able to cut weekly reporting time by more than 80%, from three to four hours down to just 20 to 30 minutes.
*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.
AI machine learning FAQ
Is AI the same as machine learning?
No. Machine learning is a subset of AI, though not all AI is machine learning. AI is the broader concept encompassing any technique that enables machines to mimic humans. AI can involve various approaches to achieve intelligent behavior, including machine learning.
Is ChatGPT a type of machine learning?
ChatGPT is powered by machine learning, specifically a large language model (LLM) trained using deep learning techniques. An LLM is a type of AI model trained on vast amounts of text data; these machine learning models grasp the statistical relationships between words and can generate text, translate languages, write content, answer questions, and perform tasks normally done by humans.
What are AI models?
An AI model is the trained system that takes an input and produces an output, like a prediction, classification, or piece of generated content. It’s built by training an algorithm on data until it can reliably perform a specific task.
Is generative AI machine learning?
Generative AI is a type of machine learning. Instead of predicting a label or score, it learns the patterns in its training data well enough to generate new content, like text, images, or code, that fits those patterns.
How do machine learning and AI help businesses?
Businesses use machine learning and AI to automate repetitive tasks, surface patterns in their data, and personalize the customer experience at scale. Common applications include product recommendations, fraud detection, demand forecasting, and AI-powered chat support.
