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Artificial intelligence (AI) is quickly becoming part of how businesses run every day. According to a 2025 McKinsey report, 78% of businesses now use AI in at least one business function — a big jump from 50% in 2022. As the technology matures, it will show up in nearly every tool you use to run your business, from marketing to finance to customer service. Understanding the language of AI can help you make smarter choices about which tools to adopt and how to put them to work.
Let’s review the key AI terms you’ll hear most often, starting with the main one itself.
What is artificial intelligence (AI)?
AI is the science of building computer systems that can perform tasks we usually associate with human intelligence, like making predictions or generating new ideas. It’s a broad field that covers many techniques and methods. For a full overview, see our guide to artificial intelligence in business.
AI vs. machine learning vs. deep learning: What’s the difference?
These terms are often used interchangeably, but they mean different things.
- Artificial intelligence is the overarching field of building computer systems with human-like intelligence.
- Machine learning (ML) is one way to build AI systems. It’s a method where computers learn from data instead of being directly programmed. Machine learning systems analyze examples, find patterns, and improve over time. For example, a machine learning application might predict your busiest sales days by learning from past records. Today, most practical AI systems are developed using machine learning.
- Deep learning is a type of machine learning that teaches computers to recognize patterns in very large datasets. This is achieved by passing information through many layers of connected systems, called neural networks, which gradually learn to spot features on their own. Deep learning makes it possible for AI to do things like recognize faces in photos or turn speech into text.
Types of AI (narrow vs. general vs. generative)
You’ll often come across references to different types of AI or types of AI models. Here are the most common ones:
- Narrow AI is built for one job, like recommending a product or detecting credit card fraud. This is what most business AI tools use today.
- General AI (also called artificial general intelligence, or AGI) refers to a system that could reason and perform any task a human can. It’s still a theoretical concept — we’re not there yet.
- Generative AI creates new content such as text, images, audio, or code based on patterns in its training data. Tools like ChatGPT fall into this category.
Other important AI terms
Prompt is the instruction you give a generative AI tool. The clearer the prompt, the better the output.
Agent is an AI system that can take steps toward a goal, not just provide answers. For example, an AI agent might schedule an appointment instead of just listing free times in your calendar.
Model is the “brain” of an AI system. It’s the set of patterns it has learned from data that power its outputs.
Training data refers to the examples a model learns from. A tool that recommends new recipes, for example, might be trained on thousands of restaurant menus and customer reviews.
Natural language processing (NLP) is the branch of AI that allows computers to understand and respond to human language.
Hallucination happens when AI gives an answer that sounds confident but is factually incorrect. That’s why it’s important to double-check AI outputs.
Bias in AI means the system’s outputs can be skewed if the training data isn’t balanced or representative. For instance, if sales data from only one region is used, the tool’s predictions may not apply to customers in other locations.
How does generative AI work?
Generative AI is what most people are experimenting with right now, so it’s worth taking a closer look. How does it work?
A generative AI tool doesn’t really “think” like a human. Instead, it looks at patterns in its training data and predicts what should come next — like a word in a sentence or a pixel in an image. That’s how it can draft an email, write a product description, or design a quick flyer when you give it a prompt.
And that’s just the beginning. For an introduction to generative AI in action, see how business owners are already using it every day.
AI in marketing: Personalization and segmentation
One of the most immediate business applications for AI is in marketing. It can power personalization, like suggesting products based on past purchases. It can also improve segmentation, grouping customers into lists — say, first-time buyers, repeat customers, or holiday shoppers — so you can target each group with the right offers.
As use cases for AI in digital marketing grow, small businesses can tap into the same strategies big brands use, often without big costs. In fact, one of the common myths about AI is that it’s only for companies with massive tech budgets. The reality is that AI features are already built into many of the marketing tools you use every day, including Square.
AI in sales: Leadscoring and forecasting
AI is also making its way into sales, especially for two key tasks:
- Forecasting: Using past data to predict future sales trends.
- Lead scoring: Predicting which prospects are most likely to become customers.
Using AI in sales can also help take the guesswork out of planning. If you can forecast not just your busiest days but also which products are likely to sell fastest, you can stock up and schedule staff accordingly.
If need more ideas on how to use AI in sales or marketing in your industry, see these Square guides: AI for retailers, AI in restaurants and the AI in the beauty industry.
Making AI work for you
The world of AI can feel technical, but once you understand the key terms and concepts, you’ll be able to spot where it can genuinely help your business. The point isn’t to replace your judgment, but to use AI as a tool to grow your business, save time, and make better decisions.
