Feeling lost in the world of AI jargon? You’re not alone! Artificial intelligence is reshaping businesses, but let’s be real—terms like LLM, GPT, NLP, and machine learning can sound like a foreign language and it's often easier to ignore it than try to learn anything about it.
Here’s the good news: You don’t need to be a tech genius to leverage AI in your business. This guide will decode AI jargon in plain English, helping you understand key concepts, use AI effectively, and stay ahead of the game.
By the end of this post, you’ll:
✔️ Understand the most important AI terms (without a headache)
✔️ Learn how these technologies impact your business
Bonus: Grab your FREE AI Terminology Cheat Sheet—a printable, easy-to-use reference guide!
What is AI & Why Should Entrepreneurs Care?
AI (Artificial Intelligence) refers to machines and software that can learn, reason, and make decisions like humans. It's improving daily and the options available to you seem never-ending.
Why is AI a Game-Changer for Your Business?
✅ Saves Time – AI automates repetitive tasks, freeing you up for higher-value work.
✅ Boosts Productivity – Get more done in less time using AI-powered tools.
✅ Enhances Marketing – AI helps with content creation, SEO, and ad targeting.
✅ Drives Revenue – AI-powered chatbots, email automation, and AI-enhanced sales funnels increase conversions.
Example: Instead of manually writing emails, tools like ChatGPT or Claude can draft high-converting emails in minutes.
I've included terms related to automation as well, because this is hugely important to understand alongside AI. They work together to make everything in YOUR business better.
Essential AI Terms Every Entrepreneur Should Know
A
AaaS (Agents as a Service): AaaS is like hiring a robot helper that works for you! Imagine you have a magic robot friend that answers customer emails, books appointments, or sells products all by itself. You don’t have to teach it—it already knows what to do! You just rent the robot online, and it does the work for you.
Action: A task that Make.com performs after a trigger.
Adaptive Decision Making: Imagine a robot learning to play a game. Adaptive decision-making means the robot can change how it plays based on what's happening in the game. If one strategy isn't working, it can try something else. It's like learning to ride a bike – you adjust how you balance based on what's happening.
Agentic AI: Like an AI Agent not only can it follow your instructions but it can also make its own plans and decide what to do next, all on its own! That's agentic AI. It's like a robot that can decide the best way to clean your room, not just follow your exact instructions. So, agentic AI is a type of AI agent that is more independent and can make its own decisions. All agentic AI are AI agents, but not all AI agents are agentic.
Aggregator: Combines multiple pieces of data into one.
AI (Artificial Intelligence): Imagine teaching a computer to think and learn like a person, but not exactly like a person. That's AI! It's like giving a computer brainpower to solve problems, understand things, and even create stuff.
AI Agent: Think of an AI agent like a helpful robot assistant. It can sense things around it (like seeing with cameras or hearing with microphones) and then take actions based on what it senses. It's like a smart helper that can follow your instructions.
AI-as-a-Service (AIaaS): Renting AI tools online instead of building your own, like using ChatGPT, Claude or Gemini.
AI Bias: When AI makes unfair decisions because it learned from unfair data. Like if it only saw pictures of big dogs and thought all dogs are big.
AI-Driven Analytics: AI analyses website traffic, sales, and marketing results to uncover trends and help you make smarter business decisions.
AI-Driven Market Research: AI studies trends, customer preferences, and competitors to give you an edge in your industry.
AI-Enhanced Cybersecurity: AI protects your business by detecting fraud, preventing cyberattacks, and keeping customer data safe.
AI Ethics: These are the rules about how to use AI nicely and fairly, like being a good friend. It's about making sure AI doesn't hurt anyone or make unfair decisions.
AI ROI (Return on Investment): Checking if using AI helps you make more money than you spend on it.
AI Scalability: How easily AI can grow with your business without slowing down.
AI Workflow Automation: Using AI to connect different tasks and tools together so work gets done automatically.
Algorithm: This is like a recipe for a computer. It's a set of instructions that tells the computer exactly what to do, step-by-step.
API (Application Programming Interface): A bridge that lets different computer programs talk to each other.
Automated Content Creation: AI helps create social media posts, blog articles, and product descriptions—saving time and effort.
Automation: This is when we use computers and machines to do things automatically, without us having to control them all the time.
Autonomous AI Agents: Agents that can work on their own to complete tasks without needing constant human input.
Autonomous Decision-Making AI: AI that can make business decisions based on data, patterns, and past experiences.
AutoML: Imagine a robot that can build other robots! AutoML is AI that can build and train other AI models without needing a person to help. It's like a robot teacher for other robots.
B
Bias: When the data a computer learns from is unfair, leading to unfair decisions. Like thinking all dogs are big.
Big Data: This is lots and lots of data! So much data that regular computers have trouble handling it. AI loves big data!
C
Chatbot: This is a computer program that can talk to you, like when you ask a website for help. It's like texting with a robot that’s been told what to say.
Compute Power: This is like the "muscle" a computer needs to run AI programs. Big, complicated AI needs lots of compute power, like a race car needs a powerful engine.
Cloud Computing: Using a super-powerful computer over the internet instead of your own computer.
Computer Vision: This is how we teach computers to "see" and understand images and videos, like recognizing what's in a picture.
Conversational AI: A chatbot that talks like a real person, not just simple replies.
Customer Relationship Management (CRM) AI: AI that organizes customer info, predicts what they’ll buy next, and automates follow-ups—so you can build better relationships without the stress.
D
Data: This is like the food for AI. It's all the information – numbers, words, pictures – that computers use to learn. The more data, the better AI can learn!
Data Augmentation: Giving AI extra examples so it learns better, like showing it more kinds of handwriting to read.
Data Security: Keeping your data safe from hackers and bad people.
Data Store: A place where Make.com can temporarily store and retrieve information.
Deep Learning (DL): This is a super-smart type of machine learning. It uses "networks" of information, kind of like a spider web, to learn really complicated things, like recognizing faces or understanding what you say.
Deployment: Sending an AI model out into the world so it can start working after testing and practising behind the scenes.
Diffusion Models: These are the special AI tools that make those amazing AI-generated images you see, like the ones from DALL-E or Midjourney. They work by starting with a blurry image and slowly making it clearer and more detailed.
Digital Twin AI: An AI-powered copy of a business, process, or system that runs in a simulation.
E
E-commerce AI: AI helps online shops recommend products, adjust prices, prevent fraud, and manage inventory to boost sales and customer satisfaction.
Edge Computing: AI working close to where it’s needed, like a phone unlocking with your face instantly instead of asking the internet for help.
Embeddings: A way AI remembers things, like a smart notebook inside its brain.
Embodied AI: This is when AI lives in a robot or smart device in the real world. Think of a robot dog that can learn to fetch or a smart speaker that can understand your voice commands.
Error Handling: What to do when a scenario fails.
Ethical AI: All about making sure that AI is used for good and not for bad, that it's fair to everyone, and that we can understand and trust how it works. It's a really important topic as AI becomes more and more powerful.
Explainability vs. Interpretability: Imagine you ask your robot to choose a toy for you. Explainability is the robot telling you why it chose that toy ("I chose the red car because it's the fastest"). Interpretability is the robot showing you how it made the decision ("I looked at the color, size, and speed of all the toys").
Explainable AI (XAI): Making AI’s decisions clear so we understand why it did something.
Explainability Score: A score showing how easy it is to understand how an AI made a decision.
F
Federated Learning: Imagine a group of friends teaching a computer together, but without sharing their drawings with each other. Federated learning is when AI learns from lots of different devices (like phones) without collecting all the information in one place. This helps keep everyone's information private.
Few-Shot Learning: AI learning something new after just a few examples, like a kid who only needs to see a bike once to know what it is.
Filter: A condition that decides if an action should happen or not.
Fine-Tuning: Teaching AI special tricks so it works exactly how you want it to.
G
Generative AI: AI that can create things, like stories, pictures, and videos.
GPT: Is like a super-smart robot friend that has read millions of books and stories. When you ask it something, it thinks about what it has learned and gives you the best answer—just like a magical talking book!
H
Hallucination: When AI makes up something that isn’t real, like a chatbot telling you unicorns exist.
Horizontal AI Agent: A flexible AI assistant that can do many different things across industries.
Human-in-the-Loop (HITL): AI working together with humans, like a self-driving car that still needs a driver just in case.
Hyperparameter: Special settings that help AI learn better, like tuning a radio to get the clearest sound.
I
Inference: When AI takes what it has learned and uses it to guess or predict something new.
Integration: Connecting AI with other tools so they work together, like linking your chatbot to your email system.
Iterator: A tool that breaks big data into smaller pieces so each piece can be processed separately.
L
Large Language Model (LLM): A super-smart AI that knows a lot about words and can write or chat like a human.
LLM Fine-Tuning: Teaching a big AI model special skills, like training it to understand your business better.
LLM Inference Time: This is how long it takes for a really smart AI, like ChatGPT, to answer your question or write a story. It's like waiting for your friend to think of a good joke – sometimes it's fast, sometimes it takes a little longer.
M
Machine Learning (ML): Teaching computers to learn things without telling them exactly what to do.
Make.com Blueprint: Is like a recipe for automation. Imagine you want to bake your favourite cake. Instead of figuring it out from scratch, you can save the recipe so you can make it again anytime, tweak it or share (export) it with your friends.
Make.com Template: Is like a ready-made automation! Imagine you get a colouring book where all the pictures are drawn—you just need to fill them in! That’s what a Make.com Template is. It’s a pre-made automation that you can use right away or change a little to fit your needs. You can’t share it.
Model Training: This is the process of teaching a machine learning model to make predictions or decisions. It’s like practicing with flashcards before a test.
Module: A single action or step inside a scenario.
Multi-Agent AI System: Multiple AI agents working together, each handling different tasks.
Multi-Modal AI: AI that can work with different things at once—like text, pictures, and videos all together.
N
Natural Language Processing (NLP): AI learning to understand and talk like a human.
Neural Network: A bunch of tiny AI “brain cells” working together to learn and solve problems.
Neuro-Symbolic AI: This is a way of building AI by combining two different approaches: one that is good at learning from lots of examples (like deep learning), and one that is good at using logic and rules (like symbolic AI). It's like combining the best parts of two different robots to make an even smarter one.
No-Code AI: AI tools that don’t need coding, so anyone can use them!
O
Orchestration AI: AI that helps coordinate multiple AI tools and agents so they work together smoothly.
P
Pattern Recognition: Imagine showing a computer lots of pictures of cats and dogs. Pattern recognition is how the computer learns to tell the difference between them by finding patterns in the pictures.
Personalisation AI: AI that makes things just for you, like Netflix suggesting shows you’d love.
Personalised Recommendation AI: AI suggests products or content tailored to each customer, increasing sales by showing them exactly what they want.
Predictive Analytics: AI guessing what might happen next, like predicting which product will sell best.
Prompt: The question or instruction you give to AI, like asking ChatGPT to write something for you.
Prompt Engineering: Writing really good instructions for the AI so it does exactly what you have in mind. The better your instructions (your prompt), the better the outcome. It's about being a good "robot teacher" so the AI does what you want!
R
Recommendation Engine: AI suggesting things you might like, like Amazon showing you products based on your past searches.
Reinforcement Learning: AI learning by trying things and getting rewards, like a puppy learning tricks for treats.
Retrieval-Augmented Generation (RAG): AI that finds real facts before answering, so it doesn’t make stuff up.
Robotics: AI-powered machines that can move and do tasks, like robot vacuum cleaners.
Router: A tool that lets data flow in different directions based on conditions.
S
Scalability: How easily AI can grow and handle more work as your business gets bigger.
Scenario: Automated workflows inside Make.com that connect different apps and services.
Self-Healing AI: AI that can fix itself when something goes wrong.
Sentiment Analysis: AI figuring out how people feel about something, like reading online reviews to see if customers are happy or upset.
Social Media AI: AI tools that analyse trends, schedule posts, engage with followers, and even create content to grow your brand effortlessly.
Supervised Learning: Teaching AI by showing it examples, like showing it pictures of cats and dogs and telling it which is which.
Supply Chain Optimization with AI: AI ensures faster deliveries, lower costs, and fewer inventory problems for product-based businesses.
Synthetic Data: Fake but realistic data used to train AI safely, so it doesn’t need real customer info.
T
Tokenization: Imagine cutting up a sentence into smaller pieces so a computer can understand it better. Tokenization is how AI breaks down words and sentences into smaller chunks called "tokens" so it can process language. It helps AI understand what you're saying.
Training Data: The examples we give AI to help it learn.
Trigger: An event that starts a scenario.
U
Unsupervised Learning: AI finding patterns in data without being told what to look for.
V
Validation Data: Extra data used to check if AI has learned correctly before using it for real.
Vector Database: A smart way AI stores and finds information quickly, like a super-organised filing cabinet.
Vertical AI Agent: A smart AI assistant that is great at one specific task or industry.
Virtual Assistant (VA) AI: AI-powered assistants schedule appointments, answer emails, and handle tasks, so you can focus on growing your business.
W
Webhook: A way for apps to send data to Make.com automatically.
Z
Zero-Shot Learning: AI solving problems it’s never seen before, like reading a language it was never taught.
AI is Here to Help, Not Replace You!
AI isn’t about replacing human creativity—it’s about enhancing it. The sooner you embrace AI, the faster you’ll scale your business while saving time and money.
What AI tool are you most excited to try? Drop a comment below!
If I've missed any terms, go to chatGPT and ask it what the term means in simple terms. 'Please explain what x means in the simplest of terms and how it would work in my business.'