11 AI terms everyone should know

Let’s be honest. AI is everywhere right now, but most conversations around it feel unnecessarily complicated.

You hear terms like machine learning, LLMs, or prompt engineering and suddenly it feels like you need a technical background just to keep up. The truth is you don’t.

This article breaks down 11 common AI terms everyone should know, explained in a simple, conversational way, using examples you can actually relate to.

If you’ve ever thought “I know AI is important, but I don’t fully get it”, this is for you.

What is Artificial Intelligence (AI)?

At its core, Artificial Intelligence is about machines doing things that usually require human thinking.

That could be understanding language, recognizing images, making recommendations, or answering questions.

AI already shows up in daily work through search, automation, and digital workplace tools. As AI becomes more embedded in platforms like Microsoft 365 and intranets, understanding the basics helps teams adopt it more confidently.

What is Machine Learning?

Machine Learning is a part of AI where systems learn from data instead of being manually programmed for every scenario.

Instead of defining every rule, the system identifies patterns and improves over time.

Example: Spam filters that get better the more emails you receive.

This is why many AI-powered workplace tools improve gradually rather than being “perfect” on day one.

What is Generative AI?

This is the term everyone’s talking about.

Generative AI doesn’t just analyze data. It creates new content such as text, images, summaries, or even code.

You’ve likely seen it used to:

• Draft emails
• Summarize documents
• Create first versions of content Generative AI is increasingly being explored in digital workplaces and intranets to reduce manual effort and improve productivity.

What is a Large Language Model (LLM)?

A Large Language Model (LLM) is the engine behind most text-based AI tools.

It’s trained on massive amounts of text so it can understand context and generate human-like responses.

Think of it this way:

• The LLM is the brain
• The chatbot or interface is what you interact with

Most users never see the model itself, only the experience it powers.

What is Prompt Engineering?

Prompt engineering sounds technical, but it’s really just learning how to ask AI better questions.

For example:

• “Summarize this document”
• “Summarize this document in 5 bullet points for leadership”

Clear prompts almost always lead to better results.

This is especially important when AI is introduced into shared environments like intranets, where clarity and consistency matter.

What is AI Hallucination?

An AI hallucination occurs when AI generates an answer that sounds confident but is incorrect.

This can include:

• Made-up facts
• Incorrect explanations
• Non-existent sources

This is why human review is still essential, particularly in business, HR, or decision-making scenarios.

What is Natural Language Processing (NLP)?

Natural Language Processing (NLP) allows AI to understand and respond to everyday human language.

It’s what makes it possible to:

• Search using full sentences
• Chat with AI assistants
• Get summaries from documents

NLP is a big reason AI feels more accessible to non-technical users today.

What is Computer Vision?

Computer vision enables AI to interpret images and videos.

It’s used for:

• Document scanning
• Facial recognition
• Quality checks
• Visual pattern detection

This capability is often paired with automation to reduce manual review work.

What is AI Automation?

AI automation focuses on letting AI handle repetitive or rule-based tasks.

Examples include:

• Automatically categorizing content
• Routing support tickets
• Generating routine reports

In many organizations, AI automation works best when it’s embedded into tools people already use rather than introduced as a separate system.

What is Ethical AI?

As AI adoption grows, ethical AI becomes critical.

Ethical AI focuses on:

• Fairness and bias reduction
• Transparency in decisions
• Data privacy and security

Trust is one of the biggest factors influencing whether employees actually use AI-powered tools.

What is AI Adoption?

AI adoption isn’t about deploying tools. It’s about people actually using them.

Many AI initiatives struggle because:

• The tools feel disconnected from daily work
• Users don’t trust the outputs
• The value isn’t clear

Successful AI adoption usually happens when AI is introduced gradually and aligned with real workflows.

Common AI Confusions (Quick Clarity)

• AI vs Machine Learning: AI is the umbrella; ML is how systems learn
• Generative AI vs Traditional AI: Generative AI creates content; traditional AI analyzes data
• LLM vs Chatbot: LLM is the engine; chatbot is the interface

Final Thoughts

AI doesn’t need to feel overwhelming.

Once you understand the language, AI conversations become far more approachable. You don’t need to understand algorithms or training models to use AI effectively.

This guide reflects real questions people ask when they’re trying to understand AI in practical, everyday terms. Clarity matters more than complexity, especially as AI becomes part of modern digital workplaces.

Understanding the basics is always the first step.

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