Artificial Intelligence is everywhere.

It’s in boardroom discussions, news headlines, software demonstrations, sales pitches and workplace conversations. Organisations are exploring AI-powered tools to improve productivity, automate tasks, enhance customer experiences and drive innovation.

Yet despite its growing popularity, there remains a significant amount of confusion around the terminology.

In fact, when many people say “AI”, they’re often actually referring to a very specific type of AI known as a Large Language Model (LLM), such as ChatGPT, Microsoft Copilot, Gemini or Claude. So what is the difference?

To understand today’s AI landscape, it’s helpful to view these technologies not as separate concepts, but as nested layers that build upon one another.

AI Isn’t Just One Thing

One of the biggest misconceptions is that Artificial Intelligence is a single technology.

In reality, AI is a broad discipline that has evolved over decades and encompasses many different approaches to making computers perform tasks that would typically require human intelligence.

Think of AI as the largest circle. Inside that circle sits Machine Learning. Inside Machine Learning sits Deep Learning. And inside Deep Learning sit Generative AI models and Large Language Models (LLMs).

Each layer is a subset of the one before it.

Artificial Intelligence (AI)

Artificial Intelligence refers to any computer system designed to mimic aspects of human intelligence. This can include:

• Problem solving
• Decision making
• Pattern recognition
• Language understanding
• Learning from experience
• Planning and reasoning

Importantly, not all AI systems are capable of generating content or holding conversations. For example:

• Spam filters use AI to detect unwanted emails
• Recommendation engines suggest products based on user behaviour.
• Fraud detection systems identify unusual financial transactions.
• Route planning software calculates optimal travel paths.

These systems demonstrate intelligence within a specific context, but they are very different from tools such as ChatGPT or Microsoft Copilot.

Key Takeaway
Artificial Intelligence is the umbrella term for any technology that simulates human intelligence.

Machine Learning (LM)

Essentially, AI that learns from data. Machine Learning is a branch of AI that enables systems to improve their performance through data rather than being explicitly programmed for every scenario.

Traditionally, software developers had to create detailed rules for every possible outcome. Machine Learning changes this approach. Instead of telling a system exactly what to do, developers provide data and allow the model to identify patterns and relationships on its own. Examples include:

• Predicting customer churn
• Detecting fraudulent transactions
• Product recommendations
• Demand forecasting
• Image recognition

The more quality data available, the more accurate these systems typically become.

Key Takeaway
Machine Learning is a subset of AI that learns patterns from data to make predictions or decisions.

Deep Learning

Deep Learning is a specialised area of Machine Learning that uses artificial neural networks. These networks are designed to loosely mimic how neurons in the human brain process information.

The term “deep” refers to the multiple layers through which data passes as the system learns increasingly complex patterns. Deep Learning has become the driving force behind many of the AI breakthroughs seen over the last decade, including:

• Speech recognition
• Facial recognition
• Medical imaging analysis
• Natural language processing
• Autonomous vehicles

The emergence of powerful graphics processing units (GPUs), large-scale datasets and cloud computing platforms has enabled Deep Learning systems to achieve unprecedented levels of performance.

Key Takeaway
Deep Learning is a subset of Machine Learning that uses layered neural networks to solve highly complex problems.

Generative AI and Large Language Models (LLMs)

The Technology Most People Mean When They Say “AI” This is where much of today’s excitement originates. Generative AI refers to models capable of creating entirely new content based on patterns learned from vast amounts of training data. That content can include:

• Text
• Images
• Audio
• Code
• Video
• Designs
• Documents

A Large Language Model (LLM) is a specific type of Generative AI focused on understanding and generating human language.
Popular examples you will likely have heard of are: Microsoft Copilot, ChatGPT, Claude and Google Gemini.
These models are able to answer questions, draft emails, summarise documents, generate reports, create marketing content, assist with coding and analyse information – though the list could go on.

This is why many conversations about “AI” today are actually discussions about LLMs. When someone says: “We’re looking at AI for our business.” What they often mean is: “We’re evaluating Large Language Models such as Microsoft Copilot or ChatGPT.”

Key Takeaway
Generative AI and LLMs are only one part of the AI landscape, albeit the most visible and rapidly adopted part today.

So, Why Does Understanding The Difference Matter?

As businesses continue exploring AI solutions, understanding the terminology can help organisations make more informed decisions. Different business challenges require different types of AI. For example:

Understanding which technology underpins a particular solution helps businesses evaluate capabilities, risks, costs and expected outcomes more effectively.

The Rise of Enterprise AI

While consumer tools like ChatGPT have attracted significant attention, many organisations are now focusing on enterprise-grade AI solutions that integrate with existing business applications and data. Platforms such as Microsoft Copilot have accelerated adoption because they connect AI capabilities directly into familiar tools including:

• Microsoft 365
• Outlook
• Teams
• Word
• Excel
• PowerPoint
• SharePoint

Rather than simply generating content, enterprise AI aims to enhance productivity, improve decision making and unlock insights from organisational knowledge. However, successful adoption requires more than deploying a tool. Businesses must also consider:

• Data governance
• Security
• Compliance
• User training
• Change management
• Responsible AI practices

Without these foundations, organisations risk creating new security and operational challenges while attempting to solve existing ones.

AI Is Bigger Than ChatGPT

Perhaps the most important takeaway is this:

ChatGPT is not AI.

Microsoft Copilot is not AI.

Large Language Models are not AI.

They are examples of Generative AI, which itself sits within Deep Learning, which sits within Machine Learning, which sits within the broader field of Artificial Intelligence.

Understanding these relationships helps cut through much of the confusion surrounding modern AI discussions and enables more meaningful conversations about how organisations can leverage the technology effectively.

Looking Ahead

Artificial Intelligence will continue to evolve rapidly over the coming years.

While Large Language Models currently dominate the conversation, they represent only one part of a much broader technology landscape that includes predictive analytics, automation, computer vision, robotics, cybersecurity and intelligent business systems.

For organisations beginning their AI journey, the first step is not choosing a tool. It’s understanding the technology. Because once you understand the building blocks of AI, you’re better positioned to identify the solutions that genuinely add value to your business.

Recommended Resources

For readers looking to deepen their understanding of AI and Large Language Models:

• Microsoft AI Learning Hub
• Microsoft Responsible AI Resources
• Google Introduction to Machine Learning
• OpenAI Guide to ChatGPT and AI Models
• NIST Artificial Intelligence Risk Management Framework