Part 1 of 3: The Basics 

Artificial Intelligence has become part of everyday business conversation. It shows up in strategy decks, hiring plans, product roadmaps, and executive conversations. Yet despite how often it’s mentioned, there’s rarely a shared understanding of what AI actually means, or how its different components fit together. 

This blog is the first in a three-part series designed to move away from vague buzzwords and toward clarity. Each part gets progressively more technical. Part 1 focuses on the fundamentals and the core AI terms that everyone should understand before discussing scale, risk, or readiness. 

1. Artificial Intelligence: often shortened to AI, is best understood as an umbrella term. It refers to systems that can perform tasks traditionally associated with human intelligence, such as recognising patterns, making predictions, or generating content.  

AI is not a single tool or capability, and it does not imply human-level reasoning. Most AI systems in use today are narrow, meaning they are designed to perform specific tasks within defined boundaries. This distinction matters, because many expectations around AI are shaped by assumptions that don’t reflect how these systems actually work. 

2. Machine Learning: is a subset of AI and is the engine behind many modern AI applications. Instead of relying on hard-coded rules, machine learning systems learn patterns from historical data.  

For example, rather than manually defining what fraudulent behaviour looks like, a model is trained on past transactions and learns to recognise suspicious patterns on its own. This makes machine learning powerful, but also highly dependent on data quality. The model can only learn from what it is shown, which means inconsistent or incomplete data directly affects outcomes. 

3. Large Language Models: or LLMs, are a specific type of machine learning model trained on vast amounts of text. Their purpose is to understand and generate human-like language. LLMs do not “know” facts in the human sense. Instead, they predict the most likely next word based on patterns learned during training. This is why LLMs can produce fluent, confident responses that sound correct, even when they are inaccurate. Understanding this limitation is essential when using LLMs in business contexts. 

4. Generative AI: builds on these models to create new content rather than simply analysing existing data. This includes generating text, images, code, or summaries. Generative AI is particularly effective for accelerating tasks like drafting content, exploring ideas, or reducing repetitive manual work. However, it is not a replacement for expertise or decision-making. Its outputs still require review, context, and governance, especially when used in operational or customer-facing scenarios. 

5. Bias and Hallucinations: Two concepts that frequently come up alongside generative systems are bias and hallucinations.  

1. Bias occurs when models reflect or amplify imbalances present in their training data, leading to unfair or skewed outcomes.  

2. Hallucinations occur when a model generates information that appears plausible but is factually incorrect.  

These behaviours are not glitches or rare edge cases; they are inherent risks in probabilistic systems. Recognising this helps organisations design appropriate safeguards rather than assuming perfect accuracy. 

6. Prompts: are the inputs used to guide AI systems. A prompt can include instructions, context, constraints, or formatting requirements. The same model can produce dramatically different results depending on how it is prompted. Clear, well-structured prompts can improve relevance and usefulness, but they do not compensate for weak data foundations or unclear objectives. Prompting is an interface layer, not a substitute for readiness. 

7. Agentic AI: represents a step beyond generation into action. These systems are designed not only to produce outputs, but to take steps such as triggering workflows, calling APIs, updating records, or coordinating multi-step tasks. Often, human approval is built into key stages. While still emerging, agentic systems signal a shift from AI as a productivity tool to AI as an operational component of how work gets done. 

8. Natural Language Processing: or NLP, is the field of AI focused on enabling machines to understand and work with human language. Tasks such as sentiment analysis, intent detection, translation, and entity recognition all fall under NLP. LLMs and generative systems are built on top of NLP techniques, which form the foundation for most language-based AI capabilities used today. 

9. Deep Learning: is a subset of machine learning that uses layered neural networks to learn complex patterns in large volumes of data. While traditional machine learning is often more data-science-driven, deep learning learns through many interconnected layers of a neural network, allowing models like LLMs and generative AI to understand language, images, and signals at scale.  

Deep learning unlocks more advanced AI capabilities, but it also raises the bar for data quality, infrastructure, and governance, making strong foundations essential before organisations can safely scale these models. 

Understanding these terms creates a shared baseline so teams can have more meaningful conversations about AI’s role in their organisation. Without this common language, discussions quickly drift into assumptions, mismatched expectations, and confusion. 

In Part 2 of this series, we’ll move from terminology to execution. We’ll unpack how AI actually works inside organisations, from data science and predictive analytics to guardrails, model drift, and cognitive systems, and explore why so many AI initiatives stall when they move from experimentation into real business use. 

Want to have a conversation about what AI means for your business? Chat with us today.