By Bartosz K. — Published: 14 October 2025 — Updated: 22 October 2025 — 8 min read
Artificial intelligence (AI) is one of the most talked-about technologies of our era. It appears in everything from smartphone keyboards to medical diagnostics, from recommendation systems on streaming platforms to autonomous vehicles. Yet despite its ubiquity, the term "artificial intelligence" remains poorly understood — and frequently misused. In this article we offer a grounded, practical introduction to what AI actually is, how it works, and why it matters.
At its broadest, artificial intelligence refers to the ability of a computer system to perform tasks that would normally require human intelligence. These tasks include things like understanding natural language, recognising objects in images, making decisions under uncertainty, and learning from past experience.
The term was coined in 1956 by John McCarthy, who defined AI as "the science and engineering of making intelligent machines." Over the following decades, the field has evolved dramatically — from rule-based expert systems in the 1980s to the data-driven, learning-based approaches that dominate today.
A useful distinction is between narrow AI and general AI. Narrow AI — which is everything that exists today — refers to systems designed to excel at a specific, well-defined task: translating text, detecting fraud, playing chess. General AI, sometimes called AGI (Artificial General Intelligence), would be capable of any intellectual task a human can perform. Despite media headlines, AGI remains a research goal rather than a reality.
These three terms are often used interchangeably, but they describe different levels of the same technology stack.
Artificial Intelligence is the broadest concept — any technique that enables a machine to mimic intelligent behaviour. This includes classical rule-based systems, search algorithms, and modern statistical methods.
Machine Learning (ML) is a subset of AI. Instead of programming explicit rules, machine learning systems learn patterns from data. Given enough labelled examples, an ML model can learn to classify emails as spam, predict housing prices, or identify tumours in X-rays — without being told explicitly how to do any of these things.
Deep Learning is a subset of machine learning that uses artificial neural networks with many layers (hence "deep"). Deep learning has driven most of the dramatic progress in AI over the past decade, powering breakthroughs in image recognition, speech synthesis, natural language understanding, and more. Large language models like GPT-4 are examples of deep learning systems.
The core idea behind machine learning is surprisingly simple: you show a model a large number of examples, each paired with the correct answer, and the model gradually adjusts its internal parameters to minimise the difference between its predictions and the correct answers. This process is called training.
Consider an email spam filter. During training, the model sees thousands of emails labelled "spam" or "not spam." It learns which features — certain words, patterns of characters, sending domains — correlate with spam. After training, the model can classify new, unseen emails with high accuracy.
There are several broad categories of machine learning:
AI is no longer confined to research labs. It is embedded in the products and services we use every day, and its applications span virtually every industry.
In healthcare, AI models assist radiologists in detecting cancers in medical images, predict patient deterioration in intensive care, and accelerate drug discovery by modelling molecular interactions.
In finance, machine learning powers fraud detection systems that flag unusual transactions in real time, credit scoring models that assess loan risk, and algorithmic trading systems that execute thousands of decisions per second.
In retail and e-commerce, recommendation systems use collaborative filtering and deep learning to personalise product suggestions for millions of users simultaneously. Demand forecasting models help retailers optimise inventory, reducing waste while ensuring products are available when customers need them.
In customer service, natural language processing (NLP) enables chatbots and virtual assistants to understand and respond to customer queries, route support tickets to the right teams, and summarise long conversation threads for human agents.
In software engineering, AI-assisted tools can suggest code completions, detect security vulnerabilities, generate unit tests, and translate code between programming languages — dramatically increasing developer productivity.
Despite remarkable capabilities, today's AI systems have meaningful limitations that are important to understand before deploying them in production.
AI models can be brittle: a model trained on one distribution of data may perform poorly when that distribution shifts. An image classifier trained on clear daylight photographs may fail in poor lighting or unusual angles.
Most AI systems lack common sense reasoning. A language model can produce fluent, plausible-sounding text, but it has no understanding of cause and effect in the physical world, no genuine intentions, and no persistent memory between conversations unless explicitly engineered.
AI is also data-hungry. Supervised learning, in particular, requires large amounts of labelled data, which is expensive and time-consuming to produce. In domains where data is scarce — rare diseases, niche industrial processes — this remains a significant bottleneck.
If you are considering introducing AI into your organisation, the most important first step is not technology selection — it is problem framing. The most successful AI projects start with a clear, specific business problem: "We need to reduce customer churn by identifying at-risk accounts earlier." "We need to cut manual data entry by automating document processing."
From there, the key questions are: Is there sufficient data? Can success be measured quantitatively? Is the organisation prepared to act on the model's outputs? If the answer to all three is yes, you have a strong candidate for an AI project.
At BKI, we help organisations answer exactly these questions — and then build the systems that deliver on the answers. If you'd like to explore how AI could benefit your business, get in touch.