For many organisations, Artificial Intelligence (AI) can seem complex, expensive, and out of reach. Yet, when approached with the right strategy, AI can become one of the most powerful enablers of growth and innovation.
At Cloud Formations, we help businesses bridge the gap between curiosity and desire into capabilities, transforming AI from an abstract concept into tangible value.
Understanding the basics: what AI really is
Artificial Intelligence refers to computer systems that can perform tasks traditionally requiring human intelligence, such as reasoning, problem-solving, and decision-making.
In practice, AI enables systems to analyse data, recognise patterns, and make predictions without explicit programming. It's an umbrella term encompassing technologies like:
- Machine Learning (ML): systems that learn from data to improve over time
- Deep Learning (DL): advanced neural networks that mimic the human brain
- Natural Language Processing (NLP): the foundation of tools like ChatGPT that understand and generate human language
- Computer Vision (CV): AI that interprets visual data, powering everything from facial recognition to autonomous vehicles
From streaming services to virtual assistants, AI is already embedded in daily life, often in ways people don't even notice. These four sub-categories are used in various ways across industries, and in many situations, in conjunction with each other.
The four building blocks, in more depth
Machine Learning (ML) aims to complete tasks autonomously, using large amounts of data to improve performance and accuracy over time. It learns through four main methods:
- Supervised: uses labelled data to train the algorithm towards a set outcome
- Reinforcement learning: trains models through a scoring of solutions, similar to trial and error
- Unsupervised: looks to identify patterns in a dataset without guidance, forming clusters of similar data
- Semi-supervised: similar to supervised learning — the initial model trains on labelled data, then continues learning from data outside that initial set using unsupervised-style techniques. For example, a model trained on cats and dogs, when shown a tiger, would likely group it with the cats due to similar traits
Deep Learning (DL) covers techniques that are as close as we currently are to mimicking the human brain — complex neural networks that replicate how signals are sent and processed between layers, adjusting values to change outcomes.
Natural Language Processing (NLP) is the use of Machine Learning to enable computers to understand and communicate with human language. It's one of the fastest-growing and most widely used areas of AI, and a key component in the development of Generative AI, given how easy it is for users to enter — they simply write prompts in plain language. Using Deep Learning techniques, Large Language Models (LLMs) have been trained on vast quantities of text data to understand questions (user inputs) and produce responses in natural language (output) that a user can understand.
Computer Vision (CV) is another use of Machine Learning that enables computers to derive information from images, videos, and other visual inputs. It works by converting images into individual pixels, assigning each a value, and looking for patterns across those values — requiring vast amounts of visual examples to identify the features that differentiate elements within an image.
How AI evolved
AI has progressed from theoretical discussions in the 1950s to real-world transformation today.
- 1950s–1970s: the foundation years, where early definitions were discussed alongside experiments like ELIZA (the first chatbot) and Shakey the Robot
- 1980s–1990s: the rise of neural networks and decision trees
- 2000–2020: big data and cloud computing accelerated adoption, and virtual assistants like Siri and Alexa brought AI into homes
- 2020–today: Generative AI, like ChatGPT, creates text, images, and code
- Future: Agentic AI will anticipate needs, make autonomous decisions, and redefine how we interact with technology — though these advancements need to be developed alongside the explainability of AI
This evolution shows one consistent truth: AI thrives when it's trusted, transparent, and explainable.
Where AI is today
With all the advancements in AI in recent years, the applications of the technology — and how deeply they're integrated into our daily lives — have exploded:
- Machine Learning predicts and recommends everything from your weekly shop to your streaming recommendations
- Natural Language Processing powers your smartphone and smart devices — anything with "smart" in front of it — to take commands, like Siri, Alexa, or Google Home
- Computer Vision shows up almost anywhere a picture or visual is being checked: scanning your passport at the airport, reading your number plate at a car park, or unlocking your phone with FaceID
This isn't to suggest AI is always watching or scanning people — it illustrates how deeply this technology has been woven into daily life to automate tasks and speed up data processing.
What's to come from this series
This series will cover topics including:
- Considerations when implementing AI
- Skills and tools that will help you or your business get the most out of AI
- Advanced topics such as Agentic AI and Explainable AI
If there are other AI topics you'd be interested in understanding more about, or issues you're facing, feel free to reach out — I can add them to the series.