Unlocking the Value of AI for Your Business Series: Ep 1 - Starting with the Basics

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 4 sub-categories are used in various ways across industries and in my situations in conjunction with each other.

• Machine Learning (ML): The goal is to complete tasks autonomously using large amounts of data to improve performance and accuracy.

  • It learns through 4 main methods

    • Supervised: which uses labelled data to train the algorithm to give a set outcome.

    • Reinforcement Learning: which trains models through a scoring of solutions similar to trial and error.

    • Unsupervised: which looks to Identify patterns in a dataset without guidance forming clusters of similar data.

    • Semi-Supervised: This is similar to that of supervised learning where the initial model is trained using labelled data however it continues to learn from data that may be outside of the initial set of lables using technics similar to the unsupervised method. eg training on cats and dogs then showing it a tiger it would likely group it with the cats as it has similar traits.

• Deep Learning (DL): covers techniques that are as close as we currently are to mimicking the human brain, with computers focusing on complex neural networks that replicate how signals are sent and processed around the brain using values to adjust and change outcomes between layers.

  

• Natural Language Processing (NLP): Is the use of Machine Learning to enable computers to understand and communicate with human language.

  • NLP is one of the fastest-growing and widely used areas of AI, serving as a key component in the development of Generative AI due to the ease of entry for users as they can create prompts in plain language.

  • With the use of Deep Learning techniques, Large Language Models (LLMs) have been developed that use 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 to enable computers to derive information from images, videos and other visual inputs

  • How does it work – Computer vision requires vast amounts of visual examples to identify key features that differentiate elements of images and provide additional usable data. This is completed by converting images into individual pixels and then assigning each a value. The algorithms look for patterns within these values shown below.

    

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, along with 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. However, these advancements need to be developed along with the explainability of AI.

This evolution shows one consistent truth, AI thrives when it’s trusted, transparent, and explainable.

Where is AI today

With all the advancements in AI in recent years, the applications of the technology and how they are integrated into our daily lives have exploded, such as:

• Using Machine Learning to predict and recommend everything from your weekly shop to your streaming recommendations.

• Using Natural Language Processing in your smartphone, smart devices (or generally anything else they slap 'smart' in front of) to take commands, such as Siri, Alexa, or Google Home.

• Computer vision is used nearly anywhere a picture or visual is being checked, such as when you go on your holidays and have your passport scanned, when you park your car and they scan your license plate, or even when unlocking your phone with FaceID.

This isn't to try and say AI is always watching or to scan people instead, it shows the growth of the integration of this technology into our daily lives to automate tasks and speed up processing data.

What's to come from this series

This series will cover topics from:

• 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 that you would be interested in understanding more about or having issues with, feel free to reach out, and we can add them to the series.