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Artificial intelligence has become a part of everyday life, from powering customer service chatbots to supporting diagnostics in healthcare.
Yet, despite its growing influence, AI remains a black box for many people and businesses outside the field. Some view it as a tool that can do anything a human can, while others dismiss it as not useful for businesses.
To help people and businesses understand artificial intelligence, we need to start with AI basics.
AI basics explained
Artificial intelligence, in technical terms, refers to a technology that allows computers and machines to mimic human learning, understanding, problem-solving, decision-making, creativity, and independence. Modern AI systems are specialised in one task.
Think of it as a big toolbox: each problem requires a different tool, and no single AI system can solve everything.
Mustafa Suleiman, Microsoft AI’s CEO, has explained the full depth of this technology.
Terms and concepts you might encounter
When talking to AI experts, you will hear them use terms such as “training dataset” or “neural networks”. While these may sound like industry buzzwords, they’re actually technical terms that describe critical concepts in artificial intelligence development.
The most important terms to know are:
Algorithm: A set of rules or steps an AI system goes through to “learn” new information.
Model: A trained system that can make predictions or decisions based on input.
Training dataset: a set of data used to train AI system to perform tasks.
Inference: The process of using a trained model to make predictions on new data.
Neural networks: A structure loosely inspired by the human brain, designed to recognise patterns, learn from data, and approximate complex functions.
Model drift: Gradual degradation of the model’s performance over time due to changes in the data.
Overfitting: When a model learns the training data too well, including its noise and quirks, and performs poorly on new data.
See also: The future is now: AI deep dive
An overview of AI types
Artificial intelligence is an umbrella term that covers various techniques, models, and architectures designed to address specific problems. The nature of the problem they aim to solve impacts AI’s design, including the type of data they use and how they process it.
There are many types of AI, but three of them are the most popular.
Machine learning
Machine learning (ML) is one of the most well-known subsets of AI. It focuses on building systems that can learn from data. One of the biggest strengths of ML is its ability to find patterns and self-improve over time without the need to explicitly change the code.
ML models vary in their application: from Netflix’s recommendations to traffic prediction engine used in Google Maps.
Within machine learning, there is another subset — deep learning. In simple terms, it follows the same general approach as traditional ML — learning from data to make predictions — but it processes information through layered neural networks.
Deep learning is especially good at tasks where traditional ML models would struggle, such as recognising objects or understanding human speech.
For example, Tesla vehicles use deep learning in driver-assistance systems, while Google relies on it to deliver more relevant search results.
Natural Language Processing
Natural Language Processing (NLP) is a branch of AI that helps machines understand and generate human language. This is the technology behind chatbots, machine translation software, sentiment analysis, and text summarisation.
Using deep learning or similar models, NLP can analyse the structure and meaning of spoken or written words.
The most popular examples of NLP in everyday products are Siri and Alexa, which both use NLP to understand human speech.
Computer vision
Computer vision enables AI to interpret real-world objects and process visual information, such as images, videos, or live camera feeds.
Common applications here vary from facial recognition technology (like iPhone’s FaceID) to checkout-free stores (like Amazon Go).
Business applications of AI
One of the most common misconceptions about AI is that it’s useless for real-world business needs. The reasoning usually stems from the fact that many AI applications operate behind the scenes and aren’t always visible to end users.
In reality, AI has quietly transformed operations in every industry it touches. Businesses use it to optimise supply chains, predict equipment maintenance, and tailor product recommendations — boosting efficiency, cutting costs, and enhancing customer experience.
AI in healthcare
In healthcare, AI is the driving force behind diagnostics, patient monitoring, early illness detection tools, and even drug discovery. Its impact spans the entire system: from hospitals to medical research facilities.
Two of the most widely adopted applications of AI in healthcare are:
Medical imaging and diagnostics
Drug discovery and development
In medical imaging, deep learning and computer vision AI models analyse X-rays, MRIs, CT scans, and mammograms to accurately detect abnormalities.
These tools can identify early signs of tumours, infections, and bone fractures, often outperforming traditional diagnostic methods. One prominent example is AI’s role in early cancer detection, where it assists radiologists in spotting subtle patterns that might otherwise be missed.
Drug discovery is another area where AI brings a lot of value. Traditionally, drug development takes years and requires substantial funding. AI speeds up this process, as it can predict the behaviour of different molecules, identify potential drug candidates, and simulate their interactions with biological targets.
One of the most well-known examples of how artificial intelligence accelerates drug discovery happened during the COVID-19 pandemic. At the time, AI was used to analyse molecular data, biomedical literature, and known drug-target interactions to predict which compounds could be effective, narrowing down candidates for clinical testing.
Looking ahead, industry experts foresee AI becoming deeply integrated into personalised medicine. Tomas Turna, Project Manager at Altamira, states:
We should expect AI to tailor treatments based on genetics, lifestyle, and real-time health data. But its future must rest on responsible use, transparency, and rigorous validation.
AI in education
Adaptive learning is a powerful application of AI in education, where platforms adjust content pace and difficulty based on each student’s performance. For example, DreamBox uses artificial intelligence to tailor math lessons: students who struggle receive additional practice, while high performers are challenged with more advanced material.
Online education also benefits from Natural Language Processing (NLP). Tools like Grammarly use NLP to provide real-time feedback on grammar, tone, and clarity of writing. More advanced platforms can even assess short answers or essays and generate automated feedback.
AI-powered analytics also help educators identify learning gaps. Platforms like Knewton track student progress and generate dashboards that highlight which concepts students are struggling with, allowing teachers to intervene more effectively.
Industry leaders highlight the transformative potential of AI. Conor Gately, Managing Director of Altamira UK, notes:
AI’s greatest impact will be personalisation at scale. We’re moving beyond one-size-fits-all content delivery toward a world where every student receives timely nudges, tailored challenges, and real-time feedback—based on how they learn best. Compliance, transparency, and alignment to curriculum standards will be non-negotiable.
AI in commodity trading
In commodity trading, artificial intelligence is primarily used in two ways: process automation and market forecasting.
AI-powered automation reduces the manual workload for traders by automating tasks such as document processing, regulatory reporting, and data analysis. It eliminates routine paperwork, freeing up time for more strategic decisions.
Forecasting tools, on the other hand, analyse historical data, supply chain indicators, and real-time news sentiment to anticipate price movements and market trends.
A strong example comes from the French energy company TotalEnergies, which uses artificial intelligence to optimise oil production, improve predictive maintenance, and reduce energy waste across its facilities. AI tools also help the company identify optimal locations for renewable energy projects and forecast their potential output.
As Ievgenii Protsenko, Advisory Board member at Altamira, puts it:
At this moment, there are a lot of cases where AI is used for automation or forecasting in the industry. The next step is active decision-making, but to get there, we need stronger AI regulatory frameworks.
Final words
Artificial intelligence has already become a part of our daily lives. From unlocking our phones with facial recognition to receiving personalised recommendations while shopping online, it influences countless decisions and experiences every day.
Having a fundamental understanding of AI, its capabilities, limitations, and practical applications can position businesses as leaders in their industries. The first step toward long-term success is encouraging curiosity and actively experimenting with AI technologies.
Embracing AI will define the leaders of tomorrow’s business.
FAQ
Think of artificial intelligence as a set of techniques that let computers make decisions, spot patterns, and solve problems in a human-like fashion. At its core, AI involves:
Data – Information used to “teach” the system.
Algorithms – Rules or instructions that help the system learn from data.
Models – The trained systems that can then make predictions or perform tasks.
Contact us to learn more about AI.
Technically, there are more, but there are four core concepts you might encounter:
Machine Learning (ML): Can learn from data itself instead of following fixed instructions.
Neural Networks: Inspired by how human brains work, such systems are used in complex tasks like image or speech recognition.
Natural Language Processing (NLP): The area of AI that deals with understanding and generating human language.
Computer Vision: Focused on how machines interpret visual information like photos and videos.
AI basics for beginners are:
Python. It’s widely used and relatively easy to pick up.
Understanding basic statistics and probability. Artificial intelligence (AI) relies on making educated guesses based on data.
Trying small, hands-on projects. Start with tutorials that walk you through real examples—classification, predictions, and simple models.
Using free resources. There’s no shortage of accessible material online to help you learn step by step.
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