Technology is advancing at an unprecedented rate, and terms like “machine learning”, “deep learning”, and “neural networks” are no longer confined to research labs or Silicon Valley boardrooms. They are shaping how banks approve loans, how telcos manage customer churn, how oil and gas firms optimise drilling, and even how governments plan infrastructure.
For business executives and the general public, it is crucial to demystify these buzzwords. Understanding them doesn’t mean becoming a data scientist, but it does mean knowing enough to see opportunities, ask the right questions, and avoid costly mistakes.
What is machine learning?
Machine learning (ML) is the foundation of modern artificial intelligence. It’s about teaching computers to learn from data and improve over time without requiring explicit programming.
Using the banking sector as an example. A Nigerian bank handling millions of Naira in transactions daily must constantly be vigilant against fraud. Instead of relying on static rules, such as flagging only large transfers. Machine learning models can spot subtle anomalies: a customer suddenly making multiple small transfers late at night or unusual login behaviour from an unknown or foreign device. The system improves and becomes more effective the more transactions it analyses.
For executives, machine learning means moving from “rules-based” systems to adaptive ones that evolve with the business environment.
What is Deep Learning?
Deep learning is a specialised branch of machine learning inspired by how the human brain processes information. The term “deep” refers to the use of many layers of interconnected processing units. Each layer learns something more complex than the previous one.
“The best results come when executives combine human insight with machine-driven intelligence.”
Think of it like how a telco manages its vast customer base. A telecom operator with 50 million subscribers wants to predict which customers are likely to switch to competitors. A basic machine learning model might look at call frequency or data usage. However, a deep learning model goes further; it analyses dozens of data points, including network quality, customer complaints, payment patterns, and even social sentiment. With this, the telco can not only predict churn but also design tailored retention offers, saving millions of Naira in lost revenue.
For businesses, deep learning brings a big change in what’s possible: automating tasks once thought to require human intelligence.
Neural networks explained
The engine behind deep learning is the artificial neural network (ANN). Modelled loosely on the human brain’s network of neurones, an ANN consists of nodes (neurones) connected by links. Each connection carries a weight, and as data passes through the network, these weights adjust, strengthening or weakening connections until the system produces reliable results.
In the oil and gas industry, neural networks are already being utilised to enhance exploration and drilling efficiency. Consider an upstream operator analysing seismic data. The data is massive and noisy, but neural networks can learn to detect subtle patterns that point to the presence of oil or gas reserves. The technology helps geologists reduce guesswork, saving millions in drilling costs and minimising environmental risks.
A simple way to think about neural networks is how children learn. If a child touches a hot stove, they quickly avoid it next time. Neural networks operate similarly; they “learn” from errors and improve decisions over time.
Why these matter for business leaders
For business leaders, the importance of machine learning, deep learning, and neural networks lies in their potential to unlock efficiency and competitiveness. Companies that harness these tools can:
Automate repetitive processes, from banking compliance checks to telco billing queries.
Predict market trends by analysing vast volumes of structured and unstructured data.
Enhance customer experience through hyper-personalisation, tailored offers, and faster service.
Reduce risks via fraud detection in finance, predictive maintenance in telcos, drilling safety in oil and gas, etc.
At the same time, there are challenges, including data privacy concerns, ethical issues, high implementation costs, and the risk of overhyped expectations. Not every problem requires deep learning; sometimes simpler machine learning approaches work well.
The Human Element (Human in the Loop)
It’s easy to get carried away with the technical jargon, but ultimately, these technologies are tools. They don’t replace human judgement, creativity, or strategic thinking. Instead, they augment them. The best results come when executives combine human insight with machine-driven intelligence.
For example, a retail CEO doesn’t need to code a neural network, but they should know what to ask:
Do we have enough quality data to train an algorithm?
How will AI-driven insights affect customer relationships?
What guardrails do we need to ensure fairness and transparency?
Bottom line
Machine learning, deep learning, and neural networks are not abstract scientific concepts; they are practical tools shaping the present and future of business. From banks tightening fraud prevention to telcos retaining customers to oil and gas companies reducing exploration costs, the applications are tangible and measurable.
The key is not to fear the complexity but to engage with it. Executives who understand the basics will be better positioned to steer their organisations through the ongoing wave of digital transformation.
Just as electricity once transformed industries, intelligent systems powered by machine learning and neural networks are poised to do the same in our time. The question is: will your business adapt early and lead, or lag?
Dotun Adeoye is a seasoned technology strategist and AI innovation leader with over 30 years of global experience across Europe, North America, Asia, and Africa. He is the co-founder of AI in Nigeria.
