Technology has advanced at unprecedented rates, with terms like “machine learning,” “deep learning,” and “neural networks” no longer being confined to labs and Silicon Valley boardrooms. They form how banks approve loans, how carriers manage customer terminations, how oil and gas companies optimize drilling, and even how governments plan infrastructure.
It is important for executives and the general public to make these buzzwords clear. Understanding them doesn't mean becoming a data scientist, but it means knowing enough to see the opportunity, asking the right questions and avoiding costly mistakes.
What is machine learning?
Machine learning (ML) is the foundation of modern artificial intelligence. It is to teach computers to learn from data and improve over time without the need for explicit programming.
Use the banking sector as an example. Nigerian banks, which handle millions of Naira in their daily transactions, must be on the lookout for fraud. Instead of relying on static rules, such as flagging only large forwardings. Machine learning models can spot subtle anomalies. The customer suddenly makes multiple small transfers late at night, causing abnormal login behavior from unknown or foreign devices. The more transactions the more the system analyzes, the better and more effective it becomes.
For executives, machine learning means moving from a “rule-based” system to an adaptive system that evolves with the business environment.
What is deep learning?
Deep learning is a specialist in 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 complicated than the previous layer.
“The best results come when executives combine human insights with machine-driven intelligence.”
Think of it as if the phone company manages its vast customer base. The carrier with 50 million subscribers wants to predict which customers may switch to their competitors. A basic machine learning model may look at the frequency of calls or data usage. However, deep learning models go even further. Analyze dozens of data points, including network quality, customer complaints, payment patterns, and even social emotions. This allows the telephone company to not only predict termination, but also design customized retention offers and save on revenues that have lost millions of Naira.
For businesses, deep learning is a major change in what is possible. It is to automate tasks that were once thought to require human intelligence.
Neural Network explained
The engine behind deep learning is an artificial neural network (ANN). An ANN, which roughly models a network of neurons in the human brain, consists of nodes (neurons) connected by links. Each connection has weights, and as data passes through the network, these weights coordinate, enhance or weaken the connection until the system produces reliable results.
Neural networks are already being used in the oil and gas industry to increase exploration and drilling efficiency. Consider upstream operators that analyze seismic data. Although the data is large and loud, neural networks can learn to detect subtle patterns that refer to the presence of oil or gas reserves. The technology helps geologists reduce speculation, save millions of drilling costs, and minimize environmental risks.
A simple way to think about neural networks is how children learn. If the child touches the hot stove, avoid it immediately next time. Neural networks work in the same way. They “learn” from errors and improve their decision making over time.
Why are these important to business leaders?
For business leaders, the importance of machine learning, deep learning and neural networks lies in the potential to unleash efficiency and competitiveness. Companies who use these tools include:
Automate the iterative process from banking compliance checks to phone billing queries.
Predict market trends by analyzing a large amount of structured and unstructured data.
Enhance your customer experience through hyper personalization, tailored offers and faster services.
Reduce risk through fraud detection in finance, predicted telecoms maintenance, and drilling oil and gas safety.
At the same time, there are challenges such as data privacy concerns, ethical issues, high implementation costs and risks of exaggerating expectations. Not all problems require deep learning. Sometimes a simpler machine learning approach works well.
Human elements (loop humans)
It's easy to get hooked on technical terms, but ultimately these technologies are tools. They do not replace human judgment, creativity, or strategic thinking. Instead, they augment them. The best results come when executives combine human insights with machine-driven intelligence.
For example, retail CEOs don't need to code neural networks, but they need to know what to ask.
Do you have enough quality data to train the algorithm?
How do AI-driven insights impact customer relationships?
What guardrails do you need to ensure fairness and transparency?
Conclusion
Machine learning, deep learning, neural networks are not abstract scientific concepts. They are practical tools that shape the present and future of your business. From banks to enhanced fraud prevention, to oil and gas companies to carriers that retain customers, applications are concrete and measurable to reduce exploration costs.
The key is not to fear complexity, but to be involved in it. An executive with a basic understanding would be better positioned to pilot an organization through the ongoing wave of digital transformation.
Just as electricity once transformed the industry, intelligent systems with machine learning and neural networks are poised to do the same in our time. The question is whether your business will adapt early and lead or delayed.
Dotun Adeoye is a veteran technology strategist and AI innovation leader with over 30 years of global experience in Europe, North America, Asia and Africa. He is the co-founder of AI in Nigeria.
