Computer scientist Jonathan De Vita studied coding and artificial intelligence as part of his degree. This article will look at the many ways that AI innovations and advancements are reshaping the future of virtually every industry today.
AI, and particularly generative AI, has increased task automation in many organisations and will likely continue to do so for many years to come. The rise of digital assistants and chatbots enables businesses to rely on AI to handle simple customer queries, as well as answering basic questions from employees. The ever-increasing proliferation of AI and automation has sparked fear of job losses among many. However, experts suggest that AI is more likely to augment existing roles rather than replace them outright, prompting forward-looking organisations to increase their upskilling efforts.
A perquisite for successful integration of AI is educating and retraining staff. Although increased implementation of AI is predicted to lead to circa 44% of employees’ roles being disrupted according to statistics shared by Built In, AI is having an unequal impact on different professions and industries. While repetitive tasks like data entry and customer service are already being automated, the digital revolution is sparking a considerable uptick in demand for other roles such as information security analysts and machine learning specialists. Experts predict that workers in creative roles will have their jobs augmented by AI rather than being replaced by it outright. Whether taking over tasks completely or forcing employees to broaden their skill set, AI is predicted to spur upskilling efforts at both an individual and organisational level.
One branch of AI is machine learning, which involves creating models by training algorithms to make data-based predictions or decisions. It encompasses a broad range of subcategories that enable computers to make data-based inferences and learn without being explicitly programmed for specific tasks. Machine learning techniques and algorithms include linear regression, decision trees, logistic regression, support vector machines, random forest, clustering, k-nearest neighbour and more. Each of these approaches is suited to different types of problems and data.
Supervised learning is the simplest form of machine learning. This involves using labelled data sets to train algorithms to predict outcomes or classify data. In supervised learning, humans pair training examples with output labels. The goal is for the model to learn mapping between inputs and outputs in the training data, enabling it to predict the labels of new and unseen data.
Deep learning is a machine learning subset that relies on multilayered neural networks known as ‘deep neural networks’ that simulate the complex decision-making capabilities of the human brain. Deep neural networks incorporate an input layer, hidden layers and an output layer. While classic machine learning models usually have one or two hidden layers, deep neural networks can have hundreds. These multiple layers pave the way for unsupervised learning, facilitating the extraction of features from vast unstructured and unlabelled datasets, enabling deep neural network technology to make its own predictions about what the data represents. As deep learning does not require human intervention, it paves the way for machine learning at a tremendous scale. Most AI applications seen in daily life today are driven by some form of deep learning powers.
AI has a multitude of benefits in the business world, reducing human error and bias, enhancing decision-making, automating repetitive tasks, reducing physical risk, and providing round-the-clock availability and consistency. AI is already fuelling growth via emerging technologies such as robotics, big data and IoT, with generative AI further expanding the popularity and possibilities of AI. As of 2024, approximately 42% of enterprise-scale organisations were already actively deploying AI across their businesses, with 92% of companies planning to increase their AI technology investments from 2025 to 2028.
