Machine Learning (ML), a branch of Artificial Intelligence (AI), identifies patterns and insights beyond human reach by analysing large datasets at high speed, unlocking valuable information from the data. This technology boosts business efficiency by enabling smarter, more informed decision-making, automating tedious and repetitive tasks, and reducing operational costs. A simple explanation of its basics is provided in this video: How does AI work?
ML is transforming industries, solving complex problems faster and more accurately than ever before. AI technologies, including ML, have been described as “as transformative as the Industrial Revolution“, highlighting their profound potential to revolutionise the way businesses and industries function. The impact is such that AI is predicted to drive 45% of total economic gains by 2030, enabling the creation of personalised experiences to meet consumer needs more effectively.
The use of AI has exploded globally, more than doubling since 2017. By 2023, 55% of organisations worldwide had actively integrated AI into their operations. In Europe, 30.4% of large enterprises had adopted AI technologies, while adoption among small and medium-sized enterprises (SMEs) stood at just 7%.
Key sectors like healthcare, finance, and logistics are already leveraging ML to optimise processes and solve challenges once out of reach.
For example, IBM notes that 30% of employees are already saving time thanks to AI tools, and adopting intelligent automation is expected to reduce operational costs by 31% within three years.
In healthcare, Machine Learning can be used to develop more effective and personalised treatments for patients. For example, Insilico Medicine, a biotechnology company, is leveraging ML to design new drugs for cancer and other diseases.
At the same time, in manufacturing, General Electric has shown how predictive maintenance powered by ML can cut unexpected downtime by 40% and lower maintenance costs by 20%, using sensor data to predict equipment failures.
In addition, personalisation powered by Machine Learning has transformed operations in retail. H&M uses AI to predict demand and optimise store locations, enhancing inventory management and boosting sales by aligning stores with customer needs. Small businesses are also using personalisation to grow their sales. For example, the sustainable fashion retailer EcoWear leveraged AI-driven personalisation to improve customer service, increasing repeat purchases by 20%, reducing returns by 15%, and doubling its sales within six months. SMEs can also benefit by reducing repetitive tasks—like the coffee retailer Grind, which partnered with Google to automate processes, boosting productivity and making their operations more efficient.
These examples showcase the wide range of applications and potential Machine Learning offers to businesses, reflecting the rapid growth of the AI market, which is expected to reach USD 638.23 billion in 2024 and an incredible USD 3,680.47 billion by 2034. However, while ML adoption has accelerated among larger organisations, many SMEs remain hesitant due to a limited understanding of its potential benefits. The truth is that AI offers significant advantages for SMEs as well, as explored in this video: AI Could Empower Any Business.
Training is essential to bridging this gap, helping SMEs identify how ML can make a real difference and how to manage the quality data needed to make it work. With the right tools and training, even small businesses can learn to exploit its potential and make it work for them. To meet this need, the DigiAdvance project addresses digital skills gaps in the SME sector through low-cost, demand-driven training tailored to SME owners, managers, and employees.
With this objective in mind, DigiAdvance offers a series of focused courses on Machine Learning designed to equip learners with practical and theoretical expertise. The Data Representation Fundamentals course teaches how to organise information into formats suitable for ML systems, while the Data Representation in the Real World focuses on real-world ML tasks through hands-on examples.
For those exploring more advanced concepts, the Deep Learning course explores Neural Networks and how they are used to solve real-world problems, while the Neural Networks course explores models inspired by biological neurons driving breakthroughs in Machine Learning.
In addition, DigiAdvance offers two microcredentials for applied learning. The Microcredential in Python Programming for Data Analysis focuses on programming and data analysis skills essential for ML. The Microcredential in Applied Machine Learning provides hands-on training in applying ML techniques effectively in practice.
Machine Learning is driving innovation, enabling businesses to work more efficiently. As the world becomes more digital, adopting ML is becoming essential for staying competitive and unlocking growth.