Machine learning is slowly touching every area of our lives. From finance, to healthcare and marketing, these AI algorithms are evolving in amazing ways. And it’s showing no signs of slowing down.
According to Statista, the Machine Learning market in the United Kingdom is projected to grow by 34.79% (2025-2030) resulting in a market volume of US$16.24bn in 2030. This makes it an exciting industry for companies and investors to watch.
What Is The Difference Between AI and Machine Learning?
Many people use the terms artificial intelligence and machine learning interchangeably, but it’s important to understand how they differ.
AI is all about creating machines that think like humans. By inputting and processing huge amounts of information, both qualitative and quantitative, AI is able to problem solve and understand content – allowing it to ‘think’ like an intelligent human.
Machine learning is a branch of AI that uses algorithms, data and statistics to learn and predict certain situations. For example, by looking at huge data sets, machine learning algorithms are able to predict patterns that may follow.
In essence, machine learning is one of the elements that AI uses to learn.
What Sectors Use Machine Learning?
Machine learning and data analytics are used across tons of industries, including:
- Healthcare
- Farming and agriculture
- Retail
- Energy
- Hospitality
- Finance
- Marketing
- Transport
- Chatbots
- Cybersecurity
To find out what 2025 might have in store for machine learning, we asked the experts. Here’s what they had to say:
Our Experts
- Tom Burton, Founder at Digility Ltd
- Laura Tristram, Co-Founder at Lumii.life and Lumii.me
- Paul Massey, Co-Founder at RTriibe
- Dr. Harro Stokman, CEO at Kepler Visions Technologies
- Christian McCaffery, CTO at VeUP
- Aaron Saxton, Director of Disruptive Education at University Academy 92 (UA92)
For any questions, comments or features, please contact us directly.
Tom Burton, Founder at Digility Ltd
“While GenerativeAI (using LLMs) is a specialisation of Machine Learning I’ll treat them distinctly because I see GenAI having a significant competitive impact on ML in 2025. 2024 was the year GenAI broke into the public consciousness, generating huge attention. I see this continuing through 2025 and at the expense of harder investments in ML.
“Delivering value with ML is hard. The engineering requires hard intellectual thought by highly qualified people. The data needs to be prepared to a high standard, and with many ‘data lakes’ resembling ‘data swamps’ this can appear insurmountable. The objective also needs to be well defined, so the hard thinking starts at the outset.
“In contrast GenAI gives the perception that you can pour your data in, ask the model a few questions, and it will magic up the desired answers. But the models are so complex with billions of parameters that they are almost impossible for a human to properly interpret and trace the inputs to deterministic outputs.
“There are huge areas where GenAI will deliver previously unachievable value, but in 2025 I see many of those that would be better served by conventional ML suffering from the illusion of an easier option.
Laura Tristram Co-Founder at Lumii.life and Lumii.me
“In 2025, machine learning will be fundamentally reshaping the mental health landscape. At Lumii.life and Lumii.me we’re using a large-scale mental health-focused AI model to empower users to understand their mental well-being through meaningful interactions. In the next year, we see machine learning moving towards a richer, more empathetic user experience. Multimodal learning will be a game changer, allowing AI to process a user’s text, tone of voice, and facial expressions holistically, tailoring support in real-time.
“Explainable AI (XAI) will also be crucial, particularly in mental health, where trust is everything. Users will demand to know how recommendations are made and why certain coping strategies are suggested. Ethical AI considerations will be front and centre, ensuring that models are free from bias and sensitive to diverse user needs.
“Additionally, edge AI will allow for greater privacy by processing data locally rather than in the cloud—critical for personal health data. Low-code platforms will empower smaller organisations to harness ML for niche support solutions. By 2025, we’ll be closer than ever to a world where AI offers not just tools, but human-like care, helping people achieve meaningful progress in their mental health journey.”
Paul Massey Co-Founder at RTriibe
“At RTriibe, our AI-driven recruitment tools are transforming how headteachers find talent. By 2025, machine learning will push the boundaries of recruitment technology even further. Multimodal learning will enhance candidate profiling, analysing not just CVs but also video interviews, online presence, and more, delivering a 360-degree view of every applicant.
“Explainable AI will be critical here too. Employers want confidence in their hiring decisions, so transparency around why a candidate is shortlisted is essential—especially in sensitive roles like education. AutoML and no-code platforms will allow schools to customise recruitment workflows without needing tech expertise.
“Generative AI will also play a big role in creating tailored job descriptions or personalised communications with candidates, streamlining processes for busy headteachers. Importantly, ethical AI will remain at the heart of recruitment, helping reduce unconscious bias and promoting diversity in education.
“The shift to edge AI could even allow on-premises data processing for sensitive recruitment tasks, maintaining data security while enabling real-time decision-making. By 2025, these innovations will make recruitment smarter, faster, and more inclusive, ensuring every school can find its perfect team member.”
For any questions, comments or features, please contact us directly.
Dr Harro Stokman, CEO at Kepler Visions Technologies
“In 2025, machine learning, particularly in healthcare computer vision, is set to evolve in three key ways:
- Advancement in Camera Technology: Camera manufacturers will integrate advanced chipsets capable of running generative AI applications, moving beyond older chipsets that only supported classic machine learning models like convolutional neural networks. This will meet the growing demand for real-time, AI-powered insights directly from the source.
- Enhanced AI-Driven Summaries: Customers will expect AI systems to provide actionable summaries of events in care facilities. For instance, AI will highlight which wards had resource-intensive nights, enabling better allocation of staff and improving patient outcomes.
- Impact of the European AI Act: As the EU’s AI Act phases in, compliance will become a significant challenge. While startups may initially dismiss it as a responsibility for larger tech companies, the Act’s requirements—such as documenting the creation process of general-purpose AI models—will also apply to fine-tuned models. This will spark debates on semantics: Is my AI application with niche, focussed functionality a general-purpose application?”
Christian McCaffery, CTO at VeUP
“In 2025, Machine Learning will continue to drive innovation across sectors such as healthcare, finance, and customer service. The focus will shift to more advanced, faster models, empowering businesses to make smarter, data-driven decisions.
“ML will also expand its role in improving operational efficiency, reducing human error, and optimising processes. This evolution aligns with the broader 2030 vision for tech development, where AI and ML are central to regional funding strategies—especially in areas like MENA and North America. To truly unlock the potential of ML, businesses must leverage technical excellence and maintain a growth-first mindset, ensuring they stay ahead of the curve.
“Ensuring the ethical use of AI will be essential, balancing innovation with transparency and fairness as these technologies become integral to everyday life.”
Aaron Saxton, Director of Disruptive Education at University Academy 92 (UA92)
“At UA92, AI and ML are transforming how we prepare students and apprentices for careers in computer science and cyber disciplines. Across our undergraduate and apprenticeship programmes, including DevOps, Cloud Computing, and Linux, AI is accelerating the development of future-ready engineers and delivering unmatched value to our employer partners.
“Leveraging cutting-edge AI tools, we empower students to master programming and infrastructure as code (IaC) across leading cloud platforms such as AWS and Azure. What traditionally required weeks or months to teach and implement now takes mere hours or days, enabling learners to contribute to their organisations with unprecedented speed and effectiveness.
“Through AI-powered tools like Copilot and ChatGPT, our students are not only learning technical skills faster but are also adopting innovative cloud technologies more effectively. These advancements enhance productivity and bring immediate value to businesses, as apprentices and graduates transition seamlessly into real-world engineering roles.
“By embedding AI into our curriculum, we are revolutionising technical education and ensuring our learners remain at the forefront of technological innovation, driving progress for their organisations.”
For any questions, comments or features, please contact us directly.
