How learning trends will reshape European manufacturing

Automation and artificial intelligence (AI) are redefining not only what people do but how they learn. The next decade of manufacturing will be led by those who can evolve as fast as technology does.

As manufacturing is moving towards smart production systems, traditional manual roles are transforming. Workers use digital twins and AI-powered decision tools, while factories adopt predictive maintenance, additive manufacturing and energy-efficient processes. Employees increasingly need hybrid skills that blend technical expertise with analytical and digital capabilities.

In this new landscape, learning and development (L&D) is no longer optional for business success. It enables talent to adapt and fuels innovation, competitiveness and resilience across the industrial sector.

Learning in the flow of work

According to McKinsey, the future of workplace learning lies in fluid development ecosystems, where learning becomes part of everyday work rather than a separate activity. The boundary between learning and work is disappearing, as organisations increasingly design daily tasks to ‘stretch’ employees to build new skills without creating unnecessary pressure.

The shift is supported by AI copilots, intelligent assistants that act as real-time mentors. They provide personalised feedback, suggest learning resources and guide people through their daily tasks based on real performance data.

This approach requires stronger collaboration across human resources (HR), L&D and operations, with data playing a central role. Organisations that build data-driven learning ecosystems — where analytics track how skills evolve and where gaps appear — are better equipped to adapt to change.

Coming from the perspective of the leading manufacturer of advanced multi-sensor unmanned aerial systems (UAS), Kyrylo Kozhemiakin, Team Lead Talent Acquisition Management at Quantum Systems, remarks: “As automation and AI mature, strong technical expertise remains essential. The technical specialist is still in charge of aligning changes with other stakeholders and systems. We increasingly look for engineers who can communicate clearly, collaborate across disciplines and adapt quickly to new tools and workflows. The ability to learn continuously and work effectively with AI systems has become a key differentiator.”

Balancing technical expertise with soft skills

By 2030, global employment will grow by about 7%, according to the World Economic Forum. This is equivalent to a net gain of 78 million jobs, as 170 million new roles are created and 92 million are displaced. The challenge lies not in job loss, but in keeping skills relevant amid technological change.

In manufacturing, the skills gap is the main barrier to transformation. The fastest-growing roles are linked to data, automation and the green transition, including AI and machine learning specialists, data analysts and renewable energy engineers. Meanwhile, the skills expected to grow most in importance are AI and big data, technological literacy, resilience and agility.

Reflecting on the changing balance between technical and soft skills, Kozhemiakin notes: “Over the next five years, the manufacturing workforce will need a mix of digital, systems and sustainability-related skills. This includes robotics and autonomous systems, data literacy, AI-enabled decision-making and a solid understanding of energy efficiency and lifecycle thinking. Equally important are problem-solving skills and the ability to operate in fast-changing, technology-driven environments.”

Embedding learning trends for seamless skill growth

Across industries, learning is becoming shorter, more personal and embedded into the flow of work, shaped by four trends.

Microlearning allows professionals to learn in short bursts – five minutes to watch, 10 to try, 15 to reflect – fitting knowledge into busy schedules while keeping it practical and actionable. In manufacturing, this often means a quick learning nugget or a training pill on machine setup, safety updates, quality checks or new production procedures that workers can apply immediately on the shop floor.

Personalised learning recognises that no two learners are alike. Training that adapts to an individual’s style, rhythm and pace ensures better retention and motivation. AI and digital learning systems make it easier to build such adaptive paths, helping learners focus on what matters most for their growth. Maintenance engineers may receive different modules based on the machines they work with, while operators get tailored refreshers on the tasks they perform most frequently.

Active learning transforms information into practice. Reading or listening is only the beginning. Real learning happens through experimentation, testing and application. The most valuable lessons come from doing, reflecting and improving. In manufacturing contexts, this often means simulations of production scenarios, digital twins, hands-on exercises with robotics or problem-solving tasks based on real factory cases.

Finally, Generative AI (GenAI) is changing the learning landscape as a tutor and assistant. GenAI refers to AI systems that create new content, such as explanations, exercises, examples or simulations, and are based on user input and context. It enables on-demand guidance, helps to clarify complex ideas and provides instant, personalised feedback. In manufacturing, GenAI can explain equipment failures, generate maintenance checklists, create practice datasets for AI training or simulate production decisions. The key is finding the right balance between “teach me” and “do it with me”, using AI to support learning without replacing human curiosity and judgement.

Highlighting the shift in new learning trends observed by Estonia’s leading institution for engineering, IT and technology-focused education, Tauno Otto, Vice Dean for Development of School of Engineering at Tallinn University of Technology (TalTech), comments: “At TalTech, we see a rapid increase in interest in engineering studies. In response, we are embedding core engineering competences across a wide range of study programmes and systematically shifting towards problem- and project-based learning. The integration of AI into the learning process is strongly encouraged: academic staff have access to ChatGPT EDU licences, enabling AI-supported tutoring and personalised learning support. At the same time, we place strong emphasis on maintaining solid foundations in engineering fundamentals, critical thinking and responsible use of AI.”

Translating trends into action

EIT Manufacturing Academy translates these global trends into practice. It serves as a European hub where industry, universities and training providers co-create digital and blended learning content tailored to real industrial needs.

Through its Platform-as-a-Service model, the EIT Manufacturing Academy enables partners to design and distribute courses that support continuous, in-the-flow learning, aligned with the shift towards skills-based and data-driven development. By combining flexibility, analytics and learner autonomy, organisations can turn education into a strategic tool for resilience and innovation.

Professionals today can access learning paths that keep pace with the latest shifts in manufacturing, from understanding digital twins and interoperability through the asset administration shell, to designing high-efficiency heat exchangers with additive manufacturing. Learners can also deepen their expertise in multi-material design for additive manufacturing or explore the emerging field of 4D printing, where materials evolve and adapt to their environment.

Shaping a learning-driven future

By 2030, nearly four in 10 core skills will change. A learner-centred and industry-aligned approach to education is key to helping Europe translate global insights into action. By prioritising lifelong learning and upskilling, organisations can ensure that the manufacturing workforce remains skilled, adaptable and prepared for the future.

As McKinsey’s research notes, work itself is becoming a powerful engine of development. To keep pace with continuous technological and organisational change, learning must happen in the flow of work. Companies embedding continuous learning into their daily operations will be best positioned to lead the next decade of industrial growth.