45 www.drives.co.uk April 2025 Artificial intelligence has undoubtedly become a cornerstone in the transformation of UK manufacturing, propelling the industry into an era of increased efficiency, productivity, and innovation. As one of the pillars of the UK economy, manufacturing has long embraced advanced technologies to remain competitive on the global stage. From traditional machine-learning systems, to the arrival of generative AI, the landscape is evolving rapidly. Yet, this progress also demands careful consideration of its implications, including potential biases, ethical challenges, and the future of work in the industrial automation sector. To understand AI's impact, it is essential to distinguish between traditional AI and generative AI. Conventional AI applications in manufacturing have focused on optimisation and predictive analytics. For example, machine-learning algorithms are commonly employed in quality control, predictive maintenance, and supply chain management. These systems process historical data to identify patterns and make accurate predictions, helping manufacturers to cut downtime and operational costs. Generative AI, on the other hand, represents a paradigm shift. Unlike standard AI systems that analyse and classify data, generative AI models, such as GPT, create new content. In manufacturing, this opens up groundbreaking possibilities. Imagine generative AI designing complex machine parts, developing new materials, or even simulating entire production processes. The ability to generate innovative solutions, occasionally beyond human imagination, is reshaping how the industry approaches research and development. It is worth noting that manufacturing was an early adopter of machine learning. Decades ago, rudimentary systems were being integrated into industrial processes, automating repetitive tasks and enabling smarter decision-making. Over time, these systems have evolved into highly sophisticated tools capable of real-time analysis and autonomous adjustments. However, their success hinges on the availability of vast datasets. While machine learning excels in structured environments with clear parameters, its limitations become apparent in more nuanced or unpredictable scenarios. Generative AI builds on this legacy, offering unprecedented flexibility and creativity. However, it also comes with its share of challenges. One significant drawback of AI, including generative models, is the risk of bias. AI systems are only as good as the data they are trained on. In manufacturing, biased datasets could lead to flawed predictions or even discriminatory practices. For instance, an AI system designed to optimise hiring processes might inadvertently favour certain demographics if the training data reflects historical inequalities. Similarly, generative AI models can produce unintended or harmful outputs if not properly regulated. Ethical considerations also loom large. Questions around intellectual property, accountability, and the environmental impact of AI development are becoming increasingly pressing. Addressing these issues requires a collaborative effort between governments, industry leaders and technologists. A recurring concern in the AI debate is its impact on employment. While fears of widespread job displacement are understandable, the reality is more nuanced. AI, including generative AI, is unlikely to replace jobs outright. Instead, it will enhance human capabilities and create new opportunities. In industrial automation, for example, AI-powered systems can handle routine tasks, allowing workers to focus on more strategic and creative aspects of their roles. Moreover, the adoption of AI is spurring demand for new skillsets. Roles such as AI specialists, data scientists, and robotics engineers, are on the rise. Upskilling and reskilling programs are essential to ensure that workers can thrive in this evolving landscape. In the UK, initiatives such as Made Smarter exemplify efforts to integrate digital technologies while prioritising workforce development. Looking ahead, the integration of AI, particularly generative AI, will play a pivotal role in shaping the future of UK manufacturing. As industry grapples with challenges such as sustainability, supply chain resilience, and global competition, AI offers tools to innovate and adapt. However, its successful adoption hinges on addressing ethical concerns, mitigating biases, and fostering a culture of continuous learning. AI is not just a tool for optimisation; it is a catalyst for reimagining what is possible in manufacturing. Generative AI, with its transformative potential, is driving the industry towards a future where human creativity and technological innovation go hand in hand. Far from rendering human workers obsolete, AI is empowering them to achieve more – setting the stage for a new era of growth and ingenuity in UK manufacturing. n * Gambica is the trade association for the automation, control, instrumentation and laboratory technology sectors in the UK. You can get in touch with Nikesh Mistry on 020 7642 8094 or nikesh.mistry@gambica.org.uk, or via the Gambica Web site: www.gambica.org.uk AI has the potential to transform UK manufacturing The manufacturing sector is embracing arti cial intelligence, setting the stage for a new era of growth and ingenuity, argues Nikesh Mistry*, Gambica’s sector head for automation. But he cautions that we need to consider ethical concerns, biases and other issues when adopting AI.
RkJQdWJsaXNoZXIy MjQ0NzM=