How AI is rewriting the rules on the factory floor

Artificial intelligence (AI) is rapidly becoming a defining topic across the manufacturing sector

While much of the public debate focuses on large language models (LLMs), such as OpenAI’s ChatGPT, AI’s impact in industry extends far beyond its role as a digital assistant or content generator.

A new wave of sector-specific applications is beginning to emerge, shaping how manufacturers design processes, make decisions, and unlock value from the data running through their operations.

The shift comes at a time when production systems are becoming more complex, supply chains more demanding, and expectations around efficiency and quality higher than ever. Traditional tools are struggling to keep pace, prompting manufacturers to examine how AI can support activities that have long relied on intuition or manual monitoring.

Across the sector, projects are demonstrating how targeted AI models can improve accuracy, strengthen process control, and provide clearer visibility on the factory floor. As momentum builds, the question is no longer whether AI will influence manufacturing, but how deeply it will permeate engineering practice and what foundations are required to unlock its full potential.

Data access is central to that progress. At the National Manufacturing Institute Scotland, our researchers are developing new AI capabilities while also generating the datasets needed to make them viable in real-world settings. Even the most sophisticated software relies on robust training data – without it, its value quickly diminishes.

Practical examples are already emerging. NMIS is working with a major aerospace company to model how a component behaves during forging – a highly complex process in which some of the underlying physics are still not widely understood. By combining machine learning with targeted trials, the team is producing datasets that help predict changes in material properties, giving engineers the insight they need to design and optimise processes with greater precision – reducing the number of trials, cutting waste, and improving overall efficiency.

Innovation is also accelerating across the wider ecosystem, with more companies developing AI tools for design, simulation, and factory-level decision-making. One UK software developer, for example, is helping engineers extract value from Computer-Aided Design (CAD) data through its HOOPS AI platform – a framework that integrates access, preparation, and training of machine-learning models using 3D geometry.

Elsewhere, an emerging engineering platform is slashing traditional simulation times from days to seconds with AI-powered models trained on existing simulation data. Engineers can refine designs in real time, speeding up development and reducing reliance on lengthy physical or computer-based testing.

NMIS is also working with academic partners, including colleagues at the University of Strathclyde and researchers at the University of Edinburgh, to apply AI to factory operations. By capturing raw data streams from equipment such as forklifts, new algorithms can track parts, identify inefficiencies and bottlenecks, and highlight opportunities to improve flow and minimise downtime within production areas.

Despite the progress being made, AI adoption on the factory floor is still at an early stage. Through its Data-Driven Design and Manufacturing Colab project, part of the Glasgow City Region Innovation Accelerator programme and funded through Innovate UK on behalf of UK Research and Innovation, NMIS is working with organisations to bridge the gap between manufacturing and digital technologies, giving engineers the skills and confidence to apply data-driven methods within their own businesses. So far, more than 120 projects – spanning aerospace, energy, food and drink, construction, and electronics – have already demonstrated how data-led approaches can cut emissions, improve component accuracy, and enhance reliability.

AI is already influencing how manufacturers design processes and make decisions. As targeted tools continue to mature, the opportunity now lies in embedding them more deeply into engineering operations and ensuring the workforce has the capability to use them effectively. This shift – from broad debate to practical integration – is where AI will deliver its most tangible impact, helping manufacturers unlock greater resilience and value from the data that underpins modern production.

NMIS is operated by the University of Strathclyde and part of the High Value Manufacturing (HVM) Catapult.