AI is transforming manufacturing by optimising processes, predicting equipment failures, enhancing quality control and streamlining supply chains through machine learning and data analysis.
Gary Hanniffy, director at PwC Ireland, moreover sees a wider context for the adoption of AI in the sector. Manufacturing is undergoing significant transformation due to a variety of factors, he says. These include the “green transition”; changes in global production and concerns over supply chain resilience; increased digitalisation; tightening regulation; and issues around workforce availability. Higher costs due to inflation and rising energy prices are creating additional pressure on the sector.
“AI, including traditional forms such as machine learning and newer developments like generative and agentic AI, is playing a crucial role in addressing these challenges. It is making it possible to revolutionise procurement, production, R&D and supply-chain processes, thereby reshaping the competitive landscape and enhancing value chains,” he says.
There is strong evidence that manufacturing companies believe in AI’s potential to increase profitability, says Hanniffy. Earlier this year, PwC’s 28th Annual Global CEO Survey: Reinvention on the edge of tomorrow reported on some early results from companies adopting GenAI, with more than half of CEOs (56 per cent) saying it has resulted in efficiencies in how employees use their time, while around a third report increased revenue (32 per cent) and profitability (34 per cent).
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“GenAI is now also complementing many AI-driven solutions already in use in operations in areas like supply chain and on the production floor, and also offers great potential for R&D and procurement,” he says.
Pat Phibbs, managing director of ALS Identify, agrees that AI is unlocking unprecedented efficiency and precision in manufacturing processes for businesses that have taken the leap.
“One of the most transformative advantages lies in defect detection and error prevention. Traditionally, a mislabelled product could go unnoticed for minutes, even hours, leading to wasted packaging, product and production time,” says Phibbs. “AI-driven vision systems, however, detect these errors instantly, allowing staff to rectify the issue in real time, minimising downtime, reducing waste and enhancing quality assurance.”
However, Phibbs says AI adoption in Irish manufacturing and supply-chain processes remains relatively low for now, with many organisations exploring possibilities but waiting to see what peers are doing before investing.
“When we speak to our customers, many are using AI in their non-manufacturing processes, such as predictive analytics, chatbots and sales support. Adding something so new into a critical production line can feel like a risky step for many.
“Many manufacturers feel like they’ve only just mastered the last wave of innovation, and now AI presents a whole new challenge that seems both immense and intimidating. In many ways, AI adoption is less about technology and more about psychology.”
Phibbs has noticed that, from his customer base, life sciences and food manufacturers are seeking out AI solutions for their processes more than others at the moment.
“Interestingly, adoption rates show little distinction between large and small enterprises,” he says. “A food customer of ours is looking for AI vision systems to identify immediately if a food product is labelled incorrectly based on visual imaging of the product as it’s being labelled. A life sciences company is using AI vision systems to detect tamper evident labelling defects to ensure compliance with EU regulations.”
The wider deployment of AI will clearly have implications for manufacturing jobs but Phibbs highlights the positive aspects of this.
“We’ve noticed our customers turning to AI to free employees from repetitive tasks and allow them to focus on higher-value roles. Companies increasingly use AI-driven automation to empower their workforce, shifting employees into upskilled positions that leverage human intelligence where it matters most.
“Rather than replacing jobs, AI is proving to be a powerful tool in workforce transformation, enabling businesses to retain and develop talent in new ways.”
One of the key manufacturing sectors in which AI is starting to have an impact is the highly skilled pharmaceutical area, with huge future implications forecast.
“Artificial intelligence and generative AI can significantly enhance connectivity across the pharmaceutical value chain, from ‘molecule’ to market,” says Lisa Goodman of Ibec’s BioPhamaChem Ireland group. “In manufacturing, AI-powered predictive maintenance could reduce unplanned downtime by 30 per cent to 40 per cent.
“Within supply chains, machine learning in demand forecasting promises to cut excess inventory, possibly by double-digit percentages, while maintaining service levels. These advancements strengthen operational efficiency and support sustainability goals like net-zero manufacturing.”
A recent manufacturing report by BioPharmaChem Ireland demonstrates the sector’s commitment to digital transition, with eight in 10 industry respondents planning to invest in digital and/or advanced manufacturing initiatives and more than half of companies [54 per cent], identifying AI is as a top three priority for their business.
“Our goal is to identify and implement AI use cases across various parts of the biopharmachem value chain, driving innovation and efficiency within the industry. As part of our work programme, BrightBeam is collaborating with our members to advance the concept of ‘workflow cognification’, a process that involves standardising documentation, using AI-driven quality assurance to detect patterns in historical reports and accelerating root cause analysis. It is projected that these efforts will lead to approximately a 30 per cent reduction in closure times while also improving overall quality,” says Goodman.
In common with other industry sectors, pharma is also facing a substantial talent gap, which requires an influx of AI-skilled professionals and necessitates new academic-industry collaborations focused on data science and AI model validation.
