Mark Venables spoke to Rohan Whitehead, Data Training Specialist at the Institute of Analytics, to discuss AI in manufacturing and why the path to AI adoption is fraught with challenges that need to be carefully navigated
Artificial intelligence (AI) is profoundly reshaping the manufacturing industry, offering tools that enhance productivity, improve safety, and streamline operations. From predictive maintenance to supply chain optimisation, AI has the potential to revolutionise traditional manufacturing processes.
One of the most impactful applications of AI in manufacturing is predictive maintenance, where machine learning algorithms are used to forecast equipment failures before they happen. “GE’s use of AI to monitor its equipment for signs of wear and tear has helped avoid costly breakdowns in its manufacturing plants.” Rohan Whitehead, Data Training Specialist at the Institute of Analytics, says. “By analysing vast amounts of data in real-time, AI systems can predict when machines need service, reducing downtime by up to 30% and extending machine life by 20%.”
However, while the benefits are clear, the implementation of AI comes with significant hurdles. “One of the primary concerns I have is the significant upfront investment needed, not just in technology but in data infrastructure and talent,” Whitehead adds. Companies often underestimate the complexities of integrating AI with their existing systems, and the lag between the release of new AI models and their practical implementation can delay these anticipated benefits.
Generative AI in product design
Generative AI is another area that is making waves in manufacturing, particularly in product design. Whitehead shares the example of Autodesk’s generative design software, which helps industries like automotive manufacturing rapidly iterate on design concepts. “Airbus used generative AI to design lighter aircraft parts, cutting material costs and fuel consumption,” he says. This innovative approach accelerates the design phase, giving engineers more time to focus on optimisation and innovation.”
However, Whitehead cautions that the effectiveness of generative AI is highly dependent on the quality of the input data. “If the data is flawed or biased, the results may not be as effective as anticipated,” he warns, leading to inefficiency in production. “It’s important for executives to temper their expectations and ensure robust data management practices are in place. Organisations like the Institute of Analytics play a role in helping businesses manage and monitor data integrity.”
AI in supply chain optimisation
AI’s ability to optimise supply chains is a significant benefit for manufacturers. Companies like DHL have successfully used AI to predict shipping delays and optimise logistics networks. “By analysing weather patterns, traffic conditions, and other factors, AI helps the company plan better routes, avoiding disruptions and saving costs,” Whitehead explains. “In manufacturing, AI can forecast demand more accurately and reduce excess inventory, leading to substantial savings.”
Yet, as Whitehead points out, AI is not a perfect solution. “Supply chains are incredibly complex, and AI systems can struggle with unpredictable disruptions, such as those caused by geopolitical events or pandemics,” he says. “While AI can provide better visibility and agility, manufacturers must maintain realistic expectations and see AI as part of a broader strategy. The Institute of Analytics helps executives think critically about where AI fits into their business strategy.”
Scaling AI in manufacturing
Scaling AI beyond initial pilot projects remains a common challenge for manufacturers. “According to Bain & Company, nearly 80 per cent of manufacturers see scaling AI as a priority, but many struggle to move beyond early-stage pilots,” Whitehead continues. “The difficulty lies in integrating AI with legacy systems that may not be equipped to handle the vast amounts of data needed for effective AI implementation. Additionally, the lack of in-house AI talent is a significant barrier.
“Executives need to take a more measured approach,” Whitehead advises. AI should be seen as a long-term investment rather than a quick fix.” Cultivating an AI-first culture, where ongoing learning and development are prioritised, is critical. The Institute of Analytics offers resources and training to help manufacturers build the skills necessary to scale AI successfully.
Ethical Considerations in AI Implementation
Ethical considerations must not be overlooked throughout the discussion of AI’s technical capabilities. One of Whitehead’s key concerns is the potential for job displacement. “While AI can automate many tasks, there’s a risk that some workers will be left behind,” he warns. “Companies must be proactive in offering retraining and re-skilling opportunities. The Institute of Analytics strongly advocates for this, ensuring that AI implementation doesn’t leave workers behind.”
Moreover, the issue of bias in AI systems cannot be ignored. “AI systems often inherit biases from the data they are trained on,” continues Whitehead, which can lead to unintended consequences in decision-making processes. For example, a biased AI system used in quality control could unfairly penalise certain product lines or even human operators. “This is why ethical AI deployment must be a priority,” Whitehead emphasises. “The Institute of Analytics offers frameworks for responsible AI use, ensuring that human oversight remains central to decision-making.”
Managing expectations for AI
AI’s potential in manufacturing is undeniable, offering the promise of enhanced efficiency, reduced costs, and improved operations. However, as Whitehead underscores, managing expectations and addressing both the technical and ethical challenges head-on is crucial. “By integrating AI thoughtfully and ensuring robust data management practices, manufacturers can unlock the true potential of AI while maintaining their broader responsibilities to their workforce and society,” he concludes. “AI should be seen as a long-term investment. Manufacturers can harness AI’s transformative power by taking a measured, ethical approach without losing sight of their broader obligations.”
