AI is changing how manufacturers operate, from streamlining processes to enhancing efficiency and decision-making. Mark Venables explores the key opportunities, challenges, and future trends that are shaping the role of AI in manufacturing.
The rapid advancement of AI technology is reshaping the manufacturing industry, creating new opportunities to streamline processes, enhance efficiency, and drive innovation. Yet, significant barriers remain, including fragmented data systems and the urgent need for new skill sets. Manufacturers must navigate these challenges to unlock AI’s full potential and transform their operations.
AI’s transformative impact
AI has been a part of manufacturing for years, enabling automation and efficiency. “Generative AI has introduced a new way for companies to interact with data and streamline processes,” Matt Iliffe, CEO at AI and Cloud agency Beyond explains. “Unlike traditional automation, generative AI provides manufacturers with the power to synthesise data, generate reports, automate repetitive tasks, and issue instructions in a more intuitive manner. AI can now help create interfaces that provide better access to real-time operational data, allowing manufacturers to explore things like demand forecasting, compliance, and even manage knowledge in new and exciting ways.”
Generative AI has brought a different layer of intelligence to the factory floor, one that makes knowledge accessible on demand and supports automated reporting, significantly improving operational visibility. “In terms of knowledge management, AI can make mission-critical data accessible on demand, ensuring employees have the information they need when they need it,” Iliffe continues. “This can be especially transformative for industries that rely on continuous production, where immediate access to data can mean the difference between efficient operation and costly downtime.”
AI’s impact is not only technological but also about redefining user experiences and making sophisticated systems more accessible to non-experts. “A friend of mine is using generative AI to bring manufacturing back to the US by simplifying how companies can set up and manage robotics, especially for small and medium-sized businesses,” Iliffe adds. “In this context, AI is not just a tool for large corporations but a technology that levels the playing field, allowing even smaller manufacturers to benefit from advanced automation and robotics.”
Overcoming barriers to AI adoption at scale
Despite its promise, adopting AI at scale is still fraught with challenges. Many companies struggle to move past the proof-of-concept phase due to issues like fragmented data, poor data quality, and insufficient infrastructure. “Adoption at scale remains a challenge,” Iliffe explains. “Many AI use cases fail to progress past the proof-of-concept stage due to issues with data quality, infrastructure, and the right skills or processes to implement them.”
The issue of fragmented data is particularly pronounced in manufacturing, where data silos can become a major barrier to creating a unified AI solution. “One client had over 200 data repositories spread across different brands and regions,” Iliffe says. “This kind of setup makes it nearly impossible to create unified services or solutions. The solution lies in data modernisation, consolidating and centralising repositories to provide a solid foundation for building scalable, intelligent tools. Beyond’s approach to data modernisation has enabled its clients to innovate more effectively by building the next generation of tools on a stable, unified data architecture.”
Data quality also plays a crucial role. It is often garbage in, garbage out, underscoring the importance of enriching data to make it usable for advanced AI applications. The process involves creating structured storage solutions and refining datasets to ensure they meet the requirements of machine learning applications. Many organisations, Iliffe observes, have not collected data with AI in mind, so there is often work needed to prepare it for these advanced use cases.
Beyond’s partnership with Google Cloud plays a critical role in addressing these challenges. Leveraging Google Cloud’s MLOps capabilities, Beyond has developed workflows that help clients operationalise locally developed models and bring them into production faster. For example, one project reduced deployment times from eight weeks down to just 11 days, which highlights the importance of having the right tools and infrastructure in place to make AI adoption not only feasible but also efficient.
Another key element of successful AI deployment is having the right skills within the organisation. “It is not AI taking jobs but rather people skilled in AI,” Iliffe emphasises, addressing a common misconception. “Instead of viewing AI as a threat to jobs, companies should see it as a means to enhance human capabilities, allowing people to focus on high-impact tasks while AI handles the repetitive or mundane. This philosophy has led Beyond to embed teams within client organisations, transferring knowledge and building internal capabilities to ensure that AI adoption is sustainable in the long run.”
Emerging trends and the future of adaptive AI
Looking ahead, the future of AI in manufacturing is poised to be shaped by trends like multimodal AI, small language models, and adaptive AI systems that can act as “co-pilots” for workers. “We’re seeing a trend towards more adaptive AI, models that can tailor responses to individual needs,” says Iliffe. This new wave of AI applications is designed to be more responsive and context-aware, making it particularly valuable in industries like manufacturing, where quick access to precise information is often critical.
“Multimodal AI and small language models are likely to be transformative,” Iliffe predicts, pointing to their ability to integrate different types of data and provide a more holistic understanding of complex systems. These emerging technologies will enable AI to act as a true partner in the manufacturing process, not just automating tasks, but also providing the nuanced, real-time support that workers need to excel in their roles.
The trend towards building AI “companions” or “co-pilots” is also gaining traction. These systems can support workers by providing on-demand insights, guiding them through complex processes, and even predicting issues before they arise. “Building AI companions is particularly important in manufacturing, where quick access to information is critical,” Iliffe notes. Such tools promise to enhance productivity, improve safety, and provide a more engaging work experience for employees.
Navigating the path forward
For companies just beginning their AI journey, Iliffe has some clear advice: “Focus on getting the fundamentals in place. Cloud migration and data infrastructure modernisation are great starting points.” Without a solid foundation, the promise of AI can quickly become a burden rather than an asset. Beyond’s work with a client who migrated a large on-premise application to Google Cloud illustrates this point. By establishing a scalable, cloud-based environment, the company was able to build machine learning services like price prediction and recommendation engines, effectively laying the groundwork for a future powered by AI.
“Once you have modernised data infrastructure, you are in a strong position to leverage AI effectively,” Iliffe adds. “This approach, starting with a strong infrastructure, focusing on data quality, and building the right capabilities, is what sets successful AI adopters apart from those whose initiatives fall flat. It is a strategy that not only addresses the technical aspects of AI but also the cultural and organisational changes needed to make these technologies work.”
AI in manufacturing is no longer about the distant future; it is about what can be done today to solve pressing operational challenges, make informed decisions, and prepare for the next wave of industrial transformation. As the world navigates a more uncertain economic landscape, companies that effectively leverage AI will be better positioned not only to survive but to thrive, using these technologies to unlock new levels of efficiency, agility, and insight.
The potential of AI in manufacturing is vast, but its success hinges on focusing on what the technology truly enables. From streamlining operations to driving innovation, AI provides manufacturers with the tools they need to transform processes and enhance efficiency. As AI continues to evolve, its impact on manufacturing will only grow, offering new opportunities for those ready to embrace the change.
