The key for leaders is to understand exactly what these technologies can do — and, crucially, where ... [+]
Manufacturers are constantly striving for efficiency. After all, any firm able to make a higher volume of products in less time for a reduced cost invariably has an edge over its rivals.
Yet while the ultimate goals of heightened productivity and optimized costs remain, the path to achieving them is transforming. From the supply chain to the shop floor, risk management to process automation, technologies like generative AI (GenAI) are set to reshape the manufacturing industry as we know it.
The key for leaders is to understand exactly what these technologies can do — and, crucially, where they can add real lasting value for their business.
The power of text
In the case of GenAI, it’s all about the creation of new content, be that text, images, audio or video. And for the manufacturing industry, the most relevant and exciting of these is text.
Using GenAI, firms can enhance their ability to generate text-based code, thereby driving higher standards and consistency in their software. Better still, workers can use natural language prompts to do this, making it easier to write and review code regardless of their level of skill or knowledge.
GenAI can also draft, summarize and review legal documents and contracts, completing tasks in minutes that would otherwise have taken a human worker several hours. This frees up staff to focus on more high-value aspects of their job while also boosting the accuracy and consistency of the documents themselves.
GenAI could even lead to better interactions with customers — from answering straightforward queries and requests to providing written summaries and analyses for call center staff to use when resolving more complex cases. This improves relationships, builds trust and drives loyalty. And just as with writing code or documents, it adds up to greater productivity and profitability, too.
Success at scale
Yet one-off pilots will only get manufacturers so far. More important is to consider how to create GenAI use cases that scale, including tailoring the technology to the individual capabilities of certain production facilities or workforces.
Here, a good place to start is by looking at the World Economic Forum (WEF) Global Lighthouse Network. Created five years ago, its stated aim is to “help manufacturers around the world adopt the latest technologies through a shared learning journey.” And to do so, it brings together a group of facilities that are all using advanced technology to deliver financial and operational benefits at scale.
Key to the Lighthouse Factories’ success is their pilot strategy. Rather than focus on individual use cases, leaders of these facilities seek to set the foundation for widespread deployment from the very first step. This includes proactively tackling any underlying barriers that could prevent a technology from being implemented at scale.
For GenAI, this means ensuring it can access and analyze the data required to optimize performance. It means looking at the technology infrastructure and operating models required to support it. And it means identifying and addressing any skills gaps in the workforce.
In this way, Lighthouse Factories are not just constantly testing and proving the technology, they are paving the way for every next use case to happen more quickly and effectively than its predecessor. The more manufacturers can replicate this approach, the more successful they will be in realizing the potential value of GenAI at scale.
Moving four-wards
In particular, there are four areas on which manufacturing leaders should focus. The first is to assess and upgrade their data architecture, identifying any challenges and developing a strategy to address them. This includes integrating key sources of data from multiple separate systems and ensuring real-time access to that data to enable more dynamic and intelligent decision-making.
They should also consider their technology stack. Of course, this relies on getting the right GenAI solutions in place. But it also means ensuring they have the necessary experience and expertise in their ecosystem partnerships to go beyond narrow applications and create an environment to build off long-term.
The third step is to evaluate the capabilities of their workforce, highlighting re-skilling needs within each use case. Embedding tools and strategies that tackle any gaps, including using GenAI itself to train workers, will make it easier to scale and accelerate in future.
And their final area of focus should be on creating a “value realization team” responsible for measuring the results of their efforts. These insights can then be used to assess a pilot’s direct value-add and, crucially, to evaluate how it can contribute to a stronger platform for further use cases over time.
Speedy impact
Whether it’s GenAI or anything else, one of the manufacturing industry’s greatest challenges in scaling new technologies is variation. Every plant, workforce and product process combination is different, meaning one size rarely fits all.
That’s why taking inspiration from the Lighthouse Factories and redefining the industry’s approach to piloting is so important. According to the WEF, the first set of Lighthouse Factories use cases took an average of 10 to 20 months to implement. The next set took fewer than six months and the set after that under three.
Whether it’s across plants, product sets or processes, addressing real barriers to adoption as part of their pilot strategy will enable manufacturers to lay the best possible foundation for deployment at scale. This, in turn, will increase the speed of impact—not just for GenAI but for every transformative technology that comes along in the future.
The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.
