To unlock the potential tomorrow of generative AI, manufacturers must lay the groundwork to scale it today.

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Manufacturers are constantly striving for efficiency. After all, any firm that can produce high volumes of products for low costs in a short period of time will always have an edge over its competitors.

Yet while the ultimate goals of higher productivity and better costs remain, the path to achieving them is changing. From supply chain to shop floor, risk management to process automation, technologies like Generative AI (GenAI) are poised to reshape the manufacturing industry as we know it.

The key for leaders is to understand what these technologies can do—and, importantly, where they can add real lasting value to their business.

The power of text

In the case of GenAI, it’s all about the creation of new content, be it text, images, audio or video. And for the manufacturing industry, the most relevant and interesting of these is text.

Using GenAI, firms can increase their ability to generate text-based code, thereby creating higher quality and consistency in their software. Better yet, workers can use natural language prompts to do this, making it easy to write and review code, regardless of their skill or knowledge level.

GenAI can also draft, summarize and review legal documents and contracts, completing tasks in minutes that would take a human worker hours. This allows staff to focus on more important aspects of their work, while also promoting accuracy and consistency of documentation.

GenAI can also lead to better interactions with customers – from answering simple questions and requests to providing written summaries and analyzes for call center staff to use to solve more complex cases. . It improves relationships, builds trust and increases loyalty. And just like with writing code or documentation, it also leads to greater productivity and profitability.

Success at scale

So far only manufacturers will get one-time pilots. More important is considering how to build GenAI use cases that include 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 goal 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 the latest technology to deliver massive financial and operational benefits.

Key to the success of Lighthouse Factories is their pilot strategy. Rather than focusing on individual use cases, the leaders of these facilities try to establish the foundation for widespread deployment from the very first step. This includes proactively addressing any fundamental barriers that may prevent a technology from being implemented at scale.

For GenAI, that means making sure it can access and analyze the data it needs to improve performance. This means looking at the technology infrastructure and operating models needed to support it. And that means identifying and addressing any skills gaps in the workforce.

In this way, Lighthouse Factories is not only constantly testing and proving the technology, but also paving the way for the next use case to happen faster and more efficiently than its predecessors. The more manufacturers can replicate this approach, the more successful they will be in realizing the potential value of GenAI at scale.

Transferring four wards

Specifically, there are four areas that manufacturing leaders should focus on. The first is to review and upgrade their data architecture, identify any challenges and develop strategies to address them. This includes integrating key data sources from multiple disparate systems and ensuring real-time access to that data to enable more proactive and intelligent decision-making.

They should also consider their technology stack. Of course, this depends on getting the right GenAI solution. But it also means making sure they have the necessary experience and expertise in their ecosystem partnerships to move beyond narrow applications and create an environment for long-term work.

The third step is to assess their workforce capabilities, highlighting reskilling needs for each use case. Embedding tools and strategies that fill any gaps, including using GenAI to train workers itself, will make it easier to scale and accelerate in the future.

And their final area of ​​focus should be building a “value realization team” responsible for measuring the results of their efforts. These insights can then be used to assess the direct value-add of the pilot and, importantly, to assess whether it will evolve over time into a robust platform for further use cases. How can you contribute?

Sharp effect

Whether it’s GenAI or something else, the manufacturing industry’s biggest challenge in scaling new technologies is variability. Each plant, workforce and product process mix is ​​different, meaning one size rarely fits all.

That’s why it’s so important to take inspiration from lighthouse factories and redefine the industry’s approach to piloting. According to the WEF, the first set of use cases for Lighthouse Factories took an average of 10 to 20 months to implement. The next set took less than six months and the one after that less than three.

Whether it’s across plants, product sets or processes, addressing the 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 in the future.

The views expressed in this article are those of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.

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