Without doubt, today’s society relies on the semiconductor industry. After all, can you imagine a world without smartphones, cars, power stations, and televisions?
We, as people, and the global economy more broadly, rely on continued innovation from the chips the industry produces. But there are challenges facing these companies across the board — design, manufacturing and demand. Talent is in increasingly short supply, and on top of that geopolitical tensions and onshore manufacturing add another layer of complexity. The industry keeps having hurdles to cross, one after another it seems. Only recently, another problem for the industry made the headlines when Hurricane Helene hit Spruce Pine, one of the world’s most important locations for semiconductor, raising questions about the impact it would have.
It’s already tough enough for semiconductor companies to deal with and resolve these issues, but they are appearing while generative AI has made the need for innovation a must do now, not a must do at some point. The question is whether the semiconductor industry can reinvent itself quickly enough for this new generative AI moment. Accenture analysis found “reinventors” (those companies that have already built the capability for continuous reinvention) increased revenues by 15 percentage points over other companies between 2019 and 2022. We expect that gap in revenue growth between reinventors and the rest to increase by 2.4 times to 37 percentage points by 2026, so there’s a clear opportunity for them. Yet our survey of global semiconductor executives found that 71% believe it will take at least three years for the semiconductor industry to deploy generative AI at scale. The industry could do with that timeframe accelerating somewhat.
The Challenges Ahead
It’s not going to be easy, however, of course it won’t. But semiconductor companies need to use generative AI across the entire spectrum — spanning design and manufacturing, through sales and marketing, to customer service –to seize opportunities for innovation in both the short- and long-term. Adapting that broad view across the value chain is a must to reinvent the value chain, however daunting that may initially seem.
There are other concerns too, such as IP. In fact, 73% of executives cite IP concerns as the biggest barrier to generative AI deployments. Then there’s of course the cost issue and the need to balance technical debt with investments for the future, both of which, are necessary.
Once leaders grapple with how those challenges can be overcome, there’s another pressing challenge and that’s having the right talent in place to deploy these applications successfully.
Most semiconductor companies are already fully aware of that and are doing everything they can to accelerate gaining new talent and reskilling their existing workforce. However, the speed with which generative AI is changing the way businesses work means they must also get support from across their ecosystem to ensure they have all critical skills in place.
It’s Time for Leaders to Place Their Bets
The industry needs to move forward with two workstreams running in parallel. First, CEOs and other business leaders must make no regrets moves; those use cases with the lowest risk, shortest time to show results and therefore, value. For example, Generative AI-enabled field service assistants would allow field service engineers to perform root cause analyses faster and recommend repair methods based on machine data, therefore reducing downtime and accelerating production. It also provides immediate access to information that helps technicians increase their knowledge, therefore helping with the skills gap. Generative AI can also be used in other areas, such as sales and marketing where it can improve the quality and level of personalization of the content to drive more personalized campaigns.
At the same time, strategic bets need to be decided upon to support the long-term goals of the business. An example of this is in process engineering. Generative AI-enabled applications that incorporate historical process parameter data to create more efficient designs for semiconductor equipment and wafer development. These tools can use drawings, text, images and more to create customized outputs that engineers can use to augment experiments, allowing for a more objective approach to experimental design. These strategic bets will be the things that will offer the highest value. They may well take some time to roll out, but they could pave the way for total reinvention and therefore, competitive advantage.
Whether the no regret moves or strategic bets, the guiding principle is choosing the right use cases, at the right point and at the right time. Every semiconductor company’s generative AI journey is different, but the approaches will be similar. All companies must establish a solid data foundation, have the necessary skills in place, and importantly, have the right ecosystem in position. Those that come out on top won’t just be the best player, but the businesses that put the right connections in place.
