Breaking Barriers: Gen AI speeds semiconductor innovation

As someone who has seen the amazing progress of AI technology firsthand, I am excited about the chances generative AI offers for the semiconductor industry. In this blog post, I'll explore key insights from a recent report I co-authored, “Breaking barriers with AI in the semiconductor industry.” The report spotlights the substantial impact generative AI is set to have on the semiconductor value chain.

The semiconductor industry stands as the cornerstone of innovation. From our smartphones to self-driving cars, these tiny chips power the devices that shape our daily lives. But the never-ending search for new ideas has led to new challenges in designing, manufacturing, distributing and selling these products. Not enough skilled workers and the limits set by the laws of physics are some of the factors that could slow down progress. In this case, generative AI is like a bright light of hope. It could transform the semiconductor industry and open up new ways to grow.

Innovation urgency: A call to action

Our report surveyed 300 semiconductor leaders worldwide. They all agree that generative AI can greatly benefit the industry, One-third of these leaders believe generative AI will revolutionize design and manufacturing processes. This shows that generative AI has the power to bring big changes and opens new ways to create value.

But to capitalize on these opportunities, semiconductor companies must embrace generative AI across their entire business spectrum. Beyond design and manufacturing, this includes areas such as sales and marketing, and even customer service. By taking a holistic approach, companies can fully use the technology to drive innovation and gain a lasting competitive advantage.

Breaking down barriers: A path to unleashing potential

Despite the huge potential of generative AI, the semiconductor industry faces several barriers to its widespread adoption. Key challenges include data security concerns, technical debt and intellectual property (IP) considerations. Our research revealed that 73% of respondents identified IP concerns as the most significant barrier to deploying generative AI across the semiconductor value chain.

Overcoming these barriers requires a paradigm shift, one that emphasizes collaboration and the breaking down of silos. Our interviews with industry leaders revealed that 75% of executives believe collaboration with industry partners, through knowledge sharing and the development of common technology platforms and IP, will unlock greater value from generative AI. This teamwork approach encourages new ideas and helps people share their knowledge. This will help generative AI reach its full potential faster.

Shaping the workforce of the future

The talent shortage in the semiconductor industry, and across high tech industries, is well-documented. In the United States alone, it is estimated that it could take about 16 years to fill jobs stemming from the CHIPS Act at current graduation rates. Generative AI presents a unique opportunity to address this talent gap and reshape the future workforce.

By using AI to train and “augment” workers, semiconductor companies can help their current workforce meet the changing needs of the industry. Our research shows that 54% of companies plan to hire people from other industries and give them training. 63% of companies plan to change how they pay their employees to keep them. 42% of companies plan to improve their workforce, because they know it's important to invest in their most valuable asset, their people.

Generative AI itself holds immense potential to address challenges in obtaining, retaining and upskilling key talent.

No-regret and strategic bets: A balanced approach

We also found that many executives perceive technology as both the semiconductor industry's biggest opportunity and its most significant challenge. This duality underscores the transformative potential of generative AI, while also highlighting the need for a strategic approach to its implementation.

No-regret use cases offer a compelling starting point for companies embarking on their generative AI journey. These use cases deliver high value, have a short implementation timeline and pose minimal risk. Examples include AI-enabled field service assistants to improve maintenance efficiency and marketing content generation to accelerate campaign creation. By focusing on these low-risk, high-value initiatives, companies can gain momentum and build a foundation for more ambitious deployments.

Strategic bets, on the other hand, offer the potential for transformative competitive advantage. These use cases may have a longer timeline but promise higher value and results closer to the industry's core. Defect detection applications that use better synthetic data for inference models show this approach. This helps engineers find and fix root causes faster.

The key to success lies in choosing the right use cases at the right time, ensuring alignment with the company's strategic objectives. By striking a balance between no-regret and strategic bets, semiconductor companies can improve their generative AI investments and maximize their potential for growth.

As we navigate this exciting landscape, let us remember the words of Leonardo da Vinci: "Innovation is born from the marriage of creativity and technology." By fostering a culture of collaboration, embracing new ideas and using the transformative power of generative AI, the semiconductor industry can redefine its boundaries and shape a future of limitless possibilities.