Issue link: https://iconnect007.uberflip.com/i/1541985
12 PCB007 MAGAZINE I DECEMBER 2025 processes, detect early signs of drift, and capture expert knowledge in a stable, consistent form. Rather than replacing engineers, they amplify engi- neering judgment and help fabs preserve and repli- cate the expertise they already have. In an industry defined by complexity, AI is not optional innovation; it is structural support. Why Now Is the Right Time AI has been discussed in manufacturing for years, but meaningful adoption has always lagged behind expectations. Not because the algorithms were immature, but because fabs lacked the prerequi- sites. That has changed. Years of digitalization have generated the data ML requires. Equipment connectivity, MES deployment, and high-resolution inspection archives mean fabs finally "have something to learn from." Meanwhile, the cost of compute has fallen, industrial AI tools have become more practical, and suppliers across the ecosystem are building AI-ready equipment and software. Demand has shifted as well. Supply chain volatil- ity, new material systems, and high-mix/low-volume production are forcing fabs to respond more quickly, with more precision, and with fewer experi- enced hands on the floor. For the first time, need, capability, and econom- ics are aligned. This is the moment when AI and ML can move from theory to practice inside PCB shops. Perceived vs. Real Risks: What Holds Fabs Back? It is natural for engineers and managers to worry about AI: imperfect data, unpredictable models, or unintended consequences on production stability. These are reasonable concerns in a process-sensi- tive industry like PCBs. But the more significant barriers are organiza- tional rather than technical. Across regions, the most common misconception is that AI requires a sweeping overhaul of the shop floor, or that it will instantly automate what humans currently do. Such assumptions raise expectations to unrealistic levels and generate unnecessary resistance. The truth is that successful fabs take a very differ- ent path: They start small, focus on specific prob- lems, build confidence, and grow their capabilities step by step. The real risks lie in readiness—digital literacy, workflow coordination, structured knowledge capture—not in the algorithms themselves. AI fails more often because organizations are not prepared to adopt it sustainably, not because the models are inherently flawed. A Practical Roadmap: Evolution, Not Revolution What the industry needs is not a "grand transforma- tion plan," but a realistic, value-driven progression. The most promising roadmap emerging across leading fabs consists of four stages: 1. Data capture: Ensuring that production data is complete, reliable, and usable. 2. Knowledge structuring: Converting expert insights into explicit, shareable logic. 3. Local applications: Deploying AI in targeted areas (inspection, single-step parameter tuning, alerts) to generate quick, visible wins. 4. Global, cross-process intelligence: Integrat- ing ML across multiple processes to optimize yield, stability, and factory-wide decision- making. This progression is not a theoretical construct. It is how real fabs are succeeding. Each step builds the foundation for the next, and every phase must deliver tangible business value to maintain momentum. AI will not rewrite the fundamental logic of PCB manufacturing; process engineering and materi- als mastery will always define the craft. But AI will redefine what it means to be competitive. Stability, predictability, knowledge replication, and the ability to manage complexity systematically are becoming the new keystones of leadership. We may not see fully autonomous PCB facto- ries soon. But we will see that PCB fabricators who embrace AI early—and do so pragmatically—will rise faster along the value chain and strengthen their standing in an increasingly demanding global market. PCB007 Sydney Xiao is president of Global Electronics Association East Asia