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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteGlobalFoundries (GF) says its Singapore-based global AI Center of Excellence works with manufacturing teams across its sites to pilot and scale AI-powered solutions. GF reported deploying more than 60 smart manufacturing solutions since 2020, but it has not published a site-by-site rollout map or solution-level results. The public record therefore shows the organizational model and reported scale—not a detailed account of each pilot’s path to production.
How GF organizes AI work across its fabs
GF describes a digital manufacturing team that accelerates the deployment of digital and AI-powered solutions across all manufacturing sites. Its global AI Center of Excellence is located in Singapore, where engineers, data analysts, and data scientists work with teams across GF locations to pilot and scale solutions. The company says the work is intended to improve efficiency and quality while reducing cost and waste. GF’s digital manufacturing overview sets out this operating model.
The structure combines a central group with work involving local site teams. That gives GF a stated mechanism for coordinating pilots and scaling solutions across locations, but the public description does not specify a formal pilot funnel, approval board, technical architecture, or stage-gate criteria.
What GF has reported deploying
In an announcement dated September 16, 2025, GF said it had deployed over 60 smart manufacturing solutions since 2020, leveraging AI, machine learning, the Internet of Things (IoT), and advanced analytics. This is a company-reported total; GF did not say that every solution was deployed at every fab. Its announcement associates the solutions with improvements in cost, quality, and productivity, but provides no solution-by-solution or site-by-site performance table. GF’s 2025 announcement also said its 300mm Singapore fab was designated part of the World Economic Forum’s Global Lighthouse Network that year.
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GF describes manufacturing operations in the United States, Europe, and Asia, with Singapore serving as a high-mix, high-volume manufacturing hub. It also describes a virtual fab model that provides round-the-clock engineering and operations support across sites. These facts offer context for cross-location coordination; GF has not said that the virtual fab model is the specific mechanism by which each AI solution is replicated. GF’s global manufacturing overview describes the footprint, while its Singapore overview describes the hub.
Manufacturing tasks GF identifies as AI/ML use cases
GF’s 2025 Form 20-F lists examples of possible AI and machine-learning uses in semiconductor manufacturing. These examples indicate where the company sees potential; they do not confirm that GF has deployed a system for each task.
- Automating repetitive tasks.
- Predictive maintenance.
- Developing and optimizing process design kits.
- Optimizing process time.
- Inspecting wafers.
- Managing inventory and supply chains.
The filing is a risk disclosure, not an inventory of production systems. It also does not publish quantified yield, uptime, cycle-time, scrap, energy, or financial improvements for the solutions GF says it has deployed. GF’s 2025 Form 20-F, filed March 20, 2025, provides the use-case examples and discusses adoption risks.
What the Siemens collaboration adds—and what it does not establish
On December 11, 2025, GF and Siemens announced a strategic collaboration spanning semiconductor fab automation, electrification, digital solutions, and software. The companies identified AI-enabled software, sensors, real-time control systems, centralized automation, and predictive maintenance as areas of work. They said they intended to develop and deploy solutions in their own operations, with goals such as improving equipment availability and operational efficiency. Those are announced areas and aims, not evidence that the intended results have already been achieved. The Siemens announcement describes the collaboration.
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GF’s public descriptions explain who works across sites and give a company-reported deployment total, but leave important implementation details undisclosed. The sources do not state how GF selects pilots, what validation is required before a model enters production, the sequence of site rollouts, or the measured impact of individual solutions. Without those details, the deployment count and Lighthouse designation should not be treated as proof of a particular improvement rate or of uniform adoption across fabs.
GF’s Form 20-F also cautions that AI and machine-learning adoption can require significant resources without delivering commensurate returns. It identifies workforce and reskilling needs, evolving legal and regulatory issues, data privacy and cybersecurity, and the possibility of inaccurate, biased, or otherwise faulty outputs. Scaling factory AI therefore entails not only deploying technology but also investing in skilled people and managing operational and information risks.
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