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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driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteGlobalFoundries says it scales manufacturing AI through a Singapore-based global AI Center of Excellence that works with teams across its manufacturing sites to pilot and scale solutions. The company reported deploying more than 60 smart manufacturing solutions since 2020, but it has not publicly detailed the pilot-selection rules, rollout sequence, or measured impact of each solution.
How GlobalFoundries organizes AI scale-up
GlobalFoundries (GF) describes a cross-site model rather than a process owned by one fab. Its digital manufacturing team accelerates deployment of digital and AI-powered solutions across its manufacturing sites. The global AI Center of Excellence is based in Singapore, where engineers, data analysts, and data scientists work with teams across GF sites to pilot and scale solutions. GF says the work aims to improve efficiency and quality while reducing cost and waste. GF’s digital manufacturing overview describes this operating model.
GF has manufacturing operations in the United States, Europe, and Asia. It describes Singapore as a high-mix, high-volume manufacturing hub, and says its virtual fab model provides 24/7 engineering and operations support across sites. These features offer context for coordinating work across locations, but GF has not said that the virtual fab model is the mechanism used to scale each AI solution. GF’s global manufacturing overview outlines its footprint and operating model.
What deployment evidence GF has published
In a September 16, 2025 announcement, GF reported that it had deployed over 60 smart manufacturing solutions since 2020, leveraging AI, machine learning, IoT, and advanced analytics. “Over 60” is the company’s reported cumulative figure; it is not an exact count, and the announcement does not say that every solution runs at every fab. GF also reported that its 300mm Singapore fab was designated to the World Economic Forum’s Global Lighthouse Network in 2025. GF’s announcement associates the deployed solutions with improvements in cost, quality, and productivity, but provides no solution-by-solution or site-by-site performance table.
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That distinction matters: the count and Lighthouse designation show reported deployment and external recognition, not the size of a particular operational gain. The public announcement does not provide percentages for yield, equipment uptime, cycle time, scrap, energy use, or financial savings, so those results cannot be inferred from the headline count.
What manufacturing problems AI might address
GF’s 2025 Form 20-F lists possible AI and machine-learning uses in manufacturing and adjacent operations. These are examples in a risk disclosure, not a confirmed inventory of deployed GF systems. The company’s 2025 Form 20-F identifies:
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- Automating repetitive tasks.
- Predictive maintenance.
- Process-design-kit development and optimization.
- Process-time optimization.
- Wafer inspection.
- Inventory and supply-chain management.
The examples span factory-floor operations, process development, and supply-chain work. They indicate the range of use cases GF considers relevant, but the filing does not establish that each has been implemented, or where it is in use.
What the Siemens collaboration adds—and what it does not prove
On December 11, 2025, GF and Siemens announced a strategic collaboration focused on semiconductor-fab automation, electrification, digital solutions, and software. The announcement identifies AI-enabled software, sensors, real-time control systems, centralized automation, and predictive maintenance as areas of work. The companies said they intend to develop and deploy solutions in their own operations, with aims that include higher equipment availability and operational efficiency. The collaboration announcement describes plans and objectives, not verified results already achieved.
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This partnership is a separate part of the scale-up picture: it adds an industrial-technology collaborator to GF’s internal cross-site model. It should not be read as evidence that the announced solutions have already been deployed throughout GF’s fabs or that the intended gains have been measured.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What remains undisclosed
GF’s public materials explain the team structure and report an aggregate deployment count, but they do not describe the implementation playbook in detail. The reviewed sources do not specify how GF selects pilots, what validation a model must pass before production use, the order in which sites receive a solution, or the measured impact of individual deployments. Those details remain unknown from the public evidence cited here.
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GF’s Form 20-F also sets out risks relevant to scaling AI/ML: adoption can require significant resources without delivering commensurate returns; the company needs skilled workers and reskilling; legal and regulatory developments are uncertain; and data privacy, cybersecurity, or inaccurate, biased, or otherwise faulty outputs can create risks. This frames scale-up as an operational and people challenge as well as a technology effort.
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