How Artificial Intelligence is Transforming Manufacturing
According to a study by the World Economic Forum, more than 70% of industrial AI projects are abandoned after the pilot phase. While some companies successfully integrate artificial intelligence (AI) into their operations and achieve significant economic benefits, others face major challenges. However, many examples show that AI can be effectively used in manufacturing and has become a vital element of flexible, efficient production (Figure 1). Today, AI solutions are available that not only integrate smoothly into industrial processes but can also handle complex tasks with high efficiency.
Figure 1: Industrial AI is poised to change the way we know manufacturing and the implications of this change are vast and far-reaching.
Visual quality control
Quality assurance is a key task in industrial manufacturing. AI-powered image processing systems now make reliable quality inspections possible. One example is Inspekto, a solution for visual quality inspection that enables companies to automate product checks without needing deep AI or image-processing knowledge. The intuitive system can be ready for use in less than an hour and needs only about twenty sample images classified as “good” to deliver accurate results. Basic production knowledge of quality testing is enough—no AI expertise is required (Figure 2).
For example, the mid-sized company MTConnectivity Power2pcb uses Inspekto to inspect connectors to identify minimal deviations and slightly bent contacts. By integrating this AI-based system into its production line, the company ensures continuous quality assurance, improves reliability and shortens delivery times.
Figure 2: Inspekto from Siemens enables out-of-the-box visual quality inspection and requires no expertise in vision solutions or AI.
Generative AI in manufacturing
The application and implementation of generative AI models are more complex. Siemens’ Industrial Copilots are designed to improve human-machine collaboration and accelerate innovation across the entire value chain—from design, planning and engineering to operations and service. The Industrial Copilot for Operations is currently being piloted at customer sites and Siemens factories to test its reliability. Meanwhile, the Industrial Copilot for Engineering is already available as a finished product (Figure 3).
Thyssenkrupp Automation Engineering, a specialized machinery and equipment manufacturer, has integrated the Siemens Industrial Copilot into its systems for handling round cells used in battery inspections for electric vehicles. The Copilot automates repetitive tasks like data management, sensor configuration and detailed reporting that helps meet strict battery inspection standards. By managing routine tasks, the Copilot allows engineering teams to focus on complex, high-value activities, while solving problems in real time, minimizing downtime and ensuring smooth production.
Figure 3: Siemens’ vision of Industrial Copilots along the entire value chain aims to unlock the potential for improving human-machine collaboration and accelerating development and innovation cycles.
Predictive maintenance with AI
AI is also revolutionizing predictive maintenance. Instead of relying on fixed maintenance intervals or manual analysis, AI uses continuous machine data monitoring to detect early signs of wear and suggest maintenance actions. Siemens’ Senseye Predictive Maintenance solution identifies deviations in temperature, vibration and torque data to offer early warnings and recommendations (Figure 4).
Mercer Celgar, a producer of pulp and wood products, uses this technology to monitor its machinery in real time. Data from multiple production lines is combined into a central platform that provides a full overview of the manufacturing process and significantly reduces downtime.
Figure 4: Senseye Predictive Maintenance uses AI to enable asset intelligence across plants without the need for manual analysis.
Seamless integration of AI models
Even companies that have already adopted AI face challenges when scaling their solutions. Issues like time-consuming updates, poor connectivity or complex maintenance often arise. To address these challenges, the Industrial AI Suite is available. Industrial AI Suite is a platform for the smooth implementation of AI solutions on the shop floor.
These solutions are customized in close collaboration with customers to combine their existing AI expertise with Siemens’ infrastructure for scalable deployment . Depending on the use case, these solutions use edge or cloud computing to integrate services like AWS or Microsoft Azure. AI models can be trained in the cloud and then easily deployed to production floors using the AI Inference Server. The Industrial Edge application enables customers to deploy and run trained AI models in production, directly on the Industrial Edge, even with GPU-accelerated inferencing.
The Industrial AI Suite also manages the full AI model lifecycle, which allows easy updates and automatic detection of performance issues. For example, Siemens helped a food and beverage company integrate AI-based soft sensors into its production. These sensors ensure consistent product quality and taste by analyzing process parameters in real time and dynamically adjusting target values to optimize production and reduce waste.
In electronics manufacturing, Siemens’ electronics factory in Erlangen, Germany, uses machine learning models to detect errors in circuit board assembly, which improves speed and cost-efficiency with the help of the Industrial AI Suite.
Making AI accessible and practical
These real-world examples show that AI plays a crucial role in modern industry. Embedding AI systems into products hides the complexity from users and makes AI accessible and usable for everyone. The key to success lies in flexible infrastructures that allow companies to tailor AI solutions to their specific needs.
Industrial AI is no longer a futuristic vision—it is already delivering real competitive advantages today.
Dr. Matthias Loskyll is the senior director of Software, Virtual Control & Industrial AI at Siemens. He is a leader with a passion for customer-centric innovation and management of interdisciplinary teams of experts. He has more than 16 years of experience and background in AI methods, software development, industrial production, automation systems, Industry 4.0, industrial operations and manufacturing execution systems.
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Holding a virtual monopoly in a product on which the artificial intelligence boom relies should be a golden ticket. For chipmaker Nvidia, it has been. But ASML, which makes extraordinarily complex machines that etch silicon and is no less integral to the rise of AI, has found that ruling the roost can still be an up-and-down affair.
The €270bn Dutch manufacturer, which reports its earnings next week, is a sine qua non of technology; chips powering AI and even fridges are invariably etched by ASML’s kit. The flipside is its exposure to customers’ fortunes and politics.
Revenue is inherently lumpy, and a single paused purchase makes a big dent — a key difference from fellow AI monopolist Nvidia, which is at present struggling to meet demand for its top-end chips. ASML’s newest high numerical aperture (NA) systems go for €380mn; as an example of how volatile revenue can be for such big-ticket items, one delayed order would be akin to drivers holding off on buying 8,000-odd Teslas.
Initial hopes were high for robust spending on wafer fab equipment this year and next. Semi, an industry body, in December reckoned on an increase of 7 per cent this year and twice that in 2026. Jefferies, for example, now expects sales to flatline next year.
Mood music bears that out. Top chipmaker TSMC has sounded more cautious over the timing of the adoption of new high NA machines. Other big customers are reining in spending. Intel in April shaved its capital expenditure plans by $2bn to $18bn, while consensus numbers for Samsung Electronics suggest the South Korean chipmaker will underspend last year’s $39bn capex budget.
Politics is also getting thornier. Washington, seeking to hobble China’s tech prowess, has banned sales of ASML’s more advanced machines. Going further would hurt. China, which buys the less advanced but more profitable deep ultraviolet machines, typically accounts for about a quarter of sales. Last year, catch-up on orders lifted that to half.
Meanwhile, Chinese homegrown competition, given an extra nudge by US trade barriers, is evolving. Shenzhen government-backed SiCarrier, for example, claims to have encroached on ASML territory with lithography capable of producing less advanced chips.
The good news is that catch-up in this industry, with a 5,000-strong supplier base and armies of engineers, requires years if not decades. Customers, too, will probably be deferring rather than nixing purchases. The zippier machines help customers juice yields; Intel reckons it cuts processes on a given layer from 40 steps to just 10.
Over time, ASML’s enviable market position looks solid — and perhaps more so than that of Nvidia, whose customers are increasingly trying to create their own chips. Yet the kit-maker’s shares have been the rockier investment. In the past year, ASML has shrunk by a third while Nvidia has risen by a quarter; its market capitalisation is within a whisker of $4tn. That makes ASML the braver bet, but by no means a worse one.
Peter Thiel is unlike any other Trump tech bro. As well as a wildly successful investor, he’s seen as a thinker – the philosopher king of Silicon Valley. Thiel’s acolytes in the tech world and Washington include vice-president JD Vance but his relationship with the Trump camp is complicated. And there are still questions about what, if anything, he wants with the president.
In the final episode of this season of Tech Tonic, Murad Ahmed speaks to FT columnist Gillian Tett about Thiel’s political philosophy, and to Tabby Kinder, the FT’s West Coast financial editor, about his influence in Silicon Valley.
This season of Tech Tonic is presented by Murad Ahmed and produced by Josh Gabert-Doyon. The senior producer is Edwin Lane and the executive producer is Flo Phillips. Sound design by Sam Giovinco. Breen Turner and Samantha Giovinco. Original music by Metaphor Music, Manuela Saragosa and Topher Forhecz are the FT’s acting co-heads of audio.
