Siemens is a technology company that develops industrial automation, software, and AI-powered digital solutions that help manufacturers optimize operations, improve productivity, and accelerate digital transformation.
Industrial AI has generated significant interest, but many pilot projects never reach production. In your view, what separates successful AI deployments from those that fail to scale?
The biggest mistake organizations make is starting with the AI model rather than the business problem they're trying to solve. Many companies approach us with a sophisticated model or an exciting use case, but we always take a step back and ask, "What problem are you trying to solve?" Once that is clear, you can work backward to determine the data, workflows, and user experience required to achieve a measurable outcome.
We’ve learned that success depends on much more than the model itself. Organizations need high-quality, contextualized data, workflows that deliver AI insights to the right people at the right time, and an intuitive user experience. Too often, engineers are expected to navigate multiple disconnected AI tools that don't communicate with one another. We believe AI should fit naturally into existing workflows rather than requiring users to become experts in prompting different models.
Where are you already seeing industrial AI deliver measurable return on investment, and could you share a few customer examples?
One example is our work with Procter & Gamble on a solution called Visual Inspection Cockpit. Manufacturers need to inspect every product moving through a production line in just a few milliseconds. Traditional cloud-based AI introduces too much latency, so our solution moves the AI model to the edge, placing it directly alongside the production equipment. That eliminated the delay associated with sending data to the cloud and enabled millisecond response inspections.
The results have been significant: 100% inspection coverage at full production speed, scrap reductions of 10% to 20% depending on the product, more consistent quality across product variants, less reliance on manual sampling, and faster commissioning than traditional vision systems. Another example is our work with BlueScope, a global steel manufacturer based in Australia, where AI-powered predictive maintenance analyzes machine data to detect early signs of equipment degradation. By identifying issues such as abnormal motor vibration before failures occur, as of 2025 the company has prevented more than 2,000 hours of unplanned downtime.
AI copilots are making complex industrial systems more intuitive. How quickly are customers embracing these tools, and what is driving adoption?
One of the biggest lessons we've learned is that successful adoption depends on embedding AI directly into workflows rather than expecting users to constantly interact with individual models. Internally, we've seen tremendous success by delivering AI-generated insights exactly when employees need them. Instead of asking users to prompt a model, AI automatically gathers information, summarizes it, and presents relevant recommendations within the workflow itself.
Siemens’ Eigen Engineering Agent follows the same philosophy. It helps engineers generate PLC logic, validate proposed changes, and update control systems with greater confidence. Because manufacturing companies are understandably cautious about modifying production systems, AI must not only generate recommendations but also validate them before deployment. That's where the Digital Twins becomes incredibly valuable, allowing organizations to simulate changes, verify outcomes, and reduce risk before implementing anything on the factory floor.
Digital twins appear to be central to your AI strategy. Why are they becoming so important in industrial environments?
The Digital Twin gives organization the confidence they need to make operational changes. If AI recommends adjusting a machine parameter, for example, it would be too risky to implement that change immediately on a live production line. Instead, they can validate the recommendation in a virtual environment, testing multiple scenarios, and confirming the expected outcome before making any physical changes.
That validation capability becomes increasingly important as AI moves from providing insights to recommending actions. By combining AI, workflows, and the Digital Twin, organizations can simulate production changes, optimize performance, and deploy improvements with much greater confidence. Rather than simply identifying problems, AI becomes a trusted decision-support system that helps manufacturers improve operations while minimizing operational risk.
From your global perspective, are you seeing differences in AI adoption across regions, and what role does AI play in the broader reshoring conversation?
From our perspective, regional differences are actually less important than industry differences. As a global company, we work with customers across the United States, Europe, Asia, and Latin America, and we see successful AI deployments everywhere. Rather than one geography leading another, adoption tends to depend on how aggressively individual industries pursue digital transformation.
Consumer packaged goods is one example of a sector moving particularly quickly, but we see similar success stories around the world. The conversation is increasingly driven by industry-specific business needs rather than geography. AI is becoming an important enabler of manufacturing competitiveness regardless of where production takes place.
What AI innovation are you personally most excited about, and where do you see the greatest opportunity for future ROI?
What excites me most is bringing AI directly to manufacturing operations. Whether it's predictive maintenance, visual inspection, or understanding why production output has changed from one day to the next, we're moving toward what I would describe as an operations copilot. Instead of simply reporting that production has declined, AI will identify the root cause, recommend corrective actions, and explain why those actions should work.
As data becomes better contextualized across an entire production line, AI will connect relationships that humans may not immediately recognize. A problem appearing at one workstation may actually originate much earlier in the manufacturing process. Helping organizations uncover those relationships and optimize entire production systems has the potential to deliver transformational productivity gains. We've already seen examples where combining AI with digital twins has increased manufacturing throughput by around 20%which is truly game changing.
Looking three years ahead, how do you expect the factory floor to evolve?
Autonomous manufacturing is increasingly within reach— not removing people from the process, but applying their experience and expertise to enable larger scale growth. Rather than overseeing individual machines on the factory floor, people will increasingly supervise operations from centralized control centers, where AI continuously monitors production, identifies issues, validates potential solutions through digital twins, and recommends actions before problems affect output.
From a technology standpoint, much of this already exists today. We can deploy these capabilities now.
The biggest challenge is no longer the technology itself but human readiness. Organizations need confidence, education, and trust before they fully embrace autonomous manufacturing. In many cases, people want to see successful implementations, understand the technology, and gain practical experience before committing to large-scale deployment. As familiarity grows, I believe adoption will accelerate rapidly.
What will ultimately determine how quickly organizations embrace this next generation of AI?
Technology companies certainly have an important role to play, but education must be a shared effort. Internally, we've had great success with AI hackathons that bring people together to demonstrate what's possible and encourage hands-on experimentation. We also have a wide range of training and self-paced learning opportunities that people from across the organization can take advantage of, regardless of their role or level of experience. Once people experience the technology firsthand, they begin thinking differently about how it can improve their own work.
The key is balancing education with execution. Organizations enjoy demonstrations, but eventually they need to implement real solutions that solve meaningful business problems. As more successful deployments emerge and employees become comfortable working alongside AI, confidence will replace uncertainty, and adoption will continue to accelerate.
