Industrial AI: Accuracy, Domain Data, and Workflow Transformation

The quiet revolution of industrial AI is reshaping our physical world, not with flashy headlines, but with profound, behind-the-scenes optimizations. This conversation with Peter Koerte of Siemens reveals that the true power of AI lies not in consumer-facing novelty, but in its ability to enhance the fundamental infrastructure of everyday life--from the energy grids powering our homes to the trains that move us. The critical, non-obvious implication is that the highest value of AI is often found where the stakes are highest and the margin for error is smallest, demanding a rigor that consumer AI rarely requires. Leaders in manufacturing, infrastructure, and technology should read this to understand how to harness AI for tangible, large-scale impact, gaining a competitive edge by focusing on accuracy, domain-specific data, and the transformation of workflows, not just the technology itself.

The Unseen Engine: Precision and Proprietary Data in Industrial AI

The allure of consumer AI, with its generative capabilities and conversational fluency, often overshadows the more grounded, yet equally transformative, work happening in industrial settings. Peter Koerte, CTO of Siemens, highlights a fundamental divergence: while consumer AI can tolerate a degree of error, even hallucination, industrial AI demands near-perfect accuracy. This isn't about witty chatbots; it's about optimizing energy grids, predicting train door failures, and ensuring the safe operation of critical infrastructure. The consequences of AI failure in these domains are not mere anecdotes, but potentially catastrophic disruptions. This necessitates a different approach to model development, one that prioritizes reliability, safety, and trustworthiness above all else.

"The big difference is today of course consumer AI is making the headlines while we think industrial AI is quietly but profoundly changing the physical infrastructure the physical world that we know of."

This pursuit of near-perfect accuracy--often expressed as 99.999%--is just one facet of the industrial AI landscape. The data itself presents a unique challenge. Unlike the vast, publicly available datasets that fuel large language models, industrial AI relies on proprietary, domain-specific information. This includes time-series sensor data, complex CAD files, engineering simulations, and operational logs. Customers are hesitant to share this sensitive data unless a clear, incremental benefit can be demonstrated. This creates a symbiotic relationship: Siemens offers advanced AI models that promise tangible improvements, such as reduced energy consumption in buildings or faster time-to-market for engineered products, in exchange for access to the data needed to train and refine these models. This is not about scraping the internet; it's about carefully curated data partnerships built on trust and demonstrated value.

The Unsung Hero: Domain Expertise as the AI’s Compass

The notion that any data scientist can readily deploy AI in industrial settings is a misconception Koerte directly addresses. The true differentiator, he emphasizes, is domain knowledge. While a data scientist might understand algorithms and data structures, they may lack the nuanced understanding of what drives success or failure in a specific industry. Consider the humble train door. A superficial analysis might focus on the brakes as the most critical component. However, Koerte reveals that operational data shows train doors are often the most frequent point of failure, directly impacting the train's core function: moving people efficiently from A to B.

"The most critical part in that regard for operations is the door and this is the main knowledge you need to understand that part."

This insight, derived from deep operational understanding, allows for the development of highly effective AI applications. By analyzing voltage readings and operational profiles of door motors, Siemens can now predict door failures up to ten days in advance. This enables proactive maintenance, significantly increasing train uptime and reliability. This marriage of domain expertise with AI technical know-how is not just an incremental improvement; it’s the engine driving customer value in the industrial world. The implication is clear: AI solutions that ignore or underestimate the importance of deep, industry-specific knowledge will likely falter, failing to capture the true potential for optimization and efficiency.

The Data Paradox: Insufficient Data, Amplified Value Through Collaboration

A recurring theme in the conversation is the paradox of data in industrial AI: often, a single customer possesses insufficient data to train a robust AI model for their specific needs. Yet, when data from multiple customers, even competitors, can be aggregated and anonymized under strict agreements, the collective dataset becomes powerful enough to yield significant insights. This is particularly evident in sectors like manufacturing, where companies may specialize in different components or processes but share common operational challenges.

Koerte explains that the key to unlocking this collaborative potential lies in demonstrating clear, quantifiable value. Customers will only share their proprietary data if they receive a direct benefit in return, whether it's improved reliability, enhanced efficiency, or reduced operational costs. This principle underpins Siemens's approach, where data sharing is often integrated into service contracts or negotiated as part of a broader value proposition. The success of such partnerships, like the one with Nvidia to accelerate complex simulations, hinges on this mutual exchange of value. It highlights a critical shift: competitive advantage is increasingly derived not just from proprietary data, but from the ability to strategically collaborate and leverage aggregated insights. This requires a fundamental rethinking of data ownership and a willingness to engage in partnerships that might have seemed unconventional in the past.

Augmentation, Not Automation: The Future of the Industrial Workforce

The conversation touches upon a significant point of anxiety: the impact of AI on jobs. Koerte frames the future not as outright automation replacing humans, but as augmentation, particularly for the blue-collar workforce. He is particularly enthusiastic about the potential of smart glasses in manufacturing. These devices, equipped with cameras and AI, can act as a "co-pilot" for workers, providing real-time guidance, diagnostic information, and knowledge transfer.

"What if you had those glasses on now and they're saying those glasses are saying you know this is a cnc machine usually the failure code of e345 means actually it is a jam tool check that lid and open this 12345 and off you are how amazing is that?"

This technology can democratize specialized knowledge, enabling less experienced workers to perform complex maintenance tasks. It also serves as a powerful tool for knowledge capture, preserving the expertise of seasoned employees before they exit the workforce. This approach addresses the critical shortage of skilled labor in industries like manufacturing by enhancing the capabilities of existing workers and accelerating the onboarding of new ones. The implication is that the most successful AI implementations will be those that empower human workers, making them more productive and effective, rather than seeking to replace them entirely. This requires a cultural shift towards embracing technology as a tool for human augmentation, fostering an environment where AI and human expertise work in concert.

Key Action Items

• Prioritize Accuracy Over All Else: For any industrial AI deployment, establish near-perfect accuracy (99.999%+) as a non-negotiable requirement from the outset. This will shape your data strategy, model selection, and validation processes.
• Invest in Domain Expertise: Ensure your AI teams include deep subject matter experts from the specific industry you are serving. Their knowledge is crucial for identifying critical use cases and interpreting data correctly.
• Develop a Data Value Proposition: Clearly articulate the tangible benefits customers will receive in exchange for sharing their proprietary data. Focus on improvements in reliability, efficiency, cost reduction, or time-to-market.
• Foster Data Sharing Partnerships: Actively seek opportunities to collaborate with customers and even industry peers to aggregate sufficient data for robust model training. Structure these partnerships with clear data governance and value-sharing agreements.
• Embrace Human Augmentation: Focus AI initiatives on empowering your workforce, particularly in operational roles. Explore technologies like smart glasses for real-time guidance and knowledge transfer.
• Plan for Workflow Transformation (Immediate Action): Recognize that AI implementation is as much about changing processes and workflows as it is about the technology itself. Begin mapping existing workflows and identifying areas ripe for AI-driven redesign. This requires immediate engagement with end-users.
• Build for Scalability and Interoperability (12-18 Month Investment): As highlighted by the discussion on industrial operating systems, design AI solutions with composability and portability in mind. This ensures that as technologies evolve, your core agentic work can be adapted rather than rebuilt. This investment pays off in long-term agility and reduced technical debt.