A Digital Enterprise Deep Dive with Jim Brown – Part 2 – Summary
In a recent episode of the Industry Forward Podcast, Dale and I once again welcomed Jim Brown to explore how companies can better leverage their industrial data through artificial intelligence (AI), the Digital Twin, and the broader vision of the Industrial Metaverse. The conversation, the second part of an ongoing series, offered a look at the challenges and opportunities facing manufacturers as they navigate digital transformation.
Digitalization and AI Unlock the Value of Industrial Data
Most companies today are not suffering from a lack of data. As Jim noted, “It’s not a lack of data, it’s a lack of value and insights from the data and that’s really what’s missing right now.” Thanks to modern sensors and enterprise systems, manufacturers are collecting vast amounts of time-series and operational data. However, much of this data remains siloed, unstructured, or disconnected from the broader context needed to make it actionable.
Dale echoed this sentiment, emphasizing the importance of contextualizing data. He shared an example from the aerospace industry, where flight test data was only useful when paired with information about the aircraft’s configuration during testing. Without that context, even the most detailed datasets can fall short of delivering real insights.
This is where the Digital Twin becomes a critical enabler. By creating a virtual representation of a product, process, or system, companies can better understand which data points matter most. “If you have the Digital Twin and you understand what is really important to the performance of your product or with the performance of your manufacturing line or a piece of equipment on the manufacturing line,” Dale explained, “it will actually help you understand, well, I only need to collect these 7 parameters instead of these 47 parameters.” This focused approach not only reduces data overload but also makes it easier to derive meaningful insights.
Jim added that while pure data science approaches—like searching for unexpected correlations—can be useful, manufacturing often benefits more from domain knowledge. “You’ve got equipment that we, we kind of understand right,” he said. “And so, you know, we know that when there’s excess vibration, something is not going well.” By combining this knowledge with the Digital Twin and AI, companies can move from reactive to predictive operations.
The conversation also touched on the chicken-and-egg dilemma many organizations face: they believe they need perfect data to start using AI, but AI can actually help improve and structure that data. Jim cited research showing that two-thirds of manufacturers lack the data needed to train predictive models, and about half are unprepared for generative AI. Yet, those who begin using AI—even with imperfect data—are already seeing benefits.
AI’s role isn’t limited to consuming data; it can also help condition and extract value from legacy systems. For example, AI can digitize and interpret handwritten quality reports or paper-based inspection records, making them usable for training models. Dale pointed out that in many aerospace programs, critical manufacturing knowledge was never formally documented—technicians would simply “fix things” on the fly. That kind of tribal knowledge is difficult to capture but essential for accurate modeling and prediction.
How AI and the Digital Twin Combine to Deliver More Value
As the discussion progressed, Dale and Jim explored how AI and the Digital Twin work together to accelerate innovation. In product development, AI can perform design space exploration, generating thousands of design scenarios based on requirements. The Digital Twin and simulation tools can then validate these designs, narrowing down the most promising options. “You can use Digital Twins and AI to help drive speed,” Jim said. He continued, “it’s that combination now of maybe using AI for design space exploration and really trying to use generative technologies to come up with novel approaches, far more than any human would have the time to do based on the requirements.”
This same approach applies to supply chain optimization. A Digital Twin of the supply chain, paired with AI, allows companies to evaluate countless scenarios quickly—balancing cost, sustainability, yield, and carbon impact. “You’re now able to optimize differently than you could have in the past,” Dale said, “because the AI solutions help automate so many of those processes.”
The conversation concluded with a look toward the future: the Industrial Metaverse. This emerging concept represents the convergence of the Digital Twin, AI, simulation, and immersive platforms like the omniverse. Dale described it as the culmination of digital transformation efforts, where companies can simulate, test, and optimize entire systems in a virtual environment before making real-world changes.
But what’s holding companies back from embracing the Industrial Metaverse? According to Jim, the biggest hurdles are not technological—they’re organizational. Companies need to invest in data governance, break down silos, and foster a culture that embraces digital innovation. Those that do will be best positioned to lead in the next era of industrial transformation.
Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens’ software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today’s ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation.
