The Future-Ready Podcast: Industry & Beyond

AI is moving from theory to practice on the shop floor. And it is only possible because of an accelerated workflow built on software. Whether it is generative AI powered assistants, smarter data integration or efficient deployment across both greenfield and brownfield deployments, the future of industry starts with digitalization and software. And we have Rainer Brehm to explore the topic.The flexibility of this transition, highlighted in parts one and two, extend to the how as well. Your facility can be large and stationary or remote microfactories, deployed at a moment’s notice, flexibility in a software-defined system is about understanding what works and applying it to every relevant use case as a business grows. Automation is transitioning from rule-based to goal-based, enabling systems that can adapt, optimize and execute in dynamic conditions.In this episode you’ll learn about:(00:00) When is this transformation happening?(04:30) Scaling across industries(08:05) Rule-based to goal-basedAbout the voices:Rainer Brehm is the Chief Operations Officer (COO) for the automation business and Chief Technology Officer (CTO) at Siemens Digital Industries. In these roles, he drives the strategic advancement of Siemens’ automation portfolio and leads the development of technologies such as Industrial AI and Software-Defined Automation to unlock the full potential of automation. His focus is on making production more adaptive, resilient, and sustainable. His vision is an automated automation that solves unknown tasks independently.Nick Finberg is a technical marketing writer and coordinator for Software-Defined Everything, with a background in Nuclear Engineering. He has worked with experts to cover many industries and topics including Automotive, Battery, sustainability, and systems engineering.

Connecting enterprise data is a necessary, yet challenging, step in the process of developing Industrial AI solutions to their fullest potential. Data fabrics are a key way to address this challenge, offering connection and context to enterprise data and credibility to AI-generated results. These three elements will, together, be necessary building blocks for developing highly autonomous AI agents in the future, paving the way for users to orchestrate complex workflows across multiple domains.In this episode, host Spencer Acain is joined by Tobias Malbretcht, Head of Product Management, Data & AI at Siemens Digital Industries to explore the future role of tools like RapidMiner in supporting complex agentic AI solutions for industry and what that will mean for product design and production going forward.In the episode you’ll learn about:(00:00) How data fabrics are applied to agentic AI(03:03) Building secure and trusted AI systems with data fabrics(07:00) What the future holds for RapidMinerAbout the voices:Tobias Malbretcht is the Head of Product Management for Data and AI at Siemens Digital Industries leading development for the RapidMiner AI Platform.Spencer Acain Is a technical marketing writer for AI thought leadership at Siemens Digital Industries with a background in Applied Mathematics and Mechanical Engineering.

The ChatGPT moment for robotics is here,” Nvidia boss Jensen Huang said. And nowhere is that more obvious than on the factory floor, where machines aren’t just becoming more agile, but smarter, too. Last November, Money Talks took a trip to Siemens in Germany, to tour the factories of the future.© The Economist Newspaper Limited, London 2026This is a special episode, first recorded by The Economist, to uncover the future of factories. Their crew interviewed a handful of guests including Cedrik Neike, Rainer Brehm and Stephan Schlauss.

Computers and software are becoming paramount to the future of industry, whether to automate automation engineering or expand the available design space for operational needs. Co-host Mark Hindsbo sat down to talk to the later and explain the transition happening in the world of production and manufacturing.There are so many interesting topics in the realm of operations software, but to start off it’d be best to understand exactly what that encompasses as well as some of the advantages to deploying a software-first plan. Going virtual first, means more trials with fewer costs and less capital investment – just like the gains automotive companies have made with virtual crash-testing.In this episode you’ll learn about:(00:00) Who is Mark Hindsbo(04:41) Integrating automation and operations(08:58) Defining operations softwareAbout the voices:Mark Hindsbo joined Siemens in 2025 as Head of Operations Software. He is leading a team to build an integrated and modular industrial operations software suite that allows customers to design, engineer, and operate their factories, data centers, or plants – powered by agentic AI and digital twins. His extensive career includes leadership roles at Ansys, Parallels, and Microsoft, alongside experiences at The Boston Consulting Group, Novo Nordisk, and CERN. Mark is also an Adjunct Professor at Carnegie Mellon University. Born and educated in Denmark, he holds an M.S. in Applied Physics & Mathematics from the Technical University of Denmark.Nick Finberg is a technical marketing writer and coordinator for Software-Defined Everything, with a background in Nuclear Engineering. He has worked with experts to cover many industries and topics including Automotive, Battery, sustainability, and systems engineering.

Data is the core of a good AI solution and, while data is plentiful in enterprise and industrial settings, accessing and utilizing that data for AI training isn’t so easy. In industry, data sources are often fragmented and, even if they can be easily accessed, lack the critical context needed to support robust AI usage. This is where data fabrics shine, offering a single, unified way of accessing and contextualizing all types of enterprise data.In this episode, host Spencer Acain is joined by Tobias Malbretcht, Head of Product Management, Data & AI at Siemens Digital Industries to examine the importance of data fabrics in the expanding role of AI in industry.In the episode you’ll learn about:(00:00) Introduction(02:47) What is RapidMiner?(05:14) The benefits of a data fabric for AI(08:06) Bringing context to AI training data(11:36) How data is connected to RapidMinerAbout the voices:Tobias Malbretcht is the Head of Product Management for Data and AI at Siemens Digital Industries leading development for the RapidMiner AI Platform.Spencer Acain Is a technical marketing writer for AI thought leadership at Siemens Digital Industries with a background in Applied Mathematics and Mechanical Engineering.