Podcasts about industrial ai

Nothing in industrial technology news annoys me more than the hype around artificial intelligence—AI. I recorded this podcast on the eve of the Automate trade show and conference in June 2026.   Looking to for realistic use of Industrial AI, I'm bringing in an interview with a practitioner. Bryan DeBois is Director of Industrial AI at RoviSys, one of the largest independent system integrators. He has 20 years in MES, historians, and plant floor software. He leads teams that operationalize AI and data infrastructure in live plants, working with the C suite and ops to turn goals into running systems.   We look at definitions of AI. Then turn to the technology development from when RoviSys developed its AI practice in 2019 pre-LLMs. RoviSys took autonomous AI beyond predictive applications. Hiring deep manufacturing expertise, they can use AI to assist the human in the loop to make constrained decisions. DeBois discusses real-world applications. He then leads us through the beginning of a project.

Our guest is Boris Scharinger from Siemens. He wrote an interesting book "Industrial AI: From Pilot to Profit" and in this episode, we sit down with him. He is a leading voice in industrial AI and we uncover what it really takes to move from flashy AI pilots to solutions that deliver real value on the shop floor and in product engineering. We dig into the challenges of bridging proof-of-concept and production, the unique demands of industrial environments, and why so many initiatives stall before reaching profitability. Our conversation cuts through the hype, sharing candid examples from Siemens Energy and Tesla to illustrate where AI is truly changing the game and where it's still hitting walls. We also tackle pressing topics like the impact of generative AI on management expectations, the evolving role of process mining, and the tough realities of deploying AI in brownfield versus greenfield environments. If you're a startup, engineer, or decision-maker looking to understand the real mechanics—and pitfalls—of industrial AI, this episode is your inside track. Join us as we set the record straight on what it takes to turn AI ambition into sustainable success.

In this episode, Colin Masson hosts a discussion with experts from Edgescale AI and Red Hat about the challenges and solutions in deploying industrial AI at the edge. They explore how to overcome data integration hurdles, the role of cloud-native platforms, and real-world use cases that demonstrate rapid deployment and operational impact.Guest Names:Brian Mengwasser (Edgescale AI's CEO) and Cole Wangsness (Red Hat's Edge Program Lead)Keywords:#Industrial AI #IndustrialEdge #industrialautomation  #AI #edgecomputing  #dataintegration  #redhat #edgescaleaiWould you like to be a guest on our growing podcast?Do you have an intriguing or thought provoking topic you'd like to discuss on our podcast? Please contact the Host, Colin Masson: cmasson@Arcweb.com (or the Producer Tom Cabot) TCabot@Arcweb.comView all the episodes here: https://thedigitaltransformationpodcast.buzzsprout.com

AI can finally write back to the plant floor, but only if you can trust it. Chris Stevens and Annemarie Breu of Siemens explain how orchestration makes that safe.Industrial AI has reached a turning point. Manufacturers can already collect data, contextualize it, and surface insights, but the hardest step has always been turning insight into action on real control equipment. Chris Stevens and Annemarie Breu of Siemens explain how an orchestration layer finally closes that loop. Annemarie frames the tension clearly. Automation depends on determinism, while large language models are probabilistic by design, so the goal is to bring that discipline into AI and validate any suggestion before it changes a set point.Most executive conversations start with return on investment, and two forces are making the case easier to prove. The workforce shortage has stretched the expected payback window from 18 months toward 36 months, and when a line cannot run for lack of people every idle minute costs thousands of dollars. The other driver is overall equipment effectiveness, since most plants run near 70 percent OEE and even a fraction of a percent of gain can justify a project. Energy is a standout case too. A BorgWarner sustainability effort used a digital twin to flatten demand peaks and reportedly paid for itself in under six months, even as data center growth pushes electricity demand higher through 2040.On trust and safety, Annemarie borrows a principle from industrial safety. Just as fail safe IO modules rely on two channel evaluation, every AI suggestion is validated against a state machine, a workflow, or a physics based digital twin before the orchestration layer passes it to a controller. With virtual commissioning and soft PLCs a change can be tested virtually, approved by a human in the loop, and only then written to control, an approach PepsiCo and NVIDIA echoed at CES when they called the digital twin a must have. Making AI real, the pair argue, comes down to discipline, clear scope, acceptance criteria, and focused 90 day challenges, plus the change management and user experience that drive adoption. Their favorite quick win is preventive maintenance driven by machine data, which both BorgWarner and Maersk tied to millions in savings.About Chris StevensChris Stevens is President of US Automation at Siemens, where he leads a roughly one billion dollar business spanning software, services, and hardware. He brings more than 25 years across Siemens Digital Industries, starting in the field selling assembly and test equipment, moving into the software and digital twin world, and returning to automation to bring the hardware and software sides of the business together.About Annemarie BreuAnnemarie Breu is a senior technology leader at Siemens Digital Industries focused on automation software deployment and customer technology partnerships in the US. She began at Siemens about a decade ago as a systems engineer in the San Francisco Bay Area, working with consumer electronics manufacturers on virtual commissioning and digital twins. Her work today centers on bringing the determinism and reliability of automation into industrial AI.Timestamps0:00 Introduction and Automate 2026 preview2:50 Meet Chris Stevens and Annemarie Breu9:30 The first AI question is always ROI14:00 Workforce gaps and OEE drive the business case19:30 Energy management and the data center demand surge23:20 Data, sensors, and contextualization requirements28:00 Guardrails, hallucinations, and two channel validation32:40 The digital twin and the human in the loop37:40 How partners and integrators move up the stack45:30 What it takes to make AI real on the floor55:50 Preventive maintenance as a quick win59:40 Predictions, career advice, and book picksAbout Your HostsVladimir Romanov is a co-host of The Manufacturing Hub Podcast and the founder of Joltek, an independent manufacturing and industrial automation consulting firm specializing in modernization strategy, digital transformation, and workforce development. Joltek works with manufacturers and investors to de-risk modernization and build the internal capability to sustain results.Connect with Vlad: https://www.linkedin.com/in/vladromanov/Want to go deeper? Vlad and the team at Joltek have covered related topics here:Edge Computing and the Value of AI in Manufacturing Data: https://www.joltek.com/blog/edge-computing-ai-value-manufacturing-dataIT and OT Architecture Integration: https://www.joltek.com/services/service-details-it-ot-architecture-integrationDave Griffith is a co-host of The Manufacturing Hub Podcast and founder of Capelin Solutions, an industrial automation firm helping manufacturers adopt smart manufacturing technology. He brings 15 years of experience in industrial automation and digital transformation.Connect with Dave: https://www.linkedin.com/in/davegriffith23/Subscribe to Manufacturing Hub: https://www.manufacturinghub.liveLinkedIn: https://www.linkedin.com/company/manufacturing-hub-networkYouTube: https://www.youtube.com/@ManufacturingHub

In this episode, Nithiya Parameswaran, VP of Product Management at Emerson | AspenTech, discusses how Industrial AI is reshaping Asset Performance Management, moving it beyond traditional monitoring toward predictive insights, faster maintenance action, and greater scalability across the enterprise.

In this episode, I sit down with Georg Katzlinger to explore the surprising complexity of the flat glass industry and the unique challenges it faces. We dive into how AI solutions are revolutionizing the way custom glass orders are processed, from deciphering handwritten requests to integrating with legacy ERP systems. Georg shares his journey from engineering to AI entrepreneurship, revealing why deterministic, human-in-the-loop automation is essential for manufacturers. I ask the tough questions on accuracy, implementation, and industry-specific hurdles, giving you an insider's perspective on what it takes to bring AI into traditional sectors. If you're curious about the intersection of deep tech and real-world manufacturing, you won't want to miss this conversation.

In this episode of Great Question: A Manufacturing Podcast, Plant Services chief editor Thomas Wilk speaks with Andy Foster, Chief Product Officer at IOTech, about the growing use of edge AI across discrete manufacturing, process manufacturing, and energy operations. The conversation explores how manufacturers are using AI for predictive maintenance, anomaly detection, production optimization, and distributed energy resource management. They also discuss why organizations are moving AI closer to the edge to improve latency, reliability, and data security in mission-critical environments. In addition, the episode examines the evolving governance, lifecycle management, and workforce responsibilities required to safely scale industrial AI systems.

In this episode, I sit down with Steven Yates, CTO and co-founder of Federant, to dive deep into the urgent need for runtime governance in edge AI. Drawing on decades of experience in embedded systems and PLC design, Steven reveals why the shift to the edge demands more than just powerful inference—it requires robust, local authority to keep operations safe when connectivity falters. We unpack real-world incidents where lack of governance led to costly mishaps, and explore how new open-source solutions are bridging the gap between cloud convenience and industrial reliability. If you think cloud SLAs are enough for industrial AI, this conversation will make you rethink the fundamentals. Join me as we explore the future of safe, autonomous operations—and why the old rules of industrial control are more relevant than ever.

Peter Carlsson is Co-founder and former CEO of Northvolt, the European battery manufacturing company that raised more than $13 billion to build a homegrown battery supply chain for Europe, before filing for bankruptcy at the end of 2024. Before Northvolt, Carlsson spent more than a decade at Ericsson building global supply chains and later served as VP of Supply Chain at Tesla during the launch of the Model S. In this live episode of Inevitable from the AENU Summit in Berlin, Carlsson reflects on the rise and fall of Northvolt, the realities of competing with China's electro-industrial stack, and what Europe still gets right in manufacturing and innovation. Peter breaks down why batteries became strategically essential to Europe, what operational challenges slowed Northvolt's scale-up, and how changing EV markets, policy shifts, and financing pressures compounded those problems. Carlsson also mentions his new ventures: Aris Machina, an agentic operating system for manufacturing and Sonder Labs, a sodium-ion battery company focused on building chemistry and supply chains less dependent on China. He talks about AI-driven manufacturing, industrial automation, battery geopolitics, and where Europe can still compete in the next generation of energy and hardware systems.  Episode recorded on April 28 2026 (Published on May 19, 2026).  In this episode, we cover:  (0:00) What happened at Northvolt (2:33) Takeaways from Ericsson and Tesla on factory operations (5:52) Why Europe needed a battery champion like Northvolt (7:01) Northvolt's strategy (8:47) The fall of Northvolt (12:23) The decision Peter wishes he had made differently (15:46) Was Northvolt's chemistry bet a mistake? (17:29) Sonder Labs: The promise of sodium-ion batteries (21:42) Can Europe still compete with China in batteries? (24:05) Aris Machina: AI agents for manufacturing operations (27:31) How AI changes factory productivity and the labor market (29:05) Data sovereignty, AI infrastructure and software challenge (32:35) Industrial automation, precision manufacturing, and fusion (34:48) Where Europe still wins (36:01) Final thoughts on Europe's industrial future Enjoyed this episode? Please leave us a review! Share feedback or suggest future topics and guests at info@mcj.vc.Connect with MCJ:Cody Simms on LinkedInVisit mcj.vcSubscribe to the MCJ Newsletter*Editing and post-production work for this episode was provided by The Podcast Consultant

AI adoption, not innovation, is the real barrier to progress in healthcare and manufacturing. Siemens' Brittany Ng and Rad AI's Demetri Giannikopoulos share what they told the U.S. Senate about deploying AI where it matters most.   In radiology, AI is reducing missed diagnoses, extending specialist expertise to underserved hospitals, and giving physicians more time with patients. In shipyards and factories, industrial AI is automating complex processes, cutting downtime, improving quality, and strengthening domestic manufacturing capacity.   But the real AI adoption challenges aren't technical. They're about data access, governance, workforce readiness, trust, and making sure smaller hospitals and manufacturers aren't left behind.   What you'll learn: What Siemens and Rad AI told the U.S. Senate about real-world AI deployment How AI in radiology is reducing missed diagnoses and extending specialist care How industrial AI is transforming manufacturing and shipbuilding Why AI adoption challenges come down to data access, governance, and trust What responsible AI deployment looks like for smaller organizations Show notes: Siemens VP Addresses Congress on Industrial AI: https://www.siemens.com/en-us/company/insights/us-stories/siemens-vp-addresses-congress-on-industrial-ai/ Less Hype, More Help: AI That Improves Safety, Productivity, and Care - Written Testimony: https://www.radai.com/blogs/less-hype-more-help-ai-that-improves-safety-productivity-and-care-written-testimony

In this episode, we dive deep into the evolving landscape of industrial AI, exploring why simply offering an API is no longer enough for true business value. We reconnect with leading minds behind recent billion-euro deals and discuss the journey from foundational models to real-world industrial impact. Our conversation with Davy Demeyer spotlights the creativity gap in industry, the challenges of automation standards, and what it takes to build the next killer application. We share firsthand insights from pioneers, reflect on lessons learned, and debate how agents, humans, and deterministic code generation will shape tomorrow's factories. If you want the latest and greatest in industrial AI—and why it matters for your business—this is the episode you can't miss.

Inductive Automation cofounders Colby Clegg and Carl Gould go deep on the origins of Ignition, the road to 8.3, and what AI means for industrial automation.Vlad and Dave host Colby Clegg, CEO, and Carl Gould, CTO, of Inductive Automation together for the first time to trace the full arc of the company. The story begins in 2003, when Sacramento systems integrator Steve Heckman brought Colby and Carl in to build the missing glue layer between OT data and modern IT tooling. What began as logging values into SQL databases became Factory PMI and eventually Ignition.A key thread is why Ignition broke through when larger automation vendors had superior distribution. Colby points to Clayton Christensen's Innovator's Dilemma. Incumbents could not match Inductive's unlimited per gateway pricing or partner with integrators because their own services groups competed with them. Carl adds the culture piece. Inductive refused to gate downloads, kept the module SDK open, made education free, and ran a public forum when competitors called it reckless, a posture they once called innovation without permission.Ignition 8.3 takes center stage, arriving after a deliberate five year gap from 8.1. Carl frames it as the completion of work that began with 8.0 in 2018. Gateway configuration is now stored in open, readable formats on disk, the gateway web interface was rewritten, and the platform supports orchestration, environmental separation, and infrastructure as code workflows Carl expects to become table stakes. The release also adds event streams, a revamped historian, and perspective drawing tools. For integrators still on 8.1, 8.3 is the version built for distributed deployments across many gateways.On AI, Carl is candid that the new MCP server module is intentionally a minimum viable product. It ships as a raw toolkit for integrators to author MCP primitives that expose Ignition data to agentic systems like Claude Code. First party MCP tools are coming, but Inductive wants to define the guardrails before shipping an API surface they will support for years. Carl frames AI as a new axis of software possibility, comparable to the shift from DOS to Windows. Colby ties it back to legacy SCADA conversion, framing the security and reliability gains as a national security issue. The episode closes with notes on the Inductive ecosystem, including a new collaboration with Tiger Data behind TimescaleDB, plus career advice on soft skills, context, and agentic coding tools.About Colby Clegg and Carl GouldColby Clegg is the CEO and cofounder of Inductive Automation, the California based company behind Ignition, the cross platform SCADA, MES, and IIoT software used by manufacturers and integrators worldwide. Carl Gould is the CTO and cofounder, leading product and engineering direction across Ignition. Both joined founder Steve Heckman in 2003 and have shaped the platform's open, integrator first philosophy ever since.Inductive Automation: https://www.inductiveautomation.comTimestamps0:00 Introduction1:00 Meet Colby Clegg and Carl Gould2:00 The origins of Inductive Automation in 20038:00 Going to market and the Innovator's Dilemma10:30 Innovation without permission as company culture18:50 Ignition 8.0 and the leap to Perspective26:00 The five year journey to 8.338:00 The MCP server module and AI in Ignition45:30 AI in the control plane and guardrails52:30 Tiger Data and the technology ecosystem1:02:30 Career advice for the next generation1:06:40 What is ripe for innovationReferencesIgnition Community Conference: https://icc.inductiveautomation.comAbout Your HostsVladimir Romanov is a cohost of The Manufacturing Hub Podcast and the founder of Joltek, an independent manufacturing and industrial automation consulting firm specializing in modernization strategy, digital transformation, and workforce development. Joltek works with manufacturers and investors to reduce the risk of modernization and build the internal capability to sustain results.Connect with Vlad: https://www.linkedin.com/in/vladromanov/Want to go deeper? Vlad and the team at Joltek have covered related topics here:Colby Clegg on Ignition 8.3 and Industrial Automation: https://www.joltek.com/blog/industrial-automation-colby-clegg-ignition-8-3Connecting Allen Bradley PLCs to Ignition: https://www.joltek.com/blog/connecting-allen-bradley-plc-ignitionDave Griffith is a cohost of The Manufacturing Hub Podcast and founder of Capelin Solutions, an industrial automation firm helping manufacturers adopt smart manufacturing technology. He brings 15 years of experience in industrial automation and digital transformation.Connect with Dave: https://www.linkedin.com/in/davegriffith23/Subscribe to Manufacturing Hub: https://www.manufacturinghub.liveLinkedIn: https://www.linkedin.com/company/manufacturing-hub-networkYouTube: https://www.youtube.com/@ManufacturingHub

It's Thursday morning and I know you're expecting a fresh episode, but today brings a twist—I have exclusive news that can't go live just yet. We're delaying our usual release by one day to bring you something truly special and timely. In the meantime, I invite you to consider joining us at this year's top AI events or reaching out for more information. Thank you for your patience and understanding as we prepare to share these exciting updates with you. I look forward to reconnecting with you tomorrow for a can't-miss episode.

Manufacturers are integrating AI into their own operations, and it's more than likely that that includes your own company. So how do we message around it? And, should we? To help us sort this out, we're talking to Bryan DeBois, Director of Industrial AI at RoviSys. Connect with Bryan on LinkedIn: https://www.linkedin.com/in/bryan-debois/ Learn more about RoviSys: https://www.rovisys.com/

We have a new podcast partner - please welcome NXAI! In this episode, I dive deep into the evolving world of AI-driven CAD and CAM with Dr. Christian Heining. We unpack why generative AI is capturing the attention of industrial designers and whether the latest integrations—like Anthropic's connector for Autodesk Fusion—are truly game-changing or just another step on a long journey. Dr. Heining shares real-world experiments, candid takes on current limitations, and what it means for both startups and big players in the industrial AI space. We discuss the speed of change, the challenges of adapting teams to new tools, and what's still missing before AI can fully transform the machine-building industry. If you're curious about the intersection of AI, engineering, and the future of design, this episode is a must-listen.

Industrial AI is moving past the chatbot phase. From the Hannover Messe show floor to system integration workflows, here's what end users actually want now.Vlad just returned from his first Hannover Messe, the largest industrial automation and manufacturing trade show in Europe. The takeaway that defined the week was a shift in how end users open conversations. A year ago, every booth visit started with the question, do you have AI? This year every vendor has some flavor of AI, so the question has flipped back to the one that actually matters. How does your product solve a specific problem in my plant? Vlad and Dave unpack what that shift means for vendors, integrators, and the end users buying these tools.On the end user side, the reality is mixed. Most knowledge workers in manufacturing have access to Microsoft Copilot and use it for better emails and meeting notes. Everything else is still mostly experimentation. While auditing PLC and SCADA logic on a recent project, Vlad expected the customer to insist on a hardened on premise model with a Dell IPC and dedicated GPUs. Instead, they shrugged and said put it in ChatGPT, the boilerplate logic has no real IP. Data governance on the carpeted side of the business is mature. On the OT side, it barely exists, and that gap matters as more plant floor data flows toward AI tools.For systems integrators, AI is compressing timelines on slow, repetitive work. Tag validation, electrical drawing automation, screenshot to bill of materials extraction, and functional spec to PLC starting points are all in active development. The tradeoff is that some of these tools save four weeks of manual auditing but require a couple of weeks to set up correctly, and a probabilistic LLM still demands human signoff on safety and control logic. Senior engineers benefit most because they already know what good output looks like. The bigger industry question is what happens to the junior to senior pipeline if entry level work disappears.Hardware tells a different story. Moore's Law, first proposed in 1965, held for about 60 years before chip density at three nanometers and heat budgets broke the cost curve. GPUs on the consumer side have been roughly stagnant since the Nvidia 30 series. On the industrial side, demand for radical hardware change has been low. PLCs, switches, IO modules, and field protocols look much like they did twenty years ago. IO Link, the protocol that should be a baseline for any Industry 4.0 deployment, was founded in 2006. Image recognition has unlocked pick and place applications that used to be too expensive to engineer the traditional way.The workforce thread runs underneath all of this. UPS recently negotiated voluntary buyouts of roughly one hundred and fifty thousand dollars per driver to remove tens of thousands of positions, while large technology firms continue to lay off staff and reinvest in data centers.Timestamps0:00 Introduction1:50 Hannover Messe scale, halls, and country delegations7:20 Booth diversity from startups to hyperscalers and the German military12:20 Why end users have stopped asking, do you have AI19:00 The 1% on the bleeding edge versus the rest of industry25:50 End users sending boilerplate PLC code through ChatGPT29:20 Data governance on the OT side32:50 AI inside systems integration workflows39:50 Workforce shifts: UPS buyouts, FAANG layoffs, and reskilling47:20 Hardware innovation, Moore's Law, and the industrial side59:50 SCADA, MES, ERP, and AI generated dashboards1:03:30 Upcoming shows: Automate 2026, ICC, and moreReferencesHannover Messe: https://www.hannover-messe.deAutomate 2026: https://www.automateshow.comIgnition Community Conference: https://icc.inductiveautomation.comRockwell Automation Fair: https://www.rockwellautomation.com/automationfairAbout Your HostsVladimir Romanov is a co-host of The Manufacturing Hub Podcast and the founder of Joltek, an independent manufacturing and industrial automation consulting firm specializing in modernization strategy, digital transformation, and workforce development. Joltek works with manufacturers and investors to de-risk modernization and build the internal capability to sustain results.Connect with Vlad: https://www.linkedin.com/in/vladimirromanov/Want to go deeper? Vlad and the team at Joltek have covered related topics here:Edge Computing, AI, and the Value of Manufacturing Data: https://www.joltek.com/blog/edge-computing-ai-value-manufacturing-dataSystems Integrators in Manufacturing: https://www.joltek.com/blog/system-integratorsDave Griffith is a co-host of The Manufacturing Hub Podcast and founder of Capelin Solutions, an industrial automation firm helping manufacturers adopt smart manufacturing technology. He brings 15 years of experience in industrial automation and digital transformation.Connect with Dave: https://www.linkedin.com/in/davegriffith23/

In this episode, we dive straight into the heart of the industrial AI landscape – beyond flashy headlines and Instagram-worthy trade shows. We tackle the uncomfortable truths behind 'white label' solutions, the real value (or lack thereof) in APIs, and why simply following the hype won't cut it for true innovation. We share our candid impressions from Hannover Messe, exploring the tension between substance and showmanship, and what it means for the future of industrial-grade AI. Plus, we sit down with Dr. Ferri Abolhassan from T-Systems to uncover how Germany's new AI Factory in Munich is aiming to rewrite the rules for secure, sovereign, and scalable AI infrastructure. Join us for honest insights, pointed critiques, and a look at where industrial AI must go next if we want to deliver real value.

Live von der Hannover Messe 2026: Host Stella-Sophie Wojtczak spricht in dieser Episode mit Neurologiq-Gründer Simon Sack. Er ist der Industrial-AI-Experte der Hannover Messe und teilt seine Einschätzung zum Eigen Engineering Agent von Siemens. Dazu gibt er Einblicke zur Fabrik der Zukunft, erklärt, woran Industrial AI in Deutschland aus seiner Sicht aktuell hängt und welche Chancen der gezielte KI-Einsatz bietet. _Hinweis: Dieser Podcast wird von einem Sponsor unterstützt. Alle Infos zu unseren Werbepartnern findest du [hier](https://linktr.ee/t3npodcast)_.

In this episode, Sam talks with Peter Koerte, member of the managing board and chief strategy and technology officer of Siemens, about how industrial AI is quietly transforming the infrastructure that powers everyday life. While consumer AI grabs headlines, Peter explains how artificial intelligence is improving factories, transportation systems, energy grids, and buildings behind the scenes. The conversation explores what makes industrial AI different — from the need for near-perfect accuracy to the challenge of working with proprietary, domain-specific data. Peter shares examples like predicting train door failures days in advance, optimizing building energy use, and accelerating complex engineering simulations. Peter and Sam also discuss the importance of domain expertise, the value of data-sharing partnerships across companies, and why transformation is as much about people and workflows as it is about technology. Read the episode transcript here. Guest bio: As a member of the managing board, chief strategy officer, and chief technology officer of Siemens, Peter Koerte is responsible for developing the company's strategy and leading its worldwide research and development activities. His current priorities include accelerating development of innovative sustainable technologies and continuing development of the Siemens Xcelerator business platform. Koerte previously headed Digital Health, a Siemens Healthineers unit that develops AI-supported diagnostic procedures for health care. He joined the corporate strategy side of the company in 2007 after working for the Boston Consulting Group. Koerte holds a master's degree in business and engineering from the Karlsruhe Institute of Technology and a doctorate in strategy and international management from the WHU-Otto Beisheim School of Management. He also completed the General Management Program at Harvard Business School. Me, Myself, and AI is a podcast produced by MIT Sloan Management Review and hosted by Sam Ransbotham. It is engineered by David Lishansky and produced by Allison Ryder. We encourage you to rate and review our show. Your comments may be used in Me, Myself, and AI materials. ME, MYSELF, AND AI® is a federally registered trademark of Massachusetts Institute of Technology. All rights reserved.

Andrew Waycott is the President & Co-Founder of TwinThread. Andrew is passionate about the intersection of manufacturing, technology, and Industrial AI, and their impact on efficiencies for today's manufacturers. For the past two decades, on a global scale, Andrew has nurtured this passion to the benefit of clients whose manufacturing processes have exhibited an untapped or business-critical growth opportunity. Working in close cooperation with corporate leaders and operations managers, Andrew has overseen and managed projects that have helped customers generate millions in total savings. The Industry 4.0 Podcast with Grantek delivers a look into the world of manufacturing, with a focus on stories and trends that lead to better solutions.   Our guests will share tips and outcomes that will help improve your productivity. You will hear from leading providers of Industrial Control System hardware and software, Grantek experts and leaders at best-in-class industry associations that serve the Data Centers, Life Sciences, CPG and Food & Beverage industries.

Warehouses have become one of the most important battlegrounds in modern supply chain.In this episode of Supply Chain Now, Scott W. Luton is joined by industry leaders Lance Olmsted, Chief Revenue Officer at IFS, and Patrick Maley, Chief Revenue Officer at IFS Softeon, to explore why warehouse execution now plays a much bigger strategic role across the business. As customer expectations rise and supply chains face more pressure to move faster, they explain how companies are rethinking the warehouse as a source of speed, flexibility, and competitive advantage.The conversation covers how real-time visibility, modern warehouse systems, and industrial AI can help teams make better decisions, respond faster to disruptions, and close the gap between planning and execution. Lance and Patrick also share their perspective on where warehouse operations are headed next and why adaptability will be critical moving forward.Jump into the conversation:(00:00) Intro(03:54) IFS acquires Softeon: what happened(07:34) Why warehouses became a strategic lever(09:31) Biggest pressures: AI adoption & tariffs(18:22) Closing the gap between planning and execution(20:36) Real-time visibility and faster decision-making(22:30) Industrial AI defined & applied in warehouses(29:05) Real-world operational improvements & customer examples(37:10) The warehouse of the next 3–5 yearsAdditional Links & Resources:Connect with Lance Olmsted: https://www.linkedin.com/in/lance-olmsted-98708568/Connect with Patrick Maley: https://www.linkedin.com/in/pmaley/Learn more about IFS: https://www.ifs.com/enLearn more about IFS Softeon: https://www.softeon.com/Learn more about our hosts: https://supplychainnow.com/aboutLearn more about Supply Chain Now: https://supplychainnow.comWatch and listen to more Supply Chain Now episodes here: https://supplychainnow.com/program/supply-chain-nowSubscribe to Supply Chain Now on your favorite platform: https://supplychainnow.com/joinWork with us! Download Supply Chain Now's NEW Media Kit: https://supplychainnow.com/media-kit/WEBINAR- Talent Management Playbook for Supply Chain Leaders: https://bit.ly/4uc2OfBWEBINAR- From Workforce Planning to Hourly Performance Management: How GEODIS Americas Turned Labor Productivity into a Growth Engine: https://bit.ly/4blRfKpWEBINAR- Ahead of Disruption: How AI-First Design Builds Supply Chain Resilience — and Transforms the Teams Behind It: https://bit.ly/4ldRn3b

Discover industrial AI at Hannover Messe with SAP's Matthias Deindl, covering embodied AI, productivity-boosting agents, and demos like ginger-shot packaging, digital twins, warehouse robots and partner integrations. Download the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠episode transcript⁠⁠⁠⁠===== This episode explores industrial AI at Hannover Messe (20–24 April) with SAP's Matthias Deindl. Key topics include embodied AI (robots in production, logistics, asset management) and AI agents that enhance productivity and reduce errors. The SAP booth at HMI features a ginger-shot packaging demonstrator, digital twins, humanoid warehouse handling, CNC machining, and partner integrations. Matthias emphasises the importance of accurate, timely data, using SAP Business Data Cloud to enable autonomous tasks like AI-assisted tendering and robot-led inspections. The SAP booth at HMI features a ginger-shot packaging demonstrator, digital twins, humanoid warehouse handling, CNC machining, and partner integrations. The episode envisions seamless disturbance response, improved productivity for an ageing workforce, and stronger human–AI collaboration.  ===== Guest: Matthias DeindlMatthias Deindl is a digital transformation leader focused on discrete industries and supply chains, with more than 17 years of leadership in dynamic, cross-functional environments. At SAP, he currently leads end-to-end product management for Discrete Industries. Previously, he headed supply chain management initiatives across the global SAP Experience Centers network and oversaw the SAP S.Factory Walldorf, helping customers in process and discrete industries accelerate their digital transformation. Before joining SAP, Matthias served as a Group Leader and Product Owner in a corporate IoT startup at Bosch and worked as an Innovation Manager in Corporate Logistics. Earlier in his career, he was a Project Lead and Head of Department in R&D at RWTH Aachen. He has collaborated with customers across automotive, aviation, pharmaceuticals, mechanical engineering, and logistics. Matthias holds a Diplom in industrial engineering from the Karlsruhe Institute of Technology and a doctorate in mechanical engineering from RWTH Aachen.Host 1: Richard Howells⁠⁠⁠⁠Richard Howells⁠⁠⁠⁠ has been working in the Supply Chain Management and Manufacturing space for over 30 years. He is responsible for driving the thought leadership and awareness of SAP's ERP, Finance, and Supply Chain solutions and is an active writer, podcaster, and thought leader on the topics of supply chain, Industry 4.0, digitization, and sustainability.Host 2: Sin ToSin brings over 15 years of experience in the digital media and technology industry – primarily in marketing, business development, thought leadership, and editorial. At SAP, they ensure that SAP's supply chain solutions are properly visible with a focus on future trends and sustainable innovations as part of the Thought Leadership & Awareness Supply Chain Team.===== Show Links:SAP Digital Supply Chain: www.sap.com/scm Visit us at Hannover Messe (HMI): Hall 15, Booth F08Follow Us on Social Media : Matthias Deindl:LinkedIn: https://www.linkedin.com/in/mdeindl/   Richard Howells:LinkedIn: www.linkedin.com/in/richardjhowells Sin To: LinkedIn: www.linkedin.com/in/sin-to-5334208 SAP Digital Supply Chain:LinkedIn: www.linkedin.com/showcase/sapdsc/ Please give us a like, share, and subscribe to stay up-to-date on future episodes!  ===== Chapters: 00:00:00 Vision for AI Supply Chains00:01:51 Meet Matthias and Industrial AI Today00:02:14 Two AI Tracks Robots and Assistants00:03:30 Data Foundations and Business Data Cloud00:05:04 Deployment Challenges and Quick Win Use Cases00:06:46 Embodied AI Inspection Robots00:09:51 Resilience Roadmap Transparency to Agents00:12:25 Human Machine Collaboration at the SAP Booth00:15:41 Ecosystems and Partner Integration00:19:17 Closing and How to Find SAP at Hannover Messe

In this episode, we dig deep into the evolving landscape of industrial AI, from April Fool's pranks to real advances in robotics and automation. We break down how the line between hype and reality is blurring, and why it's more challenging than ever to separate fact from fiction in the age of agentic AI. We welcome Jonas Messner from NEURA Robotics to unpack how their 'robot gym' is collecting real-world data, why new forms of memory and multi-modal sensing are critical, and how open platforms are redefining collaboration in physical AI. Join us as we connect industry history, current breakthroughs, and bold visions for the future—where robots learn, adapt, and even monetize their skills in dynamic environments.

What if your factory could predict failures before they happen, capture decades of human expertise, and make better decisions than ever before—without replacing the people who run it?Today's guest sits right at the intersection of innovation and industry. Bryan DeBois, Director of Industrial AI at RoviSys, is helping reshape what manufacturing looks like in the age of intelligent machines.From predictive analytics that catch problems before they happen, to data-driven systems that optimize production in real time, Bryan's work is transforming how factories think, learn, and produce. But this isn't about replacing people, it's about amplifying human expertise and capturing decades of industrial knowledge before it disappears.In this episode, we'll explore how smart factories are changing the game, what it really takes to begin a digital transformation, and why trust and transparency are just as critical as algorithms and code.What stands out most is Bryan's ability to make advanced technology practical. He's not talking about theory, he's helping real manufacturers integrate AI in ways that improve safety, efficiency, and sustainability.I speak with Bryan about how artificial intelligence is redefining manufacturing, the challenges of digital transformation, and the future of smart factories.RoviSyshttps://www.rovisys.com

To start the week, Oracle and Oracle NetSuite announced Oracle NetSuite Restaurant Operations. NetSuite followed with a more general AI announcement with the latest additions to NetSuite AI Connector Service. Finally, IFS announced a new pricing model for deployment of Industrial AI.Connect with us!https://www.erpadvisorsgroup.com866-499-8550LinkedIn:https://www.linkedin.com/company/erp-advisors-groupTwitter:https://twitter.com/erpadvisorsgrpFacebook:https://www.facebook.com/erpadvisorsInstagram:https://www.instagram.com/erpadvisorsgroupPinterest:https://www.pinterest.com/erpadvisorsgroupMedium:https://medium.com/@erpadvisorsgroup

In this episode, we dive deep into the challenges facing time series AI model leaderboards, from hidden information leakage to the complexities of benchmarking foundation models. I sit down with Marcel Meyer to unpack why traditional approaches fall short and how our new TS Arena leaderboard is setting a new standard for fair, future-proof evaluation. We explore the pitfalls that plague current benchmarks, the surprising ways data contamination can skew results, and the innovative pre-registration protocol we've developed to keep evaluations honest. If you've ever wondered what it takes to build a truly trustworthy AI leaderboard—or why it matters for industry and research alike—this conversation is packed with insights you won't want to miss.

In this episode, we dive into the cutting edge of industrial AI, from the latest advancements in large language model distillation to the transformative power of AI agents in manufacturing. I sit down with Tanmay Aggarwal, CEO of Lambda Function, for a deep conversation about autonomous CNC programming, real-time shop floor integration, and why memory and causal inference matter for the next wave of physical AI. We also unpack industry trends – from SD card AI accelerators to Europe's strategic push for data sovereignty – and share candid thoughts on what it takes to move from AI-assisted workflows to true autonomy. If you're curious about how AI is reshaping industrial strategy, or just want to hear how real-world manufacturing is meeting the future, this one's for you. Join us as we connect the dots between research breakthroughs, practical deployments, and the evolving competitive landscape of industrial AI.

What does it really take to move AI from impressive demos into the hands of the people who keep the world running every day? In this episode of Tech Talks Daily, I sat down with Kriti Sharma, CEO of IFS Nexus Black, to explore a side of AI that rarely gets the spotlight. While much of the conversation around artificial intelligence focuses on chatbots and copilots, Kriti is working in environments where failure is not an option. Manufacturing plants, energy grids, airlines, and field service operations all depend on precision, experience, and consistency. What struck me early in our conversation was how she reframes the entire AI debate. The challenge is not building the technology, it is building trust in it. Kriti's journey into AI began long before it became a boardroom priority. From building her first robot as a teenager to advising global organizations and policymakers, she has always focused on solving real problems rather than chasing trends. That perspective carries through into her work today, where she spends time on factory floors wearing safety gear alongside engineers and technicians.  It is a hands-on approach that reveals something many leaders miss. People do not adopt AI because it is advanced. They adopt it when it solves a problem they recognize in their day-to-day work. One of the most interesting themes we explored was the widening gap between what AI can do and how quickly organizations are ready to use it. Kriti described how that gap plays out on the ground, especially among deskless workers who make up the majority of the global workforce. In these environments, the conversation is far less about replacing jobs and far more about preserving knowledge, improving consistency, and helping people perform at their best. When a veteran worker with decades of experience walks out the door, that expertise often leaves with them. AI, when designed well, can help capture and share that knowledge across an entire workforce. We also discussed how IFS Nexus Black is tackling what many describe as "pilot purgatory," where companies experiment with AI but struggle to deploy it at scale. Kriti shared how building solutions alongside customers, rather than handing over generic tools, leads to faster adoption and measurable results. Real-world examples brought this to life, including how industrial AI is helping organizations move from reactive firefighting to proactive decision-making, reducing downtime and improving operational performance in ways that directly impact the bottom line. As our conversation moved toward the future, Kriti offered a clear message for leaders. The best way to prepare for AI is to start using it. Not as a novelty, but as a daily tool that can amplify how work gets done. The organizations that encourage experimentation and share those learnings across teams are the ones most likely to see real impact. So as AI continues to evolve at pace, the question is no longer whether the technology is ready. It is whether organizations and their people are ready to meet it halfway, and what happens if they are not?

Corporate innovation programs often generate activity but struggle to produce measurable business impact. Ronja Stoffregen, Director of Corporate Venturing at REHAU New Ventures, shares how venture clienting can bridge that gap by turning startup partnerships into operational outcomes. Leading REHAU's corporate venturing efforts across mobility, manufacturing, medtech, and the built environment, Ronja focuses on embedding emerging technologies—especially industrial AI, automation, and sustainability—directly into production environments and supply chains. In this conversation, Ronja breaks down what makes venture clienting fundamentally different from traditional corporate venture capital and how enterprises can structure programs that deliver both near-term operational wins and long-term strategic advantage. She shares the frameworks her team uses to move from pilot to production, why she measures pain points solved, how she launched six startup pilots in six months, and what it takes to build a venture client unit with limited budget and headcount.

In this episode, I sit down with Tobias, a robotics and automation expert, to unpack what AI agents and generative AI mean for the future of industrial automation. We explore how combining human expertise with advanced agents can optimize production cycles, break down technological silos, and open new possibilities for greenfield automation projects. From hands-on case studies to the hurdles of legacy systems, we dig into what works today, what doesn't, and where the next breakthroughs might come from. I share insights from our own experiments—what happens when you let an AI agent optimize a real automation cell, and why, for now, human know-how still matters. If you're curious about the practical realities of AI in production lines and what's hype versus what's already delivering value, this conversation is for you.

Jeff Bezos is looking to raise $100 billion dollars for a new fund. According to the Wall Street Journal, the idea is to buy manufacturing companies and use AI to ramp automation. Described as a "manufacturing transformation vehicle," Bezos, who has a net worth of approximately $250 billion as of late last year, is reportedly reaching out to major financial players in the Middle East and Singapore to raise money. Bezos is reportedly looking to target large industries, including aerospace, chipmaking and defense. Last year, The New York Times reported that Bezos would return to an operational role for the first time since 2021, when he stepped down as Amazon CEO. He'll serve ⁠as co-CEO of a new startup called Project Prometheus. Prometheus launched with some $6.2 billion in funding and a focus on using AI to engineer and manufacture hard goods, like cars, planes and computers. Project Prometheus reportedly built a staff of nearly 100 employees, including recruits from OpenAI, DeepMind, and Meta, to work on physical AI, machine learning systems that source real-world data to make decisions in real-time.#JeffBezos #Amazon #AI #ArtificialIntelligence #Manufacturing #Automation #Industry40 #SmartManufacturing #FutureOfWork #Robotics #MachineLearning #TechNews #Innovation #Aerospace #DefenseIndustry #Semiconductors #SupplyChain #IndustrialAI #Engineering #FactoryOfTheFuture #DigitalTransformation #BusinessNews #Startups #VentureCapital #TechInvesting

Industrial AI is getting a lot of attention in manufacturing right now, but one of the biggest questions is still the most practical one. How do you turn plant data, process knowledge, and operational constraints into something that actually creates value? In this episode of Manufacturing Hub, Vlad Romanov and Dave Griffith sit down with Konstantin Paradizov of Eukodyne for a detailed conversation on what industrial AI looks like when it is applied by people who understand manufacturing, MES, process improvement, data architecture, and the realities of the plant floor.What makes this discussion especially valuable is that it does not stay at the surface level. Konstantin shares how his background moved from pharma into food and beverage, how Lean Six Sigma and process thinking shaped his approach, and why many of the best opportunities in manufacturing still begin with understanding the actual workflow before talking about software. The conversation explores a theme that comes up again and again in industrial transformation: the biggest gains often do not come from adding more technology first. They come from understanding the problem clearly, identifying what information matters, validating assumptions with the people doing the work, and then using the right mix of tools to move faster.A major part of this episode focuses on the real use of AI in consulting and discovery. Konstantin explains how his team uses secure transcription workflows, on premises AI infrastructure, cloud models, masking of sensitive information, iterative validation, and ROI driven reporting to create high value outputs in a fraction of the time that would have been required even a year or two ago. This is an important point for manufacturers, system integrators, software teams, and plant leaders. AI is not just something that sits in front of an operator as a chatbot. It can be used behind the scenes to accelerate analysis, strengthen recommendations, shorten discovery, improve documentation, and reduce the cost of getting to a better answer.The technical section of this episode is especially strong for anyone working in industrial automation, OT data systems, or applied AI. The discussion covers on premises compute, Nvidia based edge hardware, Linux environments, Docker containers, RAG workflows, vector databases, knowledge graphs, MQTT pipelines, HiveMQ, Mosquitto, n8n, Claude Code, Cursor, Gemini, OpenRouter, and the tradeoffs between frontier models in the cloud and smaller or open models deployed closer to the process. One of the clearest takeaways is that manufacturers should not start with the biggest model or the most exciting headline. They should start with the problem, the constraints, the data path, and the economics of the solution.Vlad also pushes on an issue that matters to almost every manufacturer trying to prepare for AI. If you collect massive amounts of plant data into historians, cloud platforms, and enterprise systems, is that enough to create value later? Konstantin's answer is thoughtful and realistic. More data alone does not automatically lead to better outcomes. You still need filtering, context, prioritization, architecture, and a disciplined way to separate signal from noise.Learn more about Joltek here:https://www.joltek.com/serviceshttps://www.joltek.com/services/service-details-it-ot-architecture-integrationConnect with our guest:Konstantin Paradizovhttps://www.linkedin.com/in/konstantin-paradizov/Learn more about Eukodyne:https://eukodyne.com/Follow Manufacturing Hub for more conversations on industrial AI, digital transformation, OT architecture, SCADA, MES, industrial data strategy, systems integration, and the future of manufacturing technology.Timestamps00:00 Welcome and introduction to industrial AI applications01:50 Konstantin's background from pharma to manufacturing05:30 Why food and beverage offered major process improvement opportunities08:10 How to identify the right manufacturing opportunities to pursue13:10 Using AI to accelerate discovery, documentation, and customer value21:20 The on premises AI hardware stack and model selection strategy30:10 Why iterative validation still matters more than a first AI answer39:00 Claude Code, developer workflows, and practical AI tool stacks48:20 On premises versus cloud AI and how to think about the tradeoff54:10 Small models, low cost hardware, and edge deployment realities01:05:00 Plant data, historians, filtering, and separating signal from noise01:14:50 Predictions for industrial AI, career advice, and final recommendationsReferences and resources mentioned in the episodeMaintainXhttps://www.maintainx.com/Solve for Happyhttps://www.mogawdat.com/booksGeorge Orwell 1984https://www.penguinrandomhouse.com/books/326569/1984-by-george-orwell/George Orwell Animal Farmhttps://www.penguinrandomhouse.com/books/561805/animal-farm-by-george-orwell/

In this episode, we dive into the real-world impact of AI-powered knowledge platforms for industry. We sit down with Jakub from Zeta Alpha to uncover how companies like Festo are leveraging advanced search, RAG, and agentic AI to unlock decades of expertise buried in documents and data silos. We share practical examples, discuss integration challenges, and debate the future of specialized versus generic AI in the industrial landscape. You'll hear why trust, customization, and domain focus are key for adoption – and how European innovation is shaping sovereign AI solutions. Join us as we reveal what it really takes to turn industrial knowledge into a competitive advantage.

A inteligência artificial já escreve textos, cria imagens e responde perguntas. Mas uma nova fase da tecnologia começa a ganhar espaço: a chamada Physical AI, em que sistemas inteligentes passam a interagir diretamente com o mundo físico em robôs, fábricas, armazéns e máquinas industriais. No episódio de hoje do Podcast Canaltech, conversamos sobre como essa evolução da IA está transformando a indústria, o que são os chamados gêmeos digitais, e por que empresas estão usando simulações virtuais para testar decisões antes de aplicá-las no mundo real. Para entender melhor esse cenário, Fernanda Santos conversa com Daniel Lázaro: Líder de Dados e IA na Accenture da América Latina e Guilherme Goehringer: Diretor de Industrial AI no Brasil e na América Latina. Eles explicam como a inteligência artificial pode ajudar máquinas a perceber, interpretar e agir em ambientes físicos e o que isso pode mudar nas empresas e no mercado de trabalho nos próximos anos. Você também vai conferir: Exército cria sistema para comandar enxame de drones, fone diferente da JBL chega ao Brasil e Microsoft revela detalhes do próximo Xbox. Este podcast foi roteirizado e apresentado por Fernanda Santos e contou com reportagens de Paulo Amaral, Vinicius Moschen e Gabriel Cavalheiro, sob coordenação de Anaísa Catucci. A trilha sonora é de Guilherme Zomer, a edição de Leandro Gomes e a arte da capa é de Erick Teixeira.See omnystudio.com/listener for privacy information.

Manufacturing Hub is back with Episode 252, where co hosts Vlad Romanov and Dave Griffith break down what an AI survival guide should actually look like for manufacturing and industrial automation professionals. This is not a hype conversation about replacing people with magic software. It is a grounded discussion about what AI tools can do today, where they fail, why context and data quality matter so much, and how industrial teams should think about experimentation without losing sight of real operating constraints.In this episode, Vlad and Dave unpack the evolution many engineers and technical leaders have already felt in real time, from early prompt engineering, to agent based workflows, to MCP servers, skills, context management, and the growing cost of tokens and infrastructure. The conversation moves beyond generic AI commentary and into the reality of plant floor environments, where success depends on process knowledge, data architecture, OT constraints, cybersecurity, governance, and clear business value. One of the strongest themes throughout the episode is that manufacturers cannot skip the hard work of structuring data, understanding workflows, and defining use cases simply because AI tools are moving quickly.Vlad brings a very practical industrial lens to the discussion. Drawing on years of hands on experience across controls, manufacturing systems, plant modernization, and digital transformation, he explains why industrial AI has to start with operational context. A maintenance team, an engineering team, and a quality team do not need the same data, do not ask the same questions, and should not be handed the same AI workflows. That distinction matters. This conversation also highlights why the best industrial AI implementations will likely come from teams that combine domain expertise with strong technical execution, rather than generic AI shops trying to force a solution into environments they do not fully understand.Dave adds an important systems and adoption perspective, especially around cost, scaling, management expectations, and the danger of trying to prompt your way past foundational architecture work. Together, Vlad and Dave explore why manufacturers are interested in AI, why many are afraid of being left behind, and why so many projects still stall once they hit the realities of obsolete equipment, weak data models, fragmented systems, and unclear ownership of information. They also discuss deterministic logic versus LLM behavior, reporting workflows, industrial dashboards, PLC code generation concerns, and the practical question every manufacturer should ask before investing: what problem are we solving, for whom, and what is the measurable return?For those new to Vlad, he is an electrical engineer and manufacturing leader with deep experience across industrial automation, controls, data systems, OT architecture, modernization strategy, and plant operations. Through Joltek, Vlad works with manufacturers on digital transformation, IT OT architecture and integration, modernization planning, operational improvement, and technical workforce enablement. Learn more here:Joltek: https://www.joltek.com IT OT Architecture and Integration: https://www.joltek.com/services/service-details-it-ot-architecture-integrationIf you are a plant leader, controls engineer, systems integrator, OT architect, SCADA or MES practitioner, or simply someone trying to separate useful AI workflows from noise, this episode will give you a much more realistic framework for thinking about industrial AI adoption.Timestamps00:00 Welcome back and why this episode matters01:00 Setting up the industrial AI theme for the coming weeks03:10 From prompt engineering to structured AI workflows05:30 AI agents, parallel workflows, tokens, and context windows09:00 MCP tools, Playwright, and what new integrations unlock16:20 How Vlad researches AI and where useful information actually lives22:00 Real manufacturing problems versus AI in search of a problem29:40 Why industrial data architecture is harder than most people think37:00 OT expertise, workforce enablement, and who should build solutions45:40 Practical advice for manufacturers starting the AI journey50:30 Data governance, hallucinations, infrastructure, and cybersecurity57:20 What looks promising today in reporting, dashboards, and industrial applications

The startup, which was created by Rivian founder RJ Scaringe, is looking to train on data from, and deploy in, Rivian's factory. Plus, Replit raised a new $400 million round and said it hopes to have $1B in ARR by year's end. And, Breakout Ventures is finding growing success by backing early-stage startups in scientific fields such as biology and chemistry. Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode, we dive into the resurgence of world models in AI and explore why the industry is revisiting neurosymbolic approaches. We challenge the dominance of data-centric models and discuss how combining symbolic knowledge with machine learning could unlock new breakthroughs. Our conversation highlights Europe's unique strengths and potential as a leader in this evolving landscape. We also address the importance of domain expertise and mathematics, and question whether the current AI race is stifling real innovation. Join us for a candid discussion on what the next wave of AI could—and should—look like. We thank our partner [**SIEMENS** ](https://www.siemens.de/de/)

Can Nvidia Hit $500 BILLION?

In this episode, I sit down with Andre Sagodi, a manufacturing and innovation expert at Audi, to explore how artificial intelligence is transforming the car production process. We dive into Audi's journey from piloting AI-based crack detection in sheet metal parts to scaling these solutions across multiple global sites. Andre shares practical insights on overcoming challenges like data consistency, model scalability, and workforce collaboration, all while aligning with Audi's strategic vision. Together, we discuss what it really takes to turn promising AI pilots into production-ready systems that deliver real business value. If you're curious about the future of industrial AI and want a behind-the-scenes look at innovation inside a world-class manufacturer, this episode is for you.

In this episode of Manufacturing Hub, Vlad Romanov and Dave Griffith sit down with David for a practical, operator grounded conversation about industrial data, modernization, and what it actually takes to turn plant floor signals into business decisions. David has spent more than two decades in manufacturing across automotive, solar, and electric vehicles, and his story is a familiar one for a lot of us. He walked into a plant thinking he was there for a project, discovered PLCs in real time, and never left the factory world. From early days wiring up a SQL Server to pull line data instead of sending people out with stopwatches, to leading data and analytics and shaping MES and reporting strategy, this conversation stays focused on the messy middle where most factories live.A big theme here is that collecting data is not the same thing as creating information. As tooling has improved, connectivity, historians, SCADA, cloud storage, MQTT, and the modern ecosystem have made it easier to get signals out of machines. The hard part is deciding what matters, aligning stakeholders, and creating context that survives across teams and projects. David breaks down how real progress often starts with simple visibility, what is ruining your day, what is the biggest safety risk, what is the recurring quality miss, what is the downtime story you do not trust, then builds from there using workshops and iterative delivery instead of giant multi year “boil the ocean” programs.We also get into Unified Namespace, why it resonates with people who have been burned by tightly coupled ISA style integrations, and why change management is the hidden cost. If you are exploring UNS, this episode highlights the difference between drawing the box on a whiteboard and getting a whole organization to actually adopt consistent naming, context, and ownership. Then we finish with a grounded take on industrial AI. No hype, no doom. Just a realistic view of where AI helps today, where it breaks, and why context windows, documentation quality, and domain expertise still decide whether results are useful or dangerous.Timestamps00:00:00 Welcome and the month theme on technology modernization00:02:10 David's background from automotive and the Tesla Fremont NUMMI era to data leadership00:05:10 The moment data became “real” and why proactive visibility drives safety and outcomes00:07:10 How Kaizen and Toyota Production System style problem solving creates demand for data00:11:50 Why modern tooling makes collection easier and why budget and commitment still decide success00:16:10 Starting points that work in the real world and the simplest visibility model that scales00:18:20 Unified Namespace explained through decoupling, context, and why the first attempt often fails00:23:50 Who really uses the data, operators, quality, engineering, and the “next factory” teams00:29:10 Defining KPIs when nobody has answers and using workshops to force prioritization00:34:20 What rollouts actually take, machine states, data structures, controls changes, and iteration00:40:10 Industrial AI reality check, where it helps today and why it is not running your factory00:51:10 Predicting the next few years, consolidation, pricing, and better integration with agentsAbout the hostsVlad Romanov is an industrial automation and manufacturing leader with over a decade of plant floor experience across major manufacturers. He is the founder of Joltek, where he helps teams modernize operations through IT and OT architecture, integration, reliability focused execution, and practical upskilling that actually sticks. Joltek works with manufacturers who need real outcomes, not buzzwords, and the work spans controls, data, networking, and operational performance.Dave Griffith is the co host of Manufacturing Hub and works at the intersection of manufacturing operations, technology modernization, and practical delivery. He focuses on helping teams bridge the gap between “we want data” and “we can run this plant better next quarter.”About the guestDavid has 25 plus years of manufacturing experience spanning automotive, solar manufacturing, and EVs. He started in plant floor automation and conveyance projects, then moved deeper into industrial data, MES, and analytics leadership. His recent work includes leading data and analytics, defining KPI strategy, and building the layers required to turn raw plant signals into usable business information.Links from Joltekhttps://www.joltek.com/blog/mastering-unified-namespace-uns-a-guide-to-data-driven-manufacturing-transformationhttps://www.joltek.com/blog/ultimate-guide-mqtt-manufacturingSubscribe for more conversations on manufacturing modernization, industrial data architecture, MES realities, and what works on the plant floor when the budget, people, and legacy systems are all real.

In this episode, we journey from weather woes in Franconia straight into the heart of Amsterdam's AI innovation, exploring what happens when robotics, world models, and agentic AI collide. We share our firsthand impressions from deep-dive research sessions at Lab42, where startups and researchers are joining forces to push industrial AI forward. You'll hear my conversation with John Harrington of HighByte, who breaks down the role of MCP services and why they're critical for bridging the gap between factory data and intelligent agents. We don't just talk tech—we talk about the people, the practical challenges, and the evolving landscape that's making industrial data accessible to everyone, not just engineers. If you're curious about how symbolic AI, digital cousins, and scalable architectures are transforming manufacturing, or you want to know what's next for AI in the Alps, this episode is your front-row seat. Tune in for insights, laughs, and a clear-eyed look at the road ahead for industrial-grade AI.

In this episode of Manufacturing Hub, Vlad Romanov and Dave Griffith sit down with Benson Hougland from Opto 22 to get brutally practical about what is actually running on shop floors today, and what it takes to move from legacy automation to modern, data ready operations without breaking what already works. If you have ever walked into a plant and seen a mix of decades old controllers, manual processes, islands of automation, and a few shiny modern pockets of connectivity, this conversation will feel very familiar. Benson has spent roughly three decades at Opto 22 and he has seen the full spectrum, from brownfield realities where nothing can go down, to greenfield expansions where teams can finally design with data, security, and integration in mind.A major thread in this discussion is the gap between “the machine runs” and “the business can learn from the machine.” Benson lays out why so many facilities still operate in a world of siloed equipment with minimal visibility, and why digital transformation stalls when the goal is vague or driven by trend chasing. The most actionable insight is simple: start with a real problem, win small, build trust in the data, and only then scale. That approach is how you avoid proof of concept purgatory, and it is also how you get leadership buy in without overpromising. If you are looking at industrial AI, it becomes even more critical, because manufacturing cannot tolerate hallucinated answers. Benson explains why industrial AI starts with sanctity of data, meaning clean, contextualized, trustworthy signals that an organization can actually act on.You will also hear a grounded take on why hardware still matters in 2026. Not because everyone wants to rip and replace working PLCs, but because modern plants need layered edge strategies that can extract the right data, protect legacy assets, and integrate upward using open methods.About the guestBenson Hougland is a long time leader at Opto 22, a US based manufacturer of industrial controllers, edge devices, and IO. He focuses on customer and integrator feedback, product strategy, and the practical challenges teams face when modernizing systems while keeping operations running. Opto 22 is known for building and manufacturing in the United States and for leaning into open connectivity approaches that help reduce lock in and simplify integration.About the hostsVlad Romanov is an electrical engineer with an MBA from McGill University and over a decade of experience delivering automation and modernization work across high performing manufacturing environments. Through Joltek, Vlad supports manufacturers with plant floor assessments, controls and OT architecture, system modernization planning, integration execution, and technical upskilling so teams can own their systems long term. Vlad's work consistently sits at the intersection of reliability, operational execution, and the realities of IT and OT convergence, with a focus on what is feasible in real facilities, not just what looks good in a slide deck.Dave Griffith is a long time manufacturing and automation practitioner focused on bridging the gap between modern technology conversations and what is practical on the plant floor. Dave brings a systems mindset to modernization, with a strong emphasis on outcomes, maintainability, and the human factors that decide whether projects scale or stall.If this episode resonates and you are navigating modernization decisions, especially around OT networking, data infrastructure, platform selection, or plant floor security, Joltek can help you evaluate your current state, define a realistic target architecture, and build a roadmap that your team can execute.Joltek linkshttps://www.joltek.com/serviceshttps://www.joltek.com/education/ot-networking-fundamentalsTimestamps00:00:00 Welcome back and the hardware focused modernization theme00:01:40 Benson Hougland background, entrepreneur to controls to Opto 2200:04:10 A garage manufacturing story and the lessons of building real product00:09:00 The gap between cutting edge plants and manual, siloed operations00:11:10 What actually blocks modernization, capital, planning, and alignment00:13:10 Start small, solve a real problem, and build trust in outcomes00:14:40 Proof of concept purgatory and why leadership buy in changes everything00:17:50 Industrial AI needs data, and data integrity becomes the non negotiable00:22:30 Obsolescence, cybersecurity, and simplifying the industrial tech stack00:28:20 Cybersecurity is a process, not a product, and why defaults are deadly00:37:10 Linux at the edge, containers, and why modern controllers are like smartphones00:53:10 ProveIt and the virtual factories approach, real data, real integration paths

Industrial AI startup CVector built a brain and nervous system for big industry. Now, founders Richard Zhang and Tyler Ruggles are tasked with a bigger challenge: showing customers and investors how this AI-powered software layer translates to real savings on an industrial scale.  Learn more about your ad choices. Visit podcastchoices.com/adchoices

CES 2026 Just Showed Us the Future. It's More Practical Than You Think.CES has always been part crystal ball, part carnival. But something shifted this year.I caught up with Brian Comiskey—Senior Director of Innovation and Trends at CTA and a futurist by trade—days after 148,000 people walked the Las Vegas floor. What he described wasn't the usual parade of flashy prototypes destined for tech graveyards. This was different. This was technology getting serious about actually being useful.Three mega trends defined the show: intelligent transformation, longevity, and engineering tomorrow. Fancy terms, but they translate to something concrete: AI that works, health tech that extends lives, and innovations that move us, power us, and feed us. Not technology for its own sake. Technology with a job to do.The AI conversation has matured. A year ago, generative AI was the headline—impressive demos, uncertain applications. Now the use cases are landing. Industrial AI is optimizing factory operations through digital twins. Agentic AI is handling enterprise workflows autonomously. And physical AI—robotics—is getting genuinely capable. Brian pointed to robotic vacuums that now have arms, wash floors, and mop. Not revolutionary in isolation, but symbolic of something larger: AI escaping the screen and entering the physical world.Humanoid robots took a visible leap. Companies like Sharpa and Real Hand showcased machines folding laundry, picking up papers, playing ping pong. The movement is becoming fluid, dexterous, human-like. LG even introduced a consumer-facing humanoid. We're past the novelty phase. The question now is integration—how these machines will collaborate, cowork, and coexist with humans.Then there's energy—the quiet enabler hiding behind the AI headlines.Korea Hydro Nuclear Power demonstrated small modular reactors. Next-generation nuclear that could cleanly power cities with minimal waste. A company called Flint Paper Battery showcased recyclable batteries using zinc instead of lithium and cobalt. These aren't sexy announcements. They're foundational.Brian framed it well: AI demands energy. Quantum computing demands energy. The future demands energy. Without solving that equation, everything else stalls. The good news? AI itself is being deployed for grid modernization, load balancing, and optimizing renewable cycles. The technologies aren't competing—they're converging.Quantum made the leap from theory to presence. CES launched a new area called Foundry this year, featuring innovations from D-Wave and Quantum Computing Inc. Brian still sees quantum as a 2030s defining technology, but we're in the back half of the 2020s now. The runway is shorter than we thought.His predictions for 2026: quantum goes more mainstream, humanoid robotics moves beyond enterprise into consumer markets, and space technologies start playing a bigger role in connectivity and research. The threads are weaving together.Technology conversations often drift toward dystopia—job displacement, surveillance, environmental cost. Brian sees it differently. The convergence of AI, quantum, and clean energy could push things toward something better. The pieces exist. The question is whether we assemble them wisely.CES is a snapshot. One moment in the relentless march. But this year's snapshot suggests technology is entering a phase where substance wins over spectacle.That's a future worth watching.This episode is part of the Redefining Society and Technology podcast's CES 2026 coverage. Subscribe to stay informed as technology and humanity continue to intersect.Subscribe to the Redefining Society and Technology podcast. Stay curious. Stay human.> https://www.linkedin.com/newsletters/7079849705156870144/Marco Ciappelli: https://www.marcociappelli.com/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Artificial intelligence is no longer just a tool – it's becoming a force in the physical world. This special episode of The Optimistic Outlook highlights how industrial AI is moving beyond software to drive real-world impact. From factories and power grids to buildings, transportation systems, and even drug discovery, industrial AI is reshaping the systems that underpin everyday life.   You'll hear excerpts from Siemens President and CEO Roland Busch's CES keynote, including onstage conversations with leaders such as NVIDIA CEO Jensen Huang, Microsoft CEO Satya Nadella, PepsiCo Latin America CEO Athina Kanioura, and Commonwealth Fusion Systems CEO Bob Mumgaard. Together, they explore how digital twins, AI-powered simulation, and deep industry partnerships are already delivering measurable results—and setting the foundation for an industrial AI revolution.   Throughout the episode, Siemens USA Interim President and CEO Ann Fairchild provides context and commentary on what these breakthroughs mean for customers, industries, and society – and why Siemens is uniquely positioned to help scale industrial AI responsibly and impactfully.   Show notes Siemens CES Keynote with Roland Busch (Full video on YouTube) Siemens at CES 2026: Official Landing Page 

Nvidia and Siemens announced plans to build an industrial AI operating system together. CEOs Jensen Huang and Roland Busch speak to Ed Ludlow about this partnership, the need for energy, Nvidia's new chips, China and Siemen's potential deals in the operations software space. They sit down at the Consumer Electronics Show.See omnystudio.com/listener for privacy information.

2024 was the year of "Generative AI Hype," AWS re:Invent 2025 was the moment the cloud officially got physical.Fresh off the event in Las Vegas, Colin Masson, Director of Research for Industrial AI, ARC Advisory Group sat down with Steve Blackwell, Leader of the Worldwide Manufacturing Center of Excellence at AWS, to compare notes. He had just published ARC's Top 10 Takeaways for Industrial AI, and Steve came prepared with his own list: the Top 7 Strategic Announcements that he believes will reshape the manufacturing landscape.Colin's conversation zeroed in on the practical realities of deploying AI—from the "silicon to the edge." They discussed the massive potential of AWS Transform to pay down technical debt, the shift to "Frontier Agents" like Kiro, and the critical need for a new kind of talent through the Physical AI Fellowship.You can listen to the full conversation Here on Buzzsprout or watch the video episode on the ARC Advisory Group YouTube Channel.Would you like to be a guest on our growing podcast?Do you have an intriguing or thought provoking topic you'd like to discuss on our podcast? Please contact Our Producer Tom Cabot at: Tcabot@Arcweb.comView all the episodes here: https://thedigitaltransformationpodcast.buzzsprout.com

As 2026 approaches, manufacturers are under growing pressure to move faster, operate more flexibly, and compete in an increasingly complex global landscape. At the same time, the tools to meet those challenges are finally positioned to move from promise to real-world impact. In this year-end episode of The Optimistic Outlook, Siemens USA Interim CEO Ann Fairchild sits down with Del Costy, President and Managing Director of Siemens Digital Industries Americas, to explore what's changing—and what's still holding manufacturers back. Drawing on conversations with industrial leaders across the country, Del offers a clear-eyed view of today's technology adoption curve, from the realities of deploying Industrial AI to the strategic importance of digital twins, simulation, and edge processing. Together, they examine how technology, data, and people are converging to shape a new industrial tech sector—and what it will take for manufacturers to translate innovation into speed, resilience, and long-term competitiveness in 2026 and beyond.

What happens when a founder who built a billion dollar company during a global crisis steps into the centre of industrial AI and begins reshaping how entire organisations think and work? That question sat at the heart of my conversation with Somya Kapoor, CEO of IFS Loops, recorded live on the show floor at IFS Industrial X Unleashed. Somya's journey carries a level of grit and perspective that shines through every answer. She shared how surviving the Gulf War as a child shaped her instinct to take on the hardest problems in technology. That mindset not only guided her early career at SAP, ServiceNow, and other enterprise giants, it also laid the foundation for Loops, the agentic platform she co-founded in 2020 with a simple scribble on a notepad that eventually grew into one of the most significant acquisitions in the IFS ecosystem. Her stories about early rejections, the wave of scepticism around AI in the early days, and the first customer conversations held on Zoom during lockdown reveal the human side behind a platform many now take seriously across the industrial world. Across the episode, Somya explained in plain terms what makes IFS Loops so different. The platform connects data across systems using natural language, helps redesign processes that used to be locked inside individual applications, and introduces digital workers that remove the grunt work from everyday operations. She brought the technology to life with examples that landed with real clarity. From supplier order handling to complex field service tasks, and the now famous Kodiak Gas case where thousands of hours were saved each year, she showed how agentic workflows change what is possible for industrial companies who have spent decades wrestling with fragmented data and rigid processes. We also talked about the importance of keeping people at the centre of AI driven change. Somya was clear that amplification, not replacement, is the story that matters. The shift requires new skills, new supervision models, and a thoughtful approach to adoption. Her reflections on change management, the energy she felt from customers at the event, and the speed at which leaders now want to move painted a picture of an industry that feels very different from the early days of AI excitement. The hesitation has faded. Curiosity has taken over. Action is starting to follow. Somya closed with a message aimed at every leader who might still be watching from the sidelines. The technology is real, adoption is accelerating, and the window to learn, experiment, and adapt is narrowing. She believes this is the moment for teams to decide whether they want to lead or be led by others who are moving faster. As you listen to this conversation, I'd love to hear what stood out for you. Do you feel the same shift in confidence and urgency around industrial AI that Somya described? Let me know your thoughts.   Tech Talks Daily is Sponsored by NordLayer: Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.