Podcasts about industrial ai

Industrial AI is into a new phase. Agentic AI is software that can plan, reason, learn, and act with far greater independence than traditional automation. While the technology is advancing quickly, the oil and gas industry faces unique technical, regulatory, and operational challenges that make full autonomy far less straightforward.  In this episode, I share what agentic AI actually is, why it differs from conventional automation, and why the industry's aging assets, fragmented data, safety requirements, cybersecurity concerns, and accountability standards create real barriers to  adoption.  I also share where I believe agentic AI can deliver the greatest value today: reducing the cognitive burden on engineers by assisting with planning, monitoring, coordination, and decision preparation while keeping humans squarely in the loop. The future is a function of humans plus AI, instead of the gloomy either/or we hear.  ⚒️ Additional Tools & Resources:

In this episode, we dig into the recent Anthropic ban and what it signals for the global AI landscape. I connect with Jakob Tomczak to get an insider's perspective from Silicon Valley and explore how this move impacts European companies, innovation, and the future of AI ecosystems. We also share firsthand impressions of the latest LLMs, debate whether true AI revolutions are on the horizon, and discuss the challenges Europe faces in scaling up. If you're curious about the real dynamics behind AI development, hardware, and the shifting balance of power in tech, this conversation is for you.

Deutschlands besondere Stärke im internationalen KI-Wettbewerb liegt in der industriellen Anwendung. Das zeigt eine neue Studie der IW Consult im Auftrag von eco. Bereits heute entstehen in Deutschland mehr als 120 Milliarden Euro Umsatz durch KI-gestützte Produktinnovationen und neue Dienstleistungsangebote. Entscheidend ist dabei die Verbindung von leistungsfähigen KI-Modellen mit Unternehmensdaten, Branchenwissen, Engineering-Kompetenz und industriellen Prozessen. In dieser Folge von „Das Ohr am Netz“ sprechen Sidonie Krug und Sven Oswald mit Gästen aus Verband, Industrie und Cloudwirtschaft über den deutschen Industrial-AI-Ansatz. Im Mittelpunkt stehen die wirtschaftlichen Ergebnisse der Studie, praktische Erfahrungen aus der Industrie sowie die digitale Infrastruktur, die für eine breite Skalierung von KI erforderlich ist. Oliver Süme, Vorstandsvorsitzender von eco, ordnet die zentralen Ergebnisse der Studie ein. Im Gespräch geht es unter anderem darum, weshalb gerade diese KI-Spezialisierung im Industriebereich für den Standort Deutschland relevant ist und welche politischen Rahmenbedingungen Unternehmen für Investitionen und Skalierung benötigen. Dr. Sicco Lehmann-Brauns von Siemens AG gibt Einblicke in die industrielle Praxis. Er spricht darüber, wie KI in der Industrieautomatisierung und in Endprodukten eingesetzt werden kann, welche Anforderungen sich an Datenqualität und Datenaufbereitung stellen und wie bestehende Modelle an konkrete industrielle Aufgaben angepasst werden. Michael Hanisch von AWS betrachtet Industrial AI aus der Infrastrukturperspektive. Im Gespräch geht es um das Zusammenspiel von Cloud, Edge Computing, Rechenzentren, Netzwerken und Datenplattformen sowie um die unterschiedlichen Anforderungen etablierter Industrieunternehmen und junger KI-Unternehmen. Auch Multicloud-Strategien und Skalierbarkeit spielen eine zentrale Rolle. Weitere Informationen: IW-Studie von eco “Der Deutschland Case: Wie KI das industrielle Geschäftsmodell erneuert”: https://www.eco.de/der-deutschland-case-wie-ki-das-industrielle-geschaeftsmodell-erneuert/ eco zu KI-Transparenzpflichten: https://www.eco.de/presse/nur-13-tage-bis-zur-anwendung-eco-warnt-vor-regulatorischem-blindflug-bei-ki-transparenzpflichten/ eco zum französischen Social-Media-Verbot: https://www.eco.de/presse/eco-kritisiert-frankreichs-social-media-verbot-europaeische-loesungen-statt-nationalem-flickenteppich/ eco Allianz zum Netzanschlusspaket: https://digitale-infrastrukturen.net/2026/07/22/eco-allianz-fuer-rechenzentren-fordert-nachbesserungen-des-netzanschlusspakets-geplante-regelungen-duerfen-rechenzentrumsausbau-nicht-behindern/ --------- Moderation: Sidonie Krug, Sven Oswald Schnitt: David Grassinger Redaktion: Christin Müller, Irmeline Uhlmann, Anja Wittenburg, Erik Jödicke Produktion: eco – Verband der Internetwirtschaft e.V.

AI becomes valuable when it helps people do their real work, not when it simply becomes another tool to manage. In this episode of Grounding AI, Donna Peterson welcomes Aya Takase, Head of Global Marketing Communications at Rigaku and Vice President of AI Operations, for a practical conversation about helping employees adopt AI in ways that improve communication, decision making, and everyday work. Instead of discussing the latest AI trends, Donna and Aya focus on real business challenges: How busy professionals can start using AI today Why marketers are often the best people to lead AI adoption How to review AI-generated content before publishing it Why companies don't need dozens of AI tools How internal AI champions can help entire organizations learn faster Why understanding your business is more important than understanding AI Whether you work in manufacturing, industrial marketing, an association, or another B2B industry, you'll leave with practical ideas you can begin using immediately. At World Innovators, we believe AI should strengthen communication, support business strategy, and help companies build stronger relationships with their audiences. This conversation demonstrates exactly how thoughtful AI adoption can support those goals. Subscribe for weekly conversations about practical AI for business leaders. *** Reach out to dpeterson@worldinnovators.com if you'd like help building a marketing strategy that builds relationships and/or AI training for individuals or full teams.*** Visit www.worldinnovators.com for more resources on building stronger marketing and leadership strategies.*** Subscribe to the Grounding AI podcast for weekly insights into marketing, leadership, and the future of AI.

IMTS is 1.2 million square feet spread across six days, and the average visitor only spends about 2.7 of them on the floor. That math is exactly why walking in without a plan means seeing a fraction of what you came for. In this episode we sit down with Bonnie Gurney and Michelle Edmondson from AMT, the team that builds IMTS, to talk through how to actually get value out of the biggest manufacturing show in the country. We're about six and a half weeks out and registration is running 29% ahead of 2024, so the energy is real. Bonnie and Michelle break down what's new for the 100th anniversary show: the 10 technology sectors, the Industrial AI arena with 40 first-time exhibitors, a bigger defense presence, and a conference lineup, from Elevate to the job shop and LATAM tracks, that too many people still treat as a hidden gem. We also make the case for why you should go even if you're not buying this year. As Mike puts it, you don't have time not to go. You go to see the solutions you'll need in two or three years, the way he spotted a machine tending robot in 2018 and finally bought it in 2022. And this year, a plan-my-route feature built into the floor plan app is a genuinely practical use of AI. There's plenty happening on the floor too, from the main stage schedule and the 100-year history of the show to the machine tool we're giving away with DN Solutions and Kennametal. Mostly, though, this is a conversation about showing up prepared and getting the most out of your days in Chicago. If you're heading to IMTS 2026, this is the planning conversation to hear first. Come find us in the Hennig booth, and let's make some chips. What's Covered in this Episode (0:00) Why every visitor needs a game plan before the doors open (1:21) Six and a half weeks out, with registration up 29% over 2024 (3:25) The scale of IMTS: 1.2 million square feet and 500-plus semis a day (7:56) The August 5th pre-IMTS party for Oscar Mike and the Manufacturing Pathways Consortium (10:04) Elevate: powered by AMT and Women in Manufacturing (10:56) What's new: the 10 technology sectors that map the show floor (13:00) Why the conference and education lineup is the show's hidden gem (17:58) Learn more about Elevate, the Women in manufacturing conference (20:45) Kennametal's Next Level Shop and a bike giveaway on the show floor (21:47) 100 years of IMTS, from a 62,000 square foot science fair in 1927 (27:06) The main stage schedule: capital decisions, AI, and the coolest stuff we saw (28:31) How the machine giveaway works: register, collect chips, earn entries, win Friday (31:09) Find your next leader with Hire MFG Leaders (31:38) Why "you don't have time not to go" is the real case for IMTS (35:47) A practical AI use case: let the floor plan app build your route (37:48) Poll your team: What should you look for at IMTS? (39:17) The biggest changes coming to IMTS 2026 Resources Mentioned IMTS 2026 Register for the IMTS machine giveaway: https://www.makingchips.com Hire MFG Leaders Elevate, powered by AMT and Women in Manufacturing DN Solutions Oscar Mike Foundation Manufacturing Pathways Consortium Connect with Bonnie Gurney & Michelle Edmondson IMTS AMT – The Association For Manufacturing Technology IMTS on LinkedIn Connect with MakingChips Website On Facebook On LinkedIn On Instagram On Twitter On YouTube

Travis Kalanick, Ben Horowitz, and Erik Torenberg reunite to reflect on Uber's early days, the investment that almost happened, and why Kalanick believes the next great technology opportunity lies beyond software. They discuss the lessons of building Uber, the value of founder-led companies, and why Kalanick spent nearly eight years quietly building Atoms before stepping back into the spotlight. The conversation explores industrial AI, robotics, autonomy, mining, food production, and Kalanick's vision for digitizing the physical world, where software, manufacturing, real estate, and transportation come together to transform entire industries.   Resources: Follow Travis Kalanick on X: https://x.com/travisk Follow Ben Horowitz on X: https://x.com/bhorowitz Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, we dive deep into the world of autonomous AI for data science with Doudou BA, CEO and founder of Octopus. I explore how Octopus is transforming raw data into actionable business decisions using advanced time series foundation models and agentic AI. We discuss the unique reasoning capabilities of Octopus, its intuitive approach for non-technical users, and how it stands apart from traditional AutoML solutions. Along the way, I share insights from VivaTech in Paris, including thought-provoking moments with industry leaders like Yann LeCun and Jeff Bezos. Join us as we reimagine the data science workflow and uncover what's next for AI-powered business intelligence.

In this episode, I sit down with Scott Duncan, a chemical engineer who built an agentic manufacturing troubleshooting assistant – without any coding background. We dive into the real-world challenges of process manufacturing and how AI is transforming the way plant issues are solved. Scott shares his journey from hands-on troubleshooting to leveraging large language models, knowledge graphs, and real-time data for smarter, faster problem-solving. We also debate the future of domain expertise in an AI-driven world and what it means for education and workforce development. Join me as we explore the potential – and the limitations – of AI in reshaping the future of industrial operations.

How will artificial intelligence impact jobs, workforce development, and the future of American manufacturing? In this episode of the Optimistic Outlook, Siemens USA CEO Ann Fairchild sits down with U.S. Senator Ted Budd of North Carolina to discuss the future of work in the age of AI. Building on insights from a recent U.S. Senate hearing focused on artificial intelligence and workforce transformation, they explore how AI is reshaping industries, creating new opportunities for workers, and driving innovation across the U.S. economy. Senator Budd shares why concerns about widespread job displacement are increasingly being replaced by conversations about productivity, workforce augmentation, and the growing demand for AI skills. Together, he and Ann examine the role of industrial AI, workforce training, public-private partnerships, and education in preparing Americans for the jobs of the future. The conversation also explores how AI can help strengthen U.S. manufacturing, accelerate reshoring efforts, improve competitiveness, and support responsible innovation. Rather than replacing people, they argue that AI has the potential to empower workers, enhance human capabilities, and unlock new economic opportunities. Whether you're interested in artificial intelligence, workforce development, manufacturing, economic policy, or the future of jobs, this episode offers an optimistic perspective on how technology can help build a stronger future for American industry and the people who power it. Topics discussed: Artificial intelligence and the future of work AI workforce development and job creation Industrial AI and manufacturing innovation Workforce training and AI skills Reshoring and strengthening U.S. manufacturing Responsible AI adoption Public-private partnerships and economic competitiveness 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/

AI is improving quickly. The way we use it should improve too. In this episode of Grounding AI, Donna Peterson shares a simple weekly system that helps business leaders continuously improve how they use ChatGPT and other large language models. Instead of chasing every new AI tool, Donna explains how spending just 10 minutes each week reviewing your conversations with AI can help you write better prompts, communicate more clearly, and receive more valuable business insights. Using her simple Review, Learn, Test framework, you'll discover how AI can become a teacher instead of simply another productivity tool. If you're a business leader, marketer, manufacturer, association executive, or B2B professional looking to improve your AI skills through real work instead of endless tutorials, this episode is for you. In this episode you'll learn: • Why reviewing your AI conversations is more valuable than starting over every day • How ChatGPT can teach you to become a better AI user • Questions to ask AI that improve future responses • Why better business thinking creates better AI results • How just 10 minutes each Friday can improve your AI skills week after week At World Innovators, we believe AI works best when it supports better communication, stronger relationships, and smarter business decisions. This episode demonstrates a practical process any leader can begin using immediately. Subscribe for weekly conversations about practical AI for business leaders. *** Reach out to dpeterson@worldinnovators.com if you'd like help building a marketing strategy that builds relationships and/or AI training for individuals or full teams.*** Visit www.worldinnovators.com for more resources on building stronger marketing and leadership strategies.*** Subscribe to the Grounding AI podcast for weekly insights into marketing, leadership, and the future of AI.

Most AI is built for people sitting at desks. Kriti Sharma builds it for the people who work in refineries, aircraft hangars, and utility networks responding to wildfires at 4 a.m. and she spends weekends on-site with them to make sure what she builds actually holds up. In this episode, Kriti joins Craig Smith to discuss what industrial AI really looks like when failure genuinely isn't an option, and why the gap between an impressive AI pilot and a production-grade AI system is so much wider in the physical world than most technology companies appreciate. The conversation is grounded in three specific products from Nexus Black, the elite AI unit Kriti leads inside IFS. The first is Resolve, a predictive maintenance platform built in close collaboration with William Grant's - the distillery behind Glenfiddich and Hendricks Gin - that is projected to save £8.4 million per year at a single factory by reading complex engineering schematics, identifying failure patterns before they occur, and giving frontline technicians step-by-step guidance on their phones without requiring them to remove a safety glove to type. The second is an airworthiness compliance tool for commercial airlines that automates a process currently consuming weeks of human engineering time, where a single mistake carries regulatory fines of up to $20 million and grounding a fleet costs $140 million per day. The third is a disaster response coordination system for utilities, built in partnership with Anthropic, designed to help field crews coordinate during wildfires, hurricanes, and grid outages in ways that, as a California disaster responder told Kriti directly after the most recent wildfire season, will get communities back online and hospitals lit up faster than ever before. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

We just returned from three inspiring days at AI in the Alps, and in this episode, we dive into the most exciting breakthroughs and honest challenges facing industrial AI today. I share my firsthand impressions of revolutionary tools like TiRex-2, and we unpack what agentic AI really means for engineering, from breaking down data silos to transforming anomaly detection. Alongside Stefan Suwelack from Renumics, we discuss why skepticism has given way to optimism, how data infrastructure is finally catching up, and what the future holds for AI-driven organizations. The conversation is loaded with real-world examples, candid reflections, and a look at the evolving balance between sovereignty, open source, and big tech. Whether you're an engineer, decision-maker, or just curious about the future of AI in industry, this episode offers practical insights and a glimpse into what's next.

Andres Naranjo sailed a race around the world, wrote Japan's digital agenda, and spent his sabbatical teaching himself AI from scratch.This week Chris sits down with Andres, CEO of Software Toolbox, to hear some incredible life stories and dig into what bringing AI into the industrial world looks like right now.Andres has lived a lot of lives as a consultant, entrepreneur, ocean racer and AI builder. He's pretty direct about why he walked away from consulting to pursue his current endeavors - the AI wave was moving far too fast to be on the side-lines of it.There's also practical conversation here for anyone thinking about how to deploy AI on the plant floor, and Andres' breakdown of how to effectively train an agent.In this episode, find out:Why Andres left one of the most prestigious consulting firms in the world to go build things himselfWhat sailing a race around the world taught him about data, analytics and machine learningWhat the industrial tech stack of the future actually looks like and why it's simpler than anyone is making it soundThe difference between machine learning, generative AI and agentic AI and why confusing them is holding the industry backWhy the era of the dashboard is over and what should replace itWhat an AI agent actually is, how you train one, and where on the plant floor you should be deploying it firstWhy Andres bought Software Toolbox instead of building from scratch and the simple framework behind that decisionWhy Andres moved back to Silicon Valley after Japan and what he saw happening in AI that made him think it was time to stop advising and start buildingEnjoying the show? Please leave us a review here. Even one sentence helps. It's feedback from Manufacturing All-Stars like you that keeps us going!Tweetable Quotes:“AI is a salad bowl of technologies and almost always people conflate one with the other in really strange ways.” - Andres Naranjo, CEO of Software Toolbox“ It is a wonderful time to build. It's probably the best time ever. I want it to be in it. I didn't want to be on the side-lines for it.” - Andres Naranjo, CEO of Software Toolbox“There are many ways to lean in, and it's okay if there is some failure. Everything is a normal distribution. You've got to blow up rockets to land rockets on the moon.” - Andres Naranjo, CEO of Software ToolboxLinks & mentions:Software Toolbox is a full-stack industrial AI company, delivering industrial connectivity software, data platforms, and AI agents that augment the people working in manufacturing and process industries.Allie integrates intelligence into factory operations: learning from machines, sensors and systems to detect problems, recommend actions and coordinate responses in real time.Make sure to visit http://manufacturinghappyhour.com for detailed show notes and a full list of resources mentioned in this episode. Stay Innovative, Stay Thirsty.

Timestamps:09:16 - How Emmi AI Reached an Exit in 18 Months18:01 - Why They Raised €15M After Becoming Profitable25:25 - Why Mistral Was the Right Buyer46:49 - Can Europe Win in Industrial AI?Episode description:Dennis Just co-founded Emmi AI and served as its CEO, building an industrial AI company focused on complex engineering problems. Around 18 months after launch, Emmi AI was acquired by Mistral AI, where Dennis now serves as VP Manufacturing Solutions. Before Emmi AI, he had already founded several companies, including Knip and Smallpdf, and was closely involved in the Swiss startup ecosystem.In this episode, Dennis explains how the company grew around research rather than a traditional sales setup. Publishing strong technical work brought major industrial companies to them, while the €15 million seed round gave the team room to keep investing in the technology without making every decision around runway. He also takes us behind the acquisition and shares why Emmi AI chose Mistral despite having other offers on the table.The conversation then zooms out to Europe's place in the AI race. Dennis explains why he sees industrial AI as one of the continent's strongest opportunities, what Europe still needs to get right, and why this deal feels more like the start of a new chapter than the end of the journey.The cover portrait was edited by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.smartportrait.io⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Don't forget to give us a follow on⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Youtube ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠so you can always stay up to date with our latest initiatives. That way, there's no excuse for missing out on live shows, weekly giveaways or founders' dinners.

In this episode, I sit down with Levente Zolyomi, a leading PhD researcher at NXAI, to unpack the next evolution in time series AI: TiRex-2. We explore how TiRex-2 builds on its predecessor by handling complex multivariate data and streaming scenarios, opening new frontiers for industrial forecasting. Levente shares the story behind TiRex-2's architecture, its breakthrough capabilities, and what sets it apart from transformer-based models like Kronos. I ask the questions you're thinking—about zero-shot forecasting, synthetic data, and real-world benchmarks—so you can understand what matters most for your business. If you're navigating industrial AI or just curious about the future of time series models, this conversation is packed with the insights you need.

El evento reunirá a líderes tecnológicos para debatir sobre eficiencia, resiliencia y competitividad en infraestructuras críticas. Tertulia con Julio César Pereira, Iberia Sales Director de DXC; Alejandro Pinal, Desarrollo de Negocio Industria y Defensa de Grupo Amper; Gustavo Sandoval, Head of Industrial AI, IoT & Smart X de Knowmad Mood; Gonzalo Valle: Presales Manager de IFS; y Diego Sanz, Head of Utilities & Energy de Vass Company.

Venture Capital tickt anders als Private Equity. Im PE zählt der Profithebel, in der Frühphase ist Pricing dagegen vor allem ein Wachstumshebel. Genau hier setzt Folge 131 an. Dr. Sebastian Voigt spricht mit Gülsah Wilke, Partnerin und Head of German Office bei DN Capital in Berlin, darüber, wie ein VC-Fonds das Pricing eines Startups bewertet, lange bevor das Geschäftsmodell ausgereift ist. Wilke kennt beide Seiten des Tisches, denn sie leitete vor rund zehn Jahren das Pricing-Team bei Axel Springer und übernahm es dort von Sebastian Voigt selbst. DN Capital investiert seit mehr als 25 Jahren sektoragnostisch und sucht den Einstieg am liebsten in der Series A, sobald ein Startup Product Market Fit und mindestens eine Million ARR zeigt. Bewertet werden dabei drei Größen gemeinsam, der Average Contract Value, das Kundenprofil mit der Frage nach Budget Owner und Nutzer sowie die Länge des Sales Cycle. Heikel wird es, wenn ein Startup seinen eigenen Wert massiv unter Preis verkauft. Wilke beschreibt einen Anbieter für automatisierte SAP-Implementierungen mit einem ACV von rund 30.000 Euro, weit unter dem, was Konzerne sonst für Berater und eigenes Personal zahlen. Für einen VC ist das ein Warnsignal, weil sich ein Preis nicht in ein bis zwei Jahren von 30.000 Euro auf eine halbe Million heben lässt und ein Fonds diese Zeit nicht hat. Den wahren Wert liest Wilke deshalb aus den Opportunity Costs ab. Wie sie es auf den Punkt bringt: „Wenn wir ein massives Pricing Gap oder Underpricing sehen, müssten wir was sagen." Über den Gast  Gülsah Wilke ist Partnerin und Head of German Office bei DN Capital in Berlin und betreut dort vor allem Investments in Healthcare, Manufacturing und Industrial AI. Ihre Pricing-Erfahrung stammt aus ihrer Zeit bei Axel Springer, wo sie das Pricing-Team leitete und den Bereich Job Classifieds bis hin zu einem Board-Mandat bei StepStone verantwortete. Anschließend wechselte sie als COO und Geschäftsführerin zu Ada Health und begleitete den Eintritt in den US-Markt. Vor sechs Jahren gründete sie 2hearts, Europas größtes Netzwerk und Angel-Investment-Collective für Tech-Talente mit mehreren kulturellen Wurzeln.

In this episode, we dive deep into how AI is revolutionizing the entire industrial customer journey. We explore how large language models and digital twins are reshaping everything from initial customer inquiries to spare parts management and predictive maintenance. I'm joined by Jan Seyler and Werner Reichelt from Festo, who share firsthand insights on integrating AI with engineering tools, orchestrating multi-agent systems, and bridging the gap between digital sales and real-world manufacturing. Together, we discuss the challenges of interoperability, the evolving role of sales engineers, and the opportunities presented by data-driven automation. If you're curious about the practical impact of AI on industry, this conversation is packed with real examples and forward-looking ideas.

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, 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

This episode was brought to you by Mouser, our go-to source for electronics parts for any hobby or prototype. Click HERE to learn the machine safety best practices that keep modern industrial automation running safely, reliably, and efficiently. Become a founding reader of our newsletter: http://read.thenextbyte.com/ As always, you can find these and other interesting & impactful engineering articles on Wevolver.com.

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.

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.

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/

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

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.