Trend Detection Podcast

Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – which gives you visibility and insights into all your assets, from single machines to full plants to help you reduce downtime, increase knowledge sharing and accelerate digital transformation across your organization.In this episode, we’re joined by Louis Mahlau, Product & Portfolio Manager - IoT & Analytics at Siemens, who explores how digital drivetrains are transforming the way industrial assets are monitored and maintained and how combining IoT, AI, and domain expertise is unlocking a new generation of predictive maintenance.What a digital drivetrain is and why it underpins so much of modern industrial operationsHow predictive maintenance shifts organizations from reactive and preventive approaches to truly predictive insights How sensors, connectivity, cloud computing, and digital twins come together to turn raw machine data into actionable intelligence A real-world example of how connecting a single motor enabled early detection of issues before production downtime occurred Where the true value lies beyond technology — including data quality, scalability, and change management Why many pilots fail to scale, and what successful organizations do differently from the start How industrial AI and copilots are making complex machine data easier to understand and act on What the future looks like — from prescriptive maintenance to autonomous, self-optimizing systemsYou can find out more about how Senseye Predictive Maintenance can reduce unplanned downtime and contribute towards improved sustainability within your manufacturing plants, by visiting: www.siemens.com/senseye-predictive-maintenance

Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – which gives you visibility and insights into all your assets, from single machines to full plants to help you reduce downtime, increase knowledge sharing and accelerate digital transformation across your organization.In this episode, we’re joined by Richard Ella, who takes a step back in time to show how some of the most powerful ideas in predictive maintenance aren’t new at all and why understanding their origins is key to explaining, adopting, and trusting AI today.What you’ll learn in this episode:Why modern AI‑driven predictive maintenance follows the same principles as earlier mechanical and electrical innovationsHow the strobe light was originally invented for maintenance and what it teaches us about “seeing” machines differentlyA simple, practical way to explain AI and Senseye without buzzwords or hypeHow AI mirrors the instincts of experienced plant operators by detecting subtle changes before failureWhy curiosity, trust, and change management matter more than the technology itselfHow early warnings become real value only when teams act on themYou can find out more about how Senseye Predictive Maintenance can reduce unplanned downtime and contribute towards improved sustainability within your manufacturing plants, by visiting: www.siemens.com/senseye-predictive-maintenance

Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – which gives you visibility and insights into all your assets, from single machines to full plants to help you reduce downtime, increase knowledge sharing and accelerate digital transformation across your organization.In this episode, we're joined by Tom Jacques, a Solutions Engineer for Senseye at Siemens, to break down what predictive maintenance looks like in the real world, from kickoff to daily use and scale.What we cover:What actually happens during the first 30–60 days of a predictive maintenance projectHow proper scoping, asset selection, and data availability set projects up for successWhere projects commonly slow down or stall, including resource constraints and misaligned expectationsHow pilots transition into day‑to‑day operational useWhat creates real “aha moments” for maintenance teamsWhy trust is the key factor in getting teams to act on insightsHow Senseye Copilot supports decision‑making without replacing human judgementWhat separates pilots that scale successfully from those that remain stuck in PoVsYou can find out more about how Senseye Predictive Maintenance can reduce unplanned downtime and contribute towards improved sustainability within your manufacturing plants, by visiting: www.siemens.com/senseye-predictive-maintenanceConnect with Tom on LinkedIn here:https://www.linkedin.com/in/thomas-jacques-22655585/

Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – which gives you visibility and insights into all your assets, from single machines to full plants to help you reduce downtime, increase knowledge sharing and accelerate digital transformation across your organization.In this episode, we’re joined by Natalie Kurgan, Head of Delivery for Senseye across the Americas at Siemens, who shares a delivery‑side view of when predictive maintenance is (and isn’t) the right fit—and what plants need in place before they start:Why predictive maintenance is a strategy, not a tool, and why success depends on people + process, not software alone.The readiness checklist that’s often missing: leadership support, a clear workflow, and a technically minded champion who drives action.Where projects go wrong in practice—from weak ownership to poor asset selection and low/limited data quality.What PdM can realistically deliver (planning spares, reducing unnecessary planned work, avoiding risky unplanned failures) vs. what it can’t.How AI copilots help and their limits: they need context and feedback; they don’t replace human judgement.If you’re not ready yet, how to get there: define KPIs, audit maintenance logs, identify problem assets, then assess what data/sensing you actually have.You can find out more about how Senseye Predictive Maintenance can reduce unplanned downtime and contribute towards improved sustainability within your manufacturing plants, by visiting: www.siemens.com/senseye-predictive-maintenance

Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – which gives you visibility and insights into all your assets, from single machines to full plants to help you reduce downtime, increase knowledge sharing and accelerate digital transformation across your organization.In this episode of the Trend Detection podcast, we’re joined by Christian Zillner, who leads global AI deployment for Digital Industries Automation at Siemens, to explore what it really takes to scale industrial AI from experiments to real shop‑floor impact.Drawing on hands‑on experience across industries, Christian shares practical lessons on what works, what doesn’t, and why many AI initiatives struggle to move beyond pilots, including:What industrial AI deployment really means—going beyond algorithms to include business cases, ownership, services, and organisational changeWhy many AI pilots fail to scale, from unrealistic expectations to non‑serviceable, custom architecturesThe human side of IT/OT convergence, and how unclear roles and ownership can derail progressHow to choose between cloud, edge, or hybrid AI based on latency, security, cost, and operational constraintsThe role of partners and ecosystems in taking AI from the lab to productionWhere AI delivers real value today—and where expectations still need groundingWhy standardising the deployment platform early is critical to long‑term scalabilityPractical advice for moving from experimentation to production with a small set of repeatable, high‑value use casesA refreshingly realistic discussion on industrial AI for anyone responsible for digitalisation, automation, or AI strategy in manufacturing.You can find out more about how Senseye Predictive Maintenance can reduce unplanned downtime and contribute towards improved sustainability within your manufacturing plants, by visiting: www.siemens.com/senseye-predictive-maintenanceConnect with Christian on LinkedInhttps://www.linkedin.com/in/christian-zillner/