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 the closing episode of our 4-part series, we sat down with Peter O'Hara, a Customer Success Manager for Senseye who's spent close to a decade helping manufacturers actually adopt and scale predictive maintenance. We cover: → Why asset selection makes or breaks the first 90 days after go-live → What separates a real internal champion from someone who just has the time → How to turn resistors into advocates, often by solving the problem they actually have→ The KPIs that drive renewal and expansion (hint: downtime avoided is only part of it)→ Why Senseye at scale is beautifulIf you've missed Parts 1–3 with Richard Jeffers, Nat Ford, and Pontus Noren, now's the time to binge the full series:Listen to episode one: AI Is Ready. Are We? - with Richard Jeffers here.Listen to episode two: Why Change Management Makes or Breaks PdM — with Nat Ford hereListen to episode three: What Industrial AI Projects Get Wrong About Adoption — with Pontus Noren | Adoption Gap Part 3 hereYou 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 the third episode of our 4-part series, I sat down with Pontus Noren, founder of Ensemble AI, to unpack the patterns he's seen across industries —and why the same mistakes keep showing up in different factories.His perspective is especially valuable because it's from outside the Siemens world. The adoption gap isn't a PdM problem. It's an industrial AI problem.We cover: → The most common patterns behind failed industrial AI projects → Why "proof of concept" culture is one of the biggest blockers to scale → How misaligned KPIs create a gap between what AI delivers and what the business measures → What trust actually looks like in practice and how to build it incrementallyIf you missed Parts 1 and 2 you can listen below:Listen to episode one: AI Is Ready. Are We? - with Richard Jeffers here.Listen to episode two: Why Change Management Makes or Breaks PdM — with Nat Ford hereYou 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 the second episode of our 4-part "The Adoption Gap" series, we sat down with Nat Ford from Siemens to unpack why change management isn't a workstream you bolt on at the end of a PdM project. It's the project itself.His take? If you need formal "change management" by the time you go live, something has already gone wrong.We cover: Why change management is often a sign the project wasn't designed properly in the first placeThe difference between managing change and designing change in from day oneHow the Hawthorne Effect applies to PdM adoption — and why paying attention to people matters more than any frameworkWhy predictive maintenance changes how people decide, not just what they doPrevious episodes in the seriesListen to episode one, AI Is Ready. Are We? - with Richard Jeffers here.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 the opening episode of The Adoption Gap, we're joined by independent asset management consultant Richard Jeffers to explore a simple but uncomfortable question:If predictive maintenance technology has been around for decades, why are so many organisations still struggling to make it work?Richard argues that the gap isn't in the algorithms, it's in the culture, data maturity, and leadership required to turn capability into results.What you'll learn in this episode:Why predictive maintenance is a way of life, not a technology deployment and what that distinction means in practiceWhere the real gap sits between what AI can do today and what most organisations are actually ready to do with itWhy being "data-led" requires far more than having data and what data maturity really looks like on the shop floorWhat separates the manufacturers who scale predictive maintenance from those who never get past the pilotHow leadership commitment and clear ownership determine whether PdM becomes embedded or abandonedYou 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 Richard on LinkedIn:https://www.linkedin.com/in/richard-jeffers-2860a08/Listen to episode 2, Why Change Management Makes or Breaks PdM — with Nat Ford here

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 special four-part series, we tackle the uncomfortable truth behind most predictive maintenance programmes: the technology works, it's the adoption that doesn't. Across four episodes, we hear from four experts, each with a different perspective on why industrial AI projects stall after the pilot, and what the organisations that succeed actually do differently.What you'll learn in this series:Why organisational readiness (not algorithm accuracy) is the real bottleneck for predictive maintenance, and what "being ready" actually looks like in practiceHow change management should be designed into a project from day one, not bolted on when adoption starts to stallThe most common patterns behind failed industrial AI projects, from pilot traps and misaligned KPIs to trust gaps that never get closedWhy asset selection and internal champions make or break the first 90 days after go-live, and what happens when either one is wrongHow to build momentum from early wins, turning resistors into advocates and scaling from one line to an entire plantThe real KPIs that drive renewal and expansion and why downtime avoided is only part of the pictureYou 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