EPISODE · Jul 1, 2026 · 31 MIN
What Industrial AI Projects Get Wrong About Adoption — Pontus Noren | Adoption Gap Part 3
from Trend Detection Podcast · host Siemens.FM team
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
What this episode covers
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
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What Industrial AI Projects Get Wrong About Adoption — Pontus Noren | Adoption Gap Part 3
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