What Industrial AI Projects Get Wrong About Adoption — Pontus Noren | Adoption Gap Part 3

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