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 practice

• How change management should be designed into a project from day one, not bolted on when adoption starts to stall

• The most common patterns behind failed industrial AI projects, from pilot traps and misaligned KPIs to trust gaps that never get closed

• Why asset selection and internal champions make or break the first 90 days after go-live, and what happens when either one is wrong

• How to build momentum from early wins, turning resistors into advocates and scaling from one line to an entire plant

• The real KPIs that drive renewal and expansion and why downtime avoided is only part of the picture

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