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 part one of this episode, you will learn about:

• How AI is transforming predictive maintenance: Learn how AI-driven solutions like Senseye make predictive maintenance scalable—from thousands of machines at enterprise level down to individual components—without the need for complex modeling or manual data input.

• Making PdM accessible for all business sizes: Discover how small to mid-sized enterprises (like those in New Zealand) can start with as few as 50 machines and still achieve strong ROI within 3–6 months.

• From data to decisions—without new sensors: Understand how existing automation data (like current, torque, or timing from PLCs) can be used for condition monitoring, often without needing to install new instrumentation.

• Preserving institutional knowledge with AI: Hear how the system ingests everything from manuals to repair photos and videos, helping capture and share expert knowledge across sites to support the next generation of maintainers.

• A practical, people-first approach to AI: Explore how Senseye’s philosophy prioritizes augmenting human roles, integrating with current systems, and delivering real-world value—not replacing people, but empowering them with better tools.

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