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 this episode of the Trend Detection Podcast, Niall Sullivan is joined by Chris Smith, Head of Operations for Senseye Predictive Maintenance, to explore the relationship between predictive maintenance and computerized maintenance management systems, or CMMS.

Chris explains why organizations should consider establishing value and learning how predictive maintenance fits within their operations before fully integrating it into existing workflows. He also discusses how CMMS data can provide valuable context, reduce manual investigation and help maintenance teams focus their attention on the issues that require action.

In this episode, you’ll learn:

• When to integrate predictive maintenance with your CMMS

• How CMMS data adds context and accelerates decision-making

• Why trust and ownership matter more than technology alone

• How to turn predictive insights into maintenance action

• Why standardised integrations still require local flexibility

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 this episode of the Trend Detection podcast, we speak with Todd Martin from Siemens.

Drawing on experience as both a long-term Senseye user and managed services specialist, Todd explains how expert support can help maintenance teams turn predictive insights into action, reduce administration and build trust in the approach.

Listen to discover:

• What predictive maintenance managed services look like in practice

• How expert analysis and customer maintenance teams work together

• Why technology alone is not enough to deliver results

• The role of trust, feedback and internal champions

• Why an open mind remains one of the most valuable maintenance skills

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 latest episode of the Trend Detection Podcast, we speak with Michael Schrapp, Director, Global Go-to-Market for Data & AI at Siemens, about the Eigen Engineering Agent and the connection between automation engineering, reliability and maintenance.

In the episode, we explore:

🔹 Why industrial AI needs domain and project context

🔹 How integration with TIA Portal reduces manual translation and context loss

🔹 How validated engineering can support reliability and maintainability

🔹 The changing roles of skills, governance and accountability

🔹 Why the engineer remains central to review and approval

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 latest episode of the Trend Detection podcast, we're joined by Johnathan Bonner, Head of Solutions Engineering for Senseye, to explore why connectivity is often the most underestimated part of a predictive maintenance deployment.

In this episode We discuss:

• Why industrial connectivity is rarely as simple as “plug and play”

• How different machines, systems and communication protocols create complexity

• Why IT, OT and cybersecurity teams need to be involved from the outset

• How connectivity and integration can account for much of the implementation effort

• Why trying to reduce upfront costs can ultimately delay time to value

• How mapping your infrastructure and data sources can support predictive maintenance and future digitalization projects

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 this live episode of Trend Detection, Belen Brunet from Coca-Cola Europacific Partners joins Mark Hindsbo, Head of Operations Software at Siemens, to explore how manufacturers can turn operational complexity into opportunity.

They discuss connecting IT and OT, combining trusted industrial data with AI, and using Senseye Predictive Maintenance to improve asset utilization, support maintenance teams and scale smarter operations.

In this episode you will learn:

• How to turn operational complexity into a competitive advantage

• Why trusted industrial data is the foundation for effective AI

• How digital twins can help manufacturers design and optimize entire factories

• How Senseye helps teams fix the right equipment at the right time

• What it takes to scale predictive maintenance from production to logistics

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 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 beautiful

If 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 here

Listen to episode three: What Industrial AI Projects Get Wrong About Adoption — with Pontus Noren | Adoption Gap Part 3 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 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 incrementally

If 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 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 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 place

• The difference between managing change and designing change in from day one

• How the Hawthorne Effect applies to PdM adoption — and why paying attention to people matters more than any framework

• Why predictive maintenance changes how people decide, not just what they do

Previous episodes in the series

Listen 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 practice

• Where the real gap sits between what AI can do today and what most organisations are actually ready to do with it

• Why being "data-led" requires far more than having data and what data maturity really looks like on the shop floor

• What separates the manufacturers who scale predictive maintenance from those who never get past the pilot

• How leadership commitment and clear ownership determine whether PdM becomes embedded or abandoned

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

Connect 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 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