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

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, we’re joined by Nick Leeder to explore what sustainability really means for mid-sized manufacturers — and how moving beyond strategy into practical, measurable action can unlock both operational efficiency and improved profitability.

What you’ll learn in this episode:

• Why sustainability must be treated as a core business initiative — with clear ownership, budget, and measurable outcomes, not just a strategy document

• How external pressures (regulation, supply chain requirements) and internal drivers (cost, competitiveness) are accelerating adoption for mid-sized manufacturers

• Practical starting points — using existing data like energy bills and focusing on simple, high-impact improvements such as reducing idle energy consumption

• How to build a business case by directly linking sustainability initiatives to financial outcomes, from cost savings to risk reduction

• The biggest pitfalls to avoid — lack of governance, too many initiatives, and failure to embed sustainability into daily operations and decision-making

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, we’re joined by Peter Schopf to unpack the latest industrial and AI trends coming out of Hannover Messe 26 — from the rise of humanoid robotics to the growing divide between operational and strategic AI, and why many organizations are still missing where the real value lies.

What you’ll learn in this episode:

• What stood out at Hannover Messe — including the rise of humanoid (embodied) AI and the shift from hype to more tangible industrial use cases

• Why IT/OT integration and industrial data access remain persistent challenges, despite years of investment and innovation

• Practical generative AI use cases on the shop floor — from documentation and troubleshooting to AI-powered assistants for operators

• Why most organizations focus too heavily on operational AI — and are missing the much bigger opportunity at the strategic decision-making level

• How “strategic prompting” and better AI interaction can dramatically accelerate complex business decisions, and why context and experience matter more than technical skill

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’re joined by Emily Trott from BlueScope Steel to unpack a real-world predictive maintenance success case, where a single vibration sensor helped prevent a critical failure before it happened.

It’s a practical, end-to-end story of how AI, engineering expertise, and process come together to move from early signal to real intervention — and how that translates into avoided downtime and operational impact.

What you’ll learn in this episode:

• How a small vibration signal led to the discovery of a hidden failure on a connected asset

• Why predictive maintenance is not one alert → one fix, but a multi-step investigation involving both AI and human expertise

• The gap between traditional monitoring and predictive maintenance and why most failures are only detected when it’s already too late

• How combining Senseye insights with on-site expertise changes the outcome from reactive to controlled intervention

• Why sharing success cases internally is key to driving adoption, scaling, and new use cases across sites

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

To find out more about Bluescope Steel's approach to asset intelligence, please watch the video below:

https://www.youtube.com/watch?v=0dnDST5B1V4

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, we're joined by three Senseye deliver experts to share real-world lessons from Senseye deployments.

They will discuss what actually works, what doesn’t, and what separates successful projects from the rest.

What you’ll learn in this episode:

• Why choosing the right assets early is critical to proving value and building momentum

• How data quality and context (not volume) determine success in predictive maintenance

• Why early wins are essential to drive trust, adoption, and scaling across teams

• The common pitfalls in implementations, from wrong failure assumptions to poor asset selection

• How successful deployments depend on combining AI with real-world expertise and customer ownership

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, we’re joined by experts from Siemens live from Hannover Messe 26 to explore how Arca Continental, one of the world’s largest Coca-Cola bottlers, is driving a data-led manufacturing transformation across its global operations, and how combining transparency, predictive maintenance, and a strong digital foundation is unlocking measurable operational impact at scale.

What you’ll learn in this episode:

• How Arca Continental is scaling digital transformation across 45 sites by starting with transparency (OEE, cost, loss drivers) before moving to advanced use cases

• Why predictive maintenance success depends on strong cultural alignment and early digital maturity — not just technology

• A real-world example delivering impact in days — saving 13 hours of downtime on a critical production asset

• How Insights Hub provides a unified data foundation to connect use cases, systems, and workflows at scale

• What’s next: expanding with AI, contextualized data, and prescriptive insights to drive continuous operational improvement

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, we’re joined by Louis Mahlau, Product & Portfolio Manager - IoT & Analytics at Siemens, who explores how digital drivetrains are transforming the way industrial assets are monitored and maintained and how combining IoT, AI, and domain expertise is unlocking a new generation of predictive maintenance.

• What a digital drivetrain is and why it underpins so much of modern industrial operations

• How predictive maintenance shifts organizations from reactive and preventive approaches to truly predictive insights

• How sensors, connectivity, cloud computing, and digital twins come together to turn raw machine data into actionable intelligence

• A real-world example of how connecting a single motor enabled early detection of issues before production downtime occurred Where the true value lies beyond technology — including data quality, scalability, and change management

• Why many pilots fail to scale, and what successful organizations do differently from the start

• How industrial AI and copilots are making complex machine data easier to understand and act on

• What the future looks like — from prescriptive maintenance to autonomous, self-optimizing systems

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