Last week I attended the Reliability & Asset Performance Management workshop at the IBM Innovation Studio in Amsterdam. What started as a technical workshop around Reliability Centered Maintenance (RCM), Maximo Health and Maximo Monitor quickly turned into something much more interesting.

For years, many organizations have used Maximo primarily as an Enterprise Asset Management platform focused on work execution, preventive maintenance and operational control. But during this workshop, it became increasingly clear that Maximo Application Suite is evolving into something broader: a connected Asset Lifecycle Management platform where reliability engineering, condition monitoring, health scoring and AI-driven insights are starting to work together as one continuous process.

One thing especially stood out to me:

Without reliability context, sensor data remains just data.

That may sound obvious, but it perfectly captures the challenge many organizations are facing today. More sensors, more dashboards and more IoT data do not automatically lead to better maintenance decisions. The missing layer is often reliability strategy itself.

And that is exactly where RCM becomes relevant again.

RCM Is No Longer an Excel Exercise

For a long time, RCM and FMECA analyses often lived outside operational systems. They were typically created during projects, stored in spreadsheets or documents, and slowly became disconnected from daily maintenance execution.

What I found interesting during this workshop was how IBM is positioning Reliability Strategies much more as an operational capability inside MAS itself.

Failure modes, criticality, mitigation strategies, condition monitoring points and maintenance strategies are no longer isolated engineering exercises. Instead, they are becoming part of a connected digital thread that links reliability engineering directly to operational maintenance and monitoring.

The workshop repeatedly emphasized a simple but powerful concept:

Know your assets. 

Anyone who has worked with my fellow IBM Champion @Jan-Willem Steur has probably heard that phrase before, but the more we discussed reliability, condition monitoring and failure context during the workshop, the more relevant it became.

Not just from a hierarchy perspective, but truly understanding:

• how assets fail,
• what influences those failures,
• which indicators matter,
• and which maintenance strategy actually fits the operational context.

That shift matters because condition-based maintenance only becomes valuable when it is linked to known failure mechanisms. Otherwise, organizations risk creating dashboards full of disconnected signals without understanding what actually requires intervention.

The Digital Thread Inside MAS

One of the strongest workshop discussions revolved around what IBM referred to as the “digital thread” across Maximo Application Suite.

What became visible is that MAS is no longer positioning:

• Manage,
• Monitor,
• Health,
• Predict,
• and Reliability Strategies

as separate products.

Instead, they are increasingly presented as connected operational capabilities.

The flow discussed during the workshop looked something like this:

Failure analysis → maintenance strategy → sensors & meters → monitoring → health scoring → alerts → work execution → feedback into reliability improvement.

That closed-loop approach is where things become truly interesting.

For years, many organizations already had:

• work orders,
• preventive maintenance,
• inspections,
• and operational history.

But now the missing layer is being added:
real-time operational awareness connected directly to reliability context.

That combination has the potential to fundamentally change how maintenance organizations operate.

Maximo Monitor Finally Feels Operational

One of my biggest takeaways from the workshop was the evolution of Maximo Monitor itself.

Historically, Monitor sometimes felt like a separate IoT platform sitting next to Maximo. Integrations often required synchronization layers and additional architecture to connect operational data back into maintenance processes.

What feels different now is that Monitor is becoming far more integrated into the operational maintenance flow.

The direct relationship between:

• devices,
• metrics,
• asset meters,
• condition monitoring,
• Health,
• and eventually work execution

creates a much more natural experience.

The workshop demonstrated how sensor data can now flow into asset meters and become directly visible within Health scoring and operational dashboards.

That may sound like a technical detail, but operationally it is a major step forward.

It means organizations can move closer to a true condition-based maintenance workflow where:

• anomalies are detected,
• asset health deteriorates,
• alerts are generated,
• and maintenance actions can follow in a structured and traceable process.

Not because a dashboard says “something changed,” but because the system understands the reliability context behind that change.

The Reliability Engineer Is Becoming a Digital Role

Another interesting observation was how the role of the reliability engineer is evolving.

Traditionally, reliability engineering has often been positioned somewhat separately from operational systems. But in this approach, the reliability engineer becomes central to defining:

• asset criticality,
• failure modes,
• scoring logic,
• monitoring strategy,
• and the operational meaning behind asset data.

In other words:
the reliability engineer is becoming a steward of the digital asset model.

That is an important shift.

Because ultimately, AI, anomaly detection and condition monitoring are only as valuable as the operational context behind them.

The quality of the maintenance strategy still determines the quality of the outcome.

AI Is Starting to Become Practical

Of course, AI was also part of the discussion.

But what I appreciated during this workshop was that the conversation stayed relatively practical.

The focus was not on replacing maintenance teams with AI. Instead, the focus was on improving operational decision-making through:

• anomaly detection,
• alert insights,
• condition awareness,
• guided diagnostics,
• and eventually AI-assisted maintenance workflows.

That feels like the right direction.

Especially in industries facing:

• aging assets,
• workforce shortages,
• increasing safety requirements,
• and growing operational complexity.

AI becomes valuable when it helps maintenance and reliability teams make better decisions faster — not when it simply generates more information.

Final Thoughts

What made this workshop interesting to me was not just the technology itself, but the bigger shift behind it.

For years, many organizations have focused heavily on either:

• maintenance execution,
  or
• sensor data collection.

But the real value seems to emerge when reliability strategy becomes the connecting layer between those worlds.

That is where RCM, Health, Monitor, Predict and AI start to reinforce each other.

And perhaps that is the most important takeaway from this workshop:

The future of Asset Performance Management is not about adding more dashboards.

It is about creating operational understanding.

I’m curious how others in the Maximo community see this evolution. Are we finally reaching the point where reliability engineering, operational maintenance and AI-driven insights truly converge into one connected workflow?