The Predictive Maintenance Illusion: What Operational and Financial Leaders Should Know Before Investing

A recent report from the team at S&P on the Financial Impact Analysis: Predictive maintenance prompted me to roll up my sleeves and dig a little deeper beyond what appears to be the compelling business case they present, yet what I see every day as predictive maintenance challenges in manufacturing….

For years, Predictive Maintenance (PdM) has been sold as the holy grail of industrial efficiency. The promise? A factory that never stops unexpectedly, where machines predict their own failures, and maintenance is done only when needed.

The numbers are compelling:

If you’re a COO, it sounds like a dream—higher throughput, better asset utilisation, and fewer surprises on the production floor.

For a CFO, it appears to be an easy financial decision—less waste, lower maintenance costs, and better ROI on capital equipment.

So why do so many PdM projects fail to deliver these results? Because the reality is far more complex than the glossy case studies suggest.

The Mistake of Monitoring Everything

Imagine you’re handed a blank cheque to implement PdM across your entire operation. Every machine, from the massive production line to the smallest pump, is fitted with IoT sensors, AI-driven analytics, and real-time dashboards.

Now, fast forward a year.

Have you actually improved anything?

The truth is, not every machine needs or benefits from PdM. It makes sense for high-value, critical assets where failure means significant downtime or financial loss. But applying it to non-essential machines is a waste of time and money.

Yet, many PdM strategies don’t start with a clear asset prioritisation plan. They follow a “more sensors, more data”approach, assuming the insights will justify the investment. They rarely do.

>> COO’s concern: Are we optimising PdM where it matters most, or spreading investment too thin?

AI Alone Won’t Save You – PdM Needs Human Expertise

In every PdM presentation, you’ll hear about AI, machine learning, and cloud-based analytics. What you won’t hear about?

The people who actually make PdM work.

• AI can detect anomalies, but it doesn’t know what’s important.
• A vibration spike on a motor could mean failure—or just normal operation.
• Without experienced analysts, you get false positives that waste time or false negatives that lead to catastrophic failures.

The missing link in most PdM strategies? Vibration analysts, condition monitoring specialists, and asset reliability engineers.

You can spend millions on AI, but if you don’t have the right people interpreting the data, PdM insights will be ignored.

If Your Teams Can’t Act, PdM Won’t Deliver ROI

Let’s say your PdM system works perfectly. It detects a potential failure in a critical machine and alerts the team in time to prevent a breakdown.

What happens next?

Can maintenance act fast enough?

• Many factories run lean maintenance teams, already stretched between preventive schedules and reactive breakdowns.
• If teams don’t have the capacity to act, PdM becomes useless.
• The same applies to spare parts and scheduling—predicting failures doesn’t help if you can’t get the right part in time.

This is where many PdM strategies fail to consider the operational reality. Predictive insights are only valuable if they lead to faster action.

The Hidden Challenge: Changing a Maintenance Culture

PdM is often framed as a technology shift, but in reality, it’s a cultural shift.

• Many factories still run on reactive or time-based maintenance models.
• Operators and technicians often trust their experience more than algorithms.
• If maintenance teams aren’t fully bought in, PdM becomes just another dashboard that no one looks at.

Shifting from reactive to predictive maintenance isn’t just a technical project—it requires:

• ✅ Strong leadership
• ✅ Change management
• ✅ A clear communication strategy

Without these, PdM adoption will stall, no matter how advanced the technology.

Choosing the Right PdM Partner: The Big Vendors Aren’t Always the Best

Most PdM reports mention industrial giants like Siemens, ABB, and Emerson. But the real innovation is happening elsewhere.

Companies like Augury, Senseye (now part of Siemens), MachineMetrics, and AssetWatch are laser-focused on PdM and asset health.

These niche providers often:

• Deliver faster ROI
• Offer better industry-specific solutions
• Have a deeper understanding of real maintenance challenges

Also consider some of the emerging start-ups in this space like AssetMinder, Aiomatic, Uptime AI or Predictronicswho might offer more specific or cost effective options.

The Real Business Case for PdM: Beyond OEE

Most PdM justifications focus on OEE improvements and downtime reduction. But the real financial impact goes further:

• Energy efficiency – Machines in peak condition consume less power, cutting energy costs by 10% or more.
• Spare parts inventory savings – Predictive insights help optimise stock levels, reducing unnecessary inventory.
• Longer asset life – Preventing failures extends equipment lifespan, delaying capital investments.

Too many PdM business cases fail to account for these additional benefits, making ROI appear weaker than it actually is.

Who is the True Buyer of PdM?

One major problem with many PdM business cases? A lack of clarity on who owns the decision.

• CFOs want clear ROI and cost savings.
• COOs care about operational efficiency and uptime.
• VPs of Engineering focus on reliability and long-term asset health.

A PdM pitch that tries to appeal to all these stakeholders at once will fail.

Each decision-maker needs a tailored case for PdM that aligns with their priorities.

Final Thought: PdM Is a Tool—Not a Silver Bullet

PdM can transform manufacturing. But not every factory is ready for it, and not every machine needs it.

Before investing, ask:

Before you jump on the PdM bandwagon, ask yourself:

Are we solving a real business problem—or chasing a trend?

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Source Materials:

S&P Financial Impact Analysis – Predictive maintenance Report: https://info.nickleeder.com/hubfs/Blog%20and%20Newsletter%20Media/01%20451R_TBI_FIA_PredicitiveMaintenance_2024_FINAL.pdf

Eaton Report: https://www.eaton.com/us/en-us/digital/brightlayer/condition-based-maintenance.html