Insights

Everyone is talking about AI, predictive analytics and prescriptive maintenance.

For good reason.

These technologies have the potential to improve asset reliability, optimise maintenance activity and support better operational decision-making.

But technology is rarely the constraint.

I’ve seen organisations invest heavily in sensors, monitoring platforms and analytics while still struggling with poor asset information, ineffective maintenance strategies, inconsistent work execution and unreliable maintenance data.

AI won’t fix a weak maintenance system. It will simply expose its weaknesses faster.

Before asking whether an organisation is ready for prescriptive maintenance, I think there are more fundamental questions to answer:

Do we know which assets matter?
Do we understand how they fail?
Can we trust the data we’re collecting?
Are we executing maintenance effectively?
Are we making decisions based on evidence or assumptions?

If the answer to these questions is no, adding another layer of technology may generate more information, but not necessarily better decisions.

Technology Is the Multiplier, Not the Foundation

Advanced maintenance relies on some relatively unglamorous building blocks:

Asset Criticality
Understanding where risk, engineering effort and investment should be focused.

Failure Modes
Understanding how equipment can fail and which failure mechanisms can reasonably be detected before functional failure occurs.

Maintenance Strategy
Selecting the right preventive, predictive, condition-based or run-to-failure approach for each asset.

Data & Work Management
Ensuring information is reliable and that identified maintenance actions can actually be planned, prioritised and executed.

Continuous Improvement
Using reliability, maintenance and failure data to continually refine maintenance strategies and improve performance.

None of these are alternatives to AI.

They are what make AI useful.

The Most Advanced Strategy Isn’t Always the Best Strategy

One of the biggest misconceptions in maintenance is that maturity means deploying increasingly advanced technology across every asset.

It doesn’t.

Some assets are perfectly suited to run-to-failure. Others justify preventive maintenance. Critical assets with identifiable degradation mechanisms may benefit from predictive monitoring, while a smaller subset may support more prescriptive approaches.

Maintenance maturity isn’t about applying the most advanced technology. It’s about applying the right strategy to the right asset.

Once that foundation exists, AI and advanced analytics become powerful accelerators. They can process larger volumes of information, identify trends that may otherwise go unnoticed and support faster, more informed decisions.

But technology is not a shortcut to maintenance maturity.

The organisations that extract the greatest value from AI are usually those that have already mastered the fundamentals.

Build the maintenance system first. Then let AI make it better.