From Ninja to Neural Networks: Why Trust is a Must for AI in Asset Management

Data and AI Insight by Scott McGowan /

At a glance

  • Trust is the cornerstone of AI in asset management
  • Success depends on data integrity, algorithm transparency, and security
  • COSOL's Brisbane Airport case shows predictive maintenance in action
  • Building trust turns AI from hype into real operational results

When I was first introduced to the world of asset management more than 25 years ago, I visited a site where a person known only as “the ninja” would walk around the facility once a month. No one knew exactly what they did, but they could predict failures with uncanny accuracy.

“The asset will fail in three weeks,” they’d say - and they were almost always right.

It was my first exposure to what we now understand as experiential pattern recognition. The ninja had no algorithm, no digital data - just a lifetime of experience. But they were trusted. And trust, I believe, is the foundation upon which AI must be built.

Easier said than done when some 78% Australians have concerns according to a KPMG & Melbourne business school study.

infographic of 78% Australians being concerned over AI

The New Age of Asset Management

Today, AI promises to do what that ninja once did - but faster, at scale, and without needing a lifetime of on-the-ground experience. It’s attempting to demystify the ninja. With modern AI, particularly agentic and reasoning models, we can analyse performance indicators, recognise anomalies, and predict failures with remarkable accuracy. And we can do it across thousands of assets simultaneously.

But there's a catch: AI must be trusted to be used.

In the asset-centric industries we serve - mining, transport, utilities, defence, public infrastructure - AI will only succeed if executives and front-line teams have confidence in three key things: 

  • The data
  • The algorithms, and
  • The security

But as I mentioned, the numbers are currently against us when it comes to trust in AI as brought out in this CRN article highlighting how a lack of trust in AI systems is holding Australian businesses back. How do we overcome it? This is my thinking:

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Trust in Data
AI is only as good as the information it’s fed. If your source data is flawed, incomplete or misaligned with operations, the insights will be too. In asset management, where predictive maintenance and uptime are mission-critical, data integrity is non-negotiable.
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Trust in Algorithms
Most people don’t understand how today’s AI models make decisions. And that opacity breeds hesitation. We need to ensure that any AI we use or want our staff to use, are designed with algorithm transparency in mind - providing a clear methodology or, at the very least, an understandable rationale behind their recommendations, so that people can trust and make sense of how decisions are made.
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Trust in Security
Understandably, organisations are asking: Where is my data going? Who has access? What are the implications? Just as we eventually came to trust Google with our queries, and cloud platforms with our files, we must cultivate the same level of comfort with AI tools. Being able to confidently answer those three questions as a team are key.
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The Bigger Picture
The true opportunity for AI in asset management is massive. This is a sector that hasn’t yet undergone the kind of digital transformation seen in finance or retail. That means the efficiency gains, safety improvements, and cost savings ahead of us are nearly limitless.
Look no further than COSOL’s client Brisbane Airport Corporation for proof. Brisbane Airport ran a predictive maintenance pilot on select baggage conveyors, using a single dashboard to monitor the health of each asset. By combining key factors like age, condition, and performance into a clear health score, they could quickly spot issues, dig into the details, and act fast before problems escalated.
But to get there, we need to start by fostering trust - not hype.

By building credibility around the data, the algorithms, and the security posture of AI platforms, we turn fear into confidence and pilots into production.

So, how do we scale the ninja? We do it with AI - built on trust, guided by experience, and grounded in data we can rely on.

Where AI Fits at COSOL

At COSOL, AI is not a side project – it runs through our AMaaS solutions. Maintenance scheduling, planning, work management, data governance, master data, and our AI Asset Lifecycle solution all rely on it. But none of it matters without trusted data. That’s the foundation, and it’s non-negotiable.

Board and Management - COSOL, Scott McGowan headshot, part of the Executive Team

About Scott McGowan

Managing Director & CEO of COSOL
Scott is the Managing Director and Chief Executive Officer of COSOL Limited. He is a highly experienced executive manager with a demonstrated ability to lead diverse teams of professionals to new levels of success in highly competitive markets. Scott has over 20 years’ experience in both start-ups and global multinational corporations and possesses strong technical and business qualifications with an impressive track record in strategic planning, business unit development, project management, product development and system engineering strategies.

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