Published on the 12/06/2025 | Written by Newsdesk

Asset-centric industries are ripe for an AI revolution, but without the hype…

Modern asset management across industries like mining, utilities, public infrastructure, road and rail represents a massive exercise in managing and analysing data.

Data performs a critical function within these industries, albeit one that has seen less digital transformation than other segments in recent years. Because of this, there is pressure on leaders to lunge for an AI project as a means to quickly manifest efficiencies in their operations.

“Data foundations must also be trusted, and solutions that act on that data must be trusted to do so correctly.”

However, whether organisations are curious, excited, motivated, or feeling like they’re under boardroom duress to deploy an AI solution into their asset management workflows, strategic considerations about how best to integrate these tools must come first.

Digital transformation is not a small undertaking, nor should it be about technology in and of itself, especially for companies managing critical infrastructure and assets potentially worth billions.

Hype Cycles, distractions and trust If you ask Scott McGowan, CEO of Australian asset management solutions firm COSOL, what he thinks of AI, he’ll tell you it represents a lot of potential in the asset management sector. However, he’ll also tell you not to run out and invest in an AI solution just because your board thinks you should.

“Data foundations must also be trusted, and solutions that act on that data must be trusted to do so correctly.”

Observing the proliferation of AI adoption hype across industry events, social media and the press, McGowan is concerned that market pressures might push organisations in asset-centric industries to try and run with AI before they can walk.

“Everyone expects everyone to build an AI division in-house, but if you’re a mining or infrastructure company, the reality is that’s not your core business,” he says. “Your core business is to produce iron, or coal, or copper, or to run trains and provide services to the public.”

Without reason or a roadmap, the discourse around AI integration often represents an unwelcome distraction from the primary mission of asset-centric industries.

Adoption pressures are being met with a degree of hesitation from business leaders, McGowan adds, due to the somewhat untested nature of AI in an asset management context and an absence of established trust.

“Hesitation toward AI probably comes from multiple aspects, but they all come back to one underlying principle, which is around trust,” he says. “Trust in security, trust in algorithms, and trust in data.”

“Trust is built on experience and understanding, and I think the challenge we have with AI as it stands today, is that the algorithms are designed in such a way that there’s little to no transparency as to the decision-making methodology those algorithms have.

“Data foundations must also be trusted, and solutions that act on that data must be trusted to do so correctly. Organisations need to trust that any solution they adopt is repeatable, robust and resilient.”

Exploring a path forward for AI in asset-centric industries will be the subject of a series of roundtables that COSOL, in partnership with IBM, will be running across three Australian cities in June. While the sessions are closed, select insights are set to be released highlighting discussions, pain points and strategies business leaders face.

Ahead of the events, COSOL’s McGowan touched on some of the issues set to be covered during the campaign.

First Steps: Learning to Walk with AI

AI transformation is a journey, not a big bang project. Organisations that approach their AI maturity through a lens of a walk-jog-run approach based on the true needs of the business are much more likely to find value in implementation versus those that seek shiny solutions.

In the first instance, the walk phase, McGowan urges that AI has to be solving an existing problem.

“There are a lot of AI initiatives and a lot of technologies looking for a problem; I think that’s the wrong way to go about it,” he says.

“The walk concept is solving a discrete problem that already lies within the technology space. If you think about self-solving and trust in data, using AI to resolve, infer, continually assess or optimise data quality is an interesting place to start.

“Through this approach AI starts to solve its own trust problem. The opportunity around master data quality and the ability to link physical equipment with digital representations for accuracy is in my view the first part of the AI journey.”

David Small, Principal Sales Leader for IBM Automation Software in Australia and New Zealand, echoes the need for organisational data layers to be updated and as accurate as possible to enable smarter asset management.

“Without quality data, organisations will struggle to achieve key benefits as they move along the asset maturity curve,” he says. “Data is the building block that asset management systems need.

“The direction that IBM has taken is to embed AI capability within the Maximo Application Suite vs saying to organisations go and build your own AI capability.

These AI enhancements are integrated AI experiences that deliver immediate value, increase productivity and seamlessly scale.”

Adding capability and building an AI-integrated business

Looking further into an AI modernisation program, companies might look to select an AI partner to help them further investigate ways and means AI can be implemented, be it in a proof of concept, or solving discrete problems.

Partnering with someone who specialises in AI can help companies build capability within the business from a cross-functional perspective.

“This phase is around automation of things like low-value tasks, work order administration, and master data creation as examples,” McGowan says.

“AI tools can bring forward understanding how to take a piece of equipment and represent it digitally and accurately.”

These types of initiatives should not be about solving technology problems but enhancing the operational capability of the business.

Of course, McGowan notes that strategy will need to be changed to enable technology to solve business problems. This may mean redefining the operating model and job descriptions around the purpose of AI.

“I can almost see a place where you define a job description for the AI agent or component, and it performs that task, has regular performance reviews, learns from its mistakes, and is managed like anyone else in a business,” he says.

Playing the long game

The warning to ‘innovate or be left behind’ is regularly bandied around with new technologies.

While McGowan notes the AI opportunity in asset management represents considerable potential, he feels strongly that it must be driven by practical business needs, organisational fit and readiness, rather than hype.

“The potential for AI particularly in the asset management space is almost endless, because largely the sector has not gone through as many digital revolutions that other industries and sectors have gone through,” he says.

“There are opportunities to do things like provide real-time feedback around avoiding potential incidents, or automating non-value-adding tasks.

“But it’s important that when we talk about driving efficiency with AI that we evolve our thinking in terms of what it means for careers and jobs and tasks and working to make those AI-infused rather than AI-replaced.

“We need to look at evolving the operating model to support the introduction of efficiency gains from AI and then driving your valued labour to much higher value tasks.”

This messaging will be central in COSOL’s upcoming roundtable series in Australia over the coming weeks.

With asset management leaders coming together to discuss the pivotal role of AI in shaping the future, valuable insights are guaranteed to emerge.

Many of these insights will be shared in an upcoming report. To register to receive these learnings, click this link.