Asset management leaders share insights on AI initiatives, challenges

Industry perspectives on building AI maturity with practical business cases

COSOL, in partnership with IBM, recently brought together leaders from across asset-centric industries to discuss the emergence of AI across the industry. The conversations painted a picture of the state of AI  businesses.

Having kept a close eye on how AI is being adopted across asset-centric industries in Australia and Asia Pacific, Anthony Cipolla, AI Lead with COSOL there were many exciting case studies emerging. 

"Some of our customers in transport and logistics are doing some really interesting things at the moment," he said. "We know technology leaders that are leaning into robotics, automation and AI to manage how equipment, resources and people move around on their sites. They're also looking to use AI to improve workflows and rosters for staff, there's training and career development use cases, and tools to optimise space for greater utility; there's a lot of potential." 

"There's also great applications in other site-centric cases. Some teams are using computer vision for stockpile assessment or worksite safety and compliance."

Industry experts reveal key AI priorities and key to success

For Rolf Samonte, Head of ICT & Cyber Security for Metro Trains Sydney, enabling the AI opportunity for line maintenance has been a focus. The company, which operates and maintains the Sydney Metro M1 Northwest & Bankstown Line, has already taken steps to plan for success.

"I think one of the key enablers for success with AI for us is that we have established the AI steering committee, headed by our CEO. The leadership buy-in is really driving the initiatives to come forward," he said.

"Where AI could fit for us is around smarter maintenance, whether it's using IoT and bringing that data into our ERP system and then getting the trends out of that so that we can work safer, smarter and more efficiently."

Alinta Energy's Chris Pratt, General Manager for Energy Supply Technology, pointed out that prediction and forecasting were core pillars of an energy utility's work to ensure grids functioned properly. This is a space where data is fundamental and AI's potential is high.

"It all comes down to prediction. What is our demand going to be at five o'clock tomorrow, when everyone comes home? What's the weather forecast going to be at five o'clock. What's the price of the energy market going to be?," he said.

"We use machine learning in the trading space to understand and determine demand. We can also harvest the data we have available to extract better information, which results in better outcomes for industry.

"If you predict what demand is going to be, you might turn your generator on half an hour earlier or put on a cheaper generator, resulting in lower overall price.

"There is also technology we are rolling out now where a customer will call up, and AI will be able to identify that customer and what they might be calling about for the call centre operator, providing faster answers to customers." 

Fiona Love, General Manager for Workforce Development at the Australasian Railway Association, was bullish about the impact AI would have on asset management. Love sees optimisation potential in rostering and other areas to drive efficiencies on site and, in particular, improve conditions and bolster the workforce. 

"Clearly asset management maintenance is where AI can play a huge role," she said. "One area I think about is that we want to have a much more diverse workforce. There are of course shortages in talent we're dealing with, whether it's in design, construction, operations or maintenance. 

"However, the way rosters are currently designed means they will never work for a lot of women out there. AI can potentially help us a lot with some of those factors, because you can bring a lot of non-linear, social, emotional lifestyle factors into an AI model to help it work through."

'Walk, jog and run' with AI

Business transformation takes time, communication and understanding across organisations and industries. For asset-centric industries looking to walk then jog then run with AI, this means effective change management must also be one of the most important areas of focus.

This business-first view was shared by Paul Lee, IBM ANZ Senior Technical Specialist for IBM Asset Lifecycle Management.

"There's no such thing as an IT project. Everything is a business project, some just have an IT component," he said. 

"In the case of AI, organisations need to always be thinking about what the business problem is that they are trying to solve, or the business benefit they are trying to gain. You can explore those business cases with your technology partners to tease out the right AI implementation."

For organisations looking to develop their AI roadmap, COSOL brings deep experience as a trusted implementation partner across asset-centric industries, while IBM provides the proven platform foundation with Maximo's integrated AI capabilities. 

Together, this partnership approach helps companies navigate their AI maturity journeys with both strategic guidance and reliable technology infrastructure.

To access even more insights on AI from key asset management leaders, click here to download the whitepaper.