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Asset Management Leaders Share Insights on AI Initiatives and Challenges

Data and AI Asset Management Strategy Mining Energy, Water & Utilities Transport News Insight by COSOL /

At a glance

  • COSOL convened asset management leaders to share practical AI initiatives and industry challenges
  • Leaders discussed building AI maturity gradually, following a "Walk, Jog, Run" approach for safe, scalable adoption.
  • Real-world examples highlighted AI's potential in maintenance, rostering, forecasting, and operational efficiency across mining, transport, and utilities.
  • Key challenges identified included workforce readiness, data literacy, cybersecurity, and cultural change required for effective AI integration.

Industry perspectives on building AI maturity with practical business cases

COSOL recently brought together leaders in Sydney from across asset-centric industries to discuss the emergence of AI across the industry. The conversations painted a picture of the state of AI in these Australian businesses.

The theme of the luncheon focused on how companies in asset-centric industries can build up AI capability both practically and thoughtfully. This approach avoids a big bang transformation that might be tempting to pursue given the hype around AI in business and the pressure companies feel under to “get going or be left behind”.

The conversation starter was the Walk, Jog, Run Framework, where organisations are encouraged to gradually build their AI capabilities sensibly and safely. This framework sees AI first needing to become trustworthy and repeatable, then later able to deliver real value, before late-stage scaling up into production across the business.

Automation powered by AI, be it in computer vision, language models, prediction or enhanced / democratised insights retrieval based on raw data, obviously presents great opportunities for asset-centric industries. However, as Anthony Cipolla, AI Lead with COSOL noted in his opening remarks of the session, organisations across the Australian asset-centric industry landscape exhibit mixed maturity when it comes to their AI journeys.
Cipolla said businesses were keeping the impact on the workforce front of mind when looking to adopt or increase investment in AI.
“AI is both a disruptor and an enabler, and no doubt there will be tensions and hurdles along the way as businesses reconcile this."

Cultural change, mindset and trust will be key factors that organisations in Australia either have faced, are facing or will face along their efforts to modernise with data, AI and automation, and these items were certainly reflected in the prevailing discussion.

Attendees took the opportunity to offer their own experiences, opinions and expertise when it came to AI, and voices from multiple industries were heard. Fantastic insights came out across mining, transport and logistics, and utilities on topics of AI’s potential, challenges, changing management, AI education and more.

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    AI's Potential and Projects in Play

    Having kept a close eye on how AI is being adopted across asset-centric industries in Australia and Asia Pacific, COSOL’s Anthony Cipolla said 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."

    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.

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    “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.
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    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.”

    Prominent Challenges

    AI undoubtedly presents opportunities in asset-management oriented organisations. Applications like computer vision to recognise changes in assets regularly, as well as data interpretation for potential optimisation with sophisticated LLMs, just to offer an example, are often cited as technologies offering great potential in this industry.

     However, technology changes aren't small projects, particularly when they affect organisations responsible for high-value infrastructure and equipment where safety decisions matter most.

    “When projects involve multi-million dollar assets and operational environments where failure carries substantial consequences, AI deployment can raise just as many challenges that it promises to overcome."

    Some such challenges, discussed by event attendees, concerned factors like cultural differences, expectations around advancement, pace of innovation, maintaining compliance, and educating wider teams.

    For example, the group discussed certain challenges in the mining sector, one of which being the sheer volume of data that is collected, but which is not being used to its maximum potential by many companies.

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    Another challenge the roundtable group acknowledged was resistance to change. While not exclusive to mining, this barrier to transformation can be particularly strong where big, high-value operators with long legacies are laser-focused on their core operations. Mining is, however, expected to experience more transformation as new generations of workers move into the sector.

    In utilities, Alinta Energy’s Chris Pratt highlighted regulatory constraints as another challenge when trying to unlock insights through modern AI solutions.

    “We're subject to the security standards of critical infrastructure, so governments are very concerned about keeping the lights on and we ensure that components don’t get too cold or get too hot. We take risk across operations and security seriously,” he said.

    “We have a lot of obligations to our clients, we go through a lot of audits and have a lot of security, there are a lot of regulations around what we can and can’t do. For example, we can't just go and buy an off the shelf AI solution. We have to put a lot of checks and balances and guardrails in place. That can have the effect of slowing a company down with respect to innovating.” 

    The group also highlighted  the cybersecurity implications of AI.

    "From a technology leadership standpoint, cybersecurity can present one of the most significant initial hurdles," Anthony Cipolla said.

    "As organisations embark on new initiatives, it's crucial that they create secure boundaries and operate within protected environments. This ensures that sensitive information, whether it's proprietary data or financial instruments, remains fully safeguarded.

    “The foundation must be solid governance structures, comprehensive policies, and robust frameworks established from the outset.” 

    David Small, Business Unit Executive for IBM, which develops the Maximo asset management solution, said stability and software security was often a point of conjecture between the desire to benefit from the latest technologies, including AI.

    “One of the challenges we can see customers having, as the developer of asset management solutions like Maximo, is that for those on a stable version who have worked with it, configured it and pushed the envelope with it, there might be some resistance to upgrading,” he said.

    While customers do want the latest and greatest and the AI that comes with it, there can be reservations from a stability point of view. When it is one of the core systems that's running a business, and it's operating quite well and stable, there is often apprehension among customers when weighing whether they should upgrade now to gain the benefits of the added AI tools and the latest version of the software vs continuing with the existing stable version.”

    AI Coding, Literacy, and AI Shyness

    In addition to the main AI applications and challenges that asset management leaders see as being most prominent, other topics covered during the event included the practicality of AI-powered code generation and AI shaming.

    “During a conversation at Meta's LlamaCon event in April 2025, CEO Mark Zuckerberg said that within a year, approximately half of Meta's software development could be handled by AI, with expectations for this proportion to grow over time,” said COSOL’s Anthony Cipolla.

    “I’ve already seen examples of small development teams being able to spin up ideas and prototypes extremely quickly, compressing days into hours. This is going to do wonders for productivity, for one, but the speed also allows for rapid iteration, so it can be workshopped with the business.” 

    Event attendees also touched on literacy, and were conscientious about whether less experienced developers could properly evaluate what constituted good code once the AI does most of the heavy lifting.

    "One of the challenges with AI is understanding AI and what we actually can do with it,” said Alinta Energy’s Chris Pratt. “I sometimes feel like it's a hammer in search of the nail, and many seem to believe it can solve all of their problems very easily.”

    Chris Pratt also pointed out that this literacy combined with the still early days of AI adoption also raises questions about whether it’s OK to use AI. 

    “Because AI is still new, the rules of how to use it and when are still being tested across all industries” he said.

    A phenomenon, termed ‘GPT shame’ or ‘AI shaming’, refers to when businesses, employees, and students use AI tools like ChatGPT to review, summarise and generate content but feel uncomfortable admitting this to their peers.

    This creates an interesting dynamic where executives might prefer not to know about widespread AI usage, even though it's already happening across their organisations. The irony wasn't lost on participants that while they sat discussing AI strategies, many of their teams were likely already using these tools to boost productivity.
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    Closing Thoughts on Managing Change

    Adopting AI presents many opportunities but also challenges, and demands of companies to interrogate their business across a number of areas. Several of those discussed during COSOL recent executive roundtable included cultural transformation, security, talent shortages, lack of data expertise and more.

    “The scale of the change that AI presents to all industries is perhaps comparable to the disruption brought by the internet, mobile technology and cloud computing (though likely more exponential in nature). At the same time, the concept of the Agentic-Web is being developed to determine how AI systems are standardised and communicate with each other.” 

    AI Governance has also become a priority for organisations, though the good news is that it builds on the existing Data Governance work many companies have already undertaken.

    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 one of our partners, 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

    Bonus Resources
    12 Inconvenient Truths of AI - A Resource for Asset-Centric Organisations

    During the session, COSOL presented a set of 12 Inconvenient Truths of AI, principles which the company believes are important to recognise as companies move through the walk-jog-run phases of their AI maturity.

    12 inconvenient truths

    To access even more insights on AI from key asset management leaders, click on the link below.

    Download the whitepaper

    About COSOL

    COSOL is built on one belief: in asset-centric industries, reliability is everything. We’re a trusted, data-led asset management partner for organisations around the world who can’t afford to fail. And known for our deep expertise, dependable delivery, and ability to keep critical assets performing at their best.

    We recently celebrated 25 years in business, are Australian-owned and operated, and  recognised as reliable partners by their clients across the globe.

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