Why it’s time Australian asset management leaders explore the true value of AI
Modern asset management across industries like mining, utilities, public infrastructure, road and rail represents a massive exercise in managing and analysing data.
This data-driven domain is a critical function within these industries, albeit one that has seen less digital transformation than other segments in recent years. Because of this, there appears to be increasing pressure on asset-centric business leaders to lunge for an AI project as a means to quickly manifest efficiencies in their operations.
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. The stakes are high, which is why one industry leader is calling on organisations to zoom out and take a look at the big picture.
Hype cycles, distractions and trust
If you ask Scott McGowan, CEO of data-led 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.
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 messaging set to be covered during the campaign, including what might it look like for an organisation to take its first steps with AI before building maturity in considered stages.
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 actually a really interesting place to start.
"Through this approach AI starts to solve its own trust problem by addressing one of the initial play areas. 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 in our context."
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 and rely on to support the company's chosen asset management strategies.
"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 around asset management.
"This phase is around automation of things like non-value tasks, work order administration, and master data creation as examples," McGowan says.
"There is a lot of work that goes into master data creation. So, following from a solution in the first phase that knows what good data looks like, you can take it to the next phase of being a master data specialist that understands how to take a piece of equipment and represent it digitally and accurately."
When a company is running with AI, it is essentially changing its operating business model. With each new wave of technology, transformation initiatives should not be about solving technology problems, but enhancing the operational capability of the business.
Of course, McGowan notes that business parameters will need to be changed to enable technology to solve business problems if the integration is significant. In the case of AI, this may look like redefining career paths, redefining the operating model and the job descriptions for individual components and then rolling out 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.
"I think that's the evolution: creating a space where AI has a role to play that is embedded in the organisation and the operating model, which measures and manages it like every other human resource within the business."
Playing the long game
With every new wave of technology across data, analytics, automation and AI, business leaders are often targeted and lobbied to act with urgency on a new solution. The warning to 'innovate or be left behind' is regularly bandied around these concepts.
While McGowan notes the AI opportunity in asset management represents considerable potential, he feels strongly that it must be approached 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, and the automation of non-value-adding tasks will also be significant.
"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 form part of the jumping off point for COSOL's upcoming roundtable series in Australia.
With 60 asset management leaders across Sydney, Melbourne and Brisbane coming together to discuss the pivotal role of AI in shaping the future of asset management in the coming weeks, valuable insights are guaranteed to emerge.
Many of these insights will be shared in an upcoming report. To register for these learnings, click this link.