Banner for the 'Utility Experts Share Insights on AI Initiatives, Challenges' article with a utilities related background

Utility Experts Share Insights on AI Initiatives, Challenges

Data and AI Energy, Water & Utilities Insight by COSOL /

At a glance

  • Practical perspectives on AI maturity in utilities and asset-centric industries
  • Key challenges: literacy, governance, regulation, and safe adoption

Industry perspectives on building AI maturity with practical business cases

COSOL, in partnership with IBM, recently brought together leaders from across asset-centric industries like utilities to discuss the emergence of AI across the industry. 

Utilities AI experts agreed that a good start 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.

Alinta Energy's prediction and forecasting model

According to Alinta Energy’s Chris Pratt, General Manager for Energy Supply Technology, as a start, 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.

Pratt reveals Alinta Energy uses 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.”

AI Literacy and Utilities

In addition to the main AI applications and challenges that asset management leaders see as being most prominent, experts discussed the challenges of AI literacy.

"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.

Productivity and Potential of AI

Anthony Cipolla, AI Lead with COSOL said the future points to more AI adoption within the utilities sector.

“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,”
he said.

“On top of this, a recent 2025 global study by the University of Melbourne and KPMG, 57% of employees admit to hiding their use of AI tools like ChatGPT at work and presenting AI-generated content as their own.”

“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.”

Constraints, challenges, and data governance

AI undoubtedly presents opportunities in asset-management oriented organisations like utilities. 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.

Utilities long image

Some such challenges, discussed across the utilities sector, concerned expectations around advancement, pace of innovation, maintaining compliance, and educating wider teams.

Pratt highlighted regulatory constraints as a particular challenge when trying to unlock insights through modern AI solutions within the utilities sector.

“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 explained.

“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.”

While AI Governance has become a priority for many organisations, 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 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.

Access the insights

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

DOWNLOAD INSIGHTS

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. 

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

IBM Gold Partner