After two days attending the recent 2026 Australian Energy Week, one thing became clear: while the energy industry is dealing with no shortage of challenges, many organisations are wrestling with the same underlying questions.
Working the exhibition floor gave us the opportunity to speak with people from network operations, asset management, engineering, digital, strategy and executive teams. The specifics varied from conversation to conversation, but four themes surfaced repeatedly. They also happen to sit at the intersection of where data, technology and network operations are increasingly converging.
IT/OT convergence has been on industry roadmaps for years, but it feels like many network businesses have reached the point where it can no longer be treated as a standalone transformation program.
As networks become more dynamic, decisions increasingly depend on bringing together operational and enterprise data that historically lived in separate worlds. Asset information, outage data, network telemetry, planning models and customer insights all need to work together in ways that many organisations weren’t originally designed to support.
What we heard repeatedly was that the challenge is rarely a lack of data. Most networks have invested heavily in collecting it. The difficulty lies in creating a consistent, trusted view across systems, teams and business functions.
For many organisations, the next generation of use cases—whether that’s advanced analytics, operational decision support or AI—depends less on new technology and more on solving some of these long-standing integration challenges. The networks making the strongest progress appear to be treating IT/OT convergence as a capability-building exercise rather than a technology deployment.
Asset managers have always relied on data, but the conversation is increasingly shifting from visibility to foresight.
Predictive maintenance, fault detection, and asset health analytics featured in discussions throughout the week. Not because the concepts are new, but because organisations are moving beyond pilots and asking how these capabilities can be embedded into day-to-day operations.
The focus has become more practical. Rather than debating whether machine learning can identify emerging asset issues, people are asking how predictions fit into maintenance planning, how confidence can be measured, and how operational teams can trust and act on the outputs.
Computer vision was another recurring topic, particularly for inspection programs and defect identification. Many organisations are now sitting on growing volumes of imagery and sensor data and are looking for ways to extract more value from it without simply creating another stream of information that requires manual review.
Underlying all of this is a desire for better asset visibility. As networks become more complex and operating conditions become less predictable, static asset records are no longer enough. The organisations gaining traction are building a more continuous understanding of asset condition and network behaviour, allowing them to intervene earlier and allocate resources more effectively.
Unsurprisingly, AI was impossible to avoid, but the conversations were more nuanced than the headlines would suggest.
A lot of attention was focused on agentic AI, operational copilots and conversational interfaces that can help people interact with data more naturally. For many organisations, the appeal isn’t necessarily full autonomy. It’s the ability to make information more accessible to engineers, planners, operators and field teams without requiring them to navigate multiple systems or rely on specialist analysts.
At the same time, there was strong interest in more established techniques such as machine learning, forecasting and computer vision. These capabilities continue to deliver value across asset management, defect detection, demand forecasting and network operations, and many organisations are focused on scaling proven use cases rather than chasing the latest trend.
What emerged from these conversations was that agentic AI and traditional data science are not competing approaches. They solve different problems and, in many cases, complement each other. Machine learning models might generate forecasts, risk scores or asset insights, while agentic AI provides a more intuitive way for people to discover, interpret and act on that information.
The common challenge remains the same: data quality, governance and integration. Whether an organisation is deploying computer vision models, predictive analytics or AI copilots, the value of any solution ultimately depends on the quality and accessibility of the underlying data. As several attendees pointed out, making data easier to access is only useful if people can trust what they’re seeing.
The most strategically important discussions weren’t necessarily about technology at all. They were about the changing role of distribution networks in a power system that is becoming increasingly decentralised, dynamic and customer-driven.
For a long time, DSO concepts have largely existed in industry reports, regulatory discussions and future-state operating models. What felt different this year was how often elements of that future state appeared in practical conversations about planning, operations and investment.
The traditional model of electricity flowing from large generators through transmission networks to passive consumers is steadily giving way to something far more complex. Distribution networks are now managing growing volumes of rooftop solar, batteries, electric vehicles and other distributed energy resources, all of which are changing how power flows through the system.
Against that backdrop, there is growing recognition that the future role of distribution businesses extends beyond managing poles and wires. Increasingly, they will be expected to coordinate a much broader system of network assets, flexible demand, customer energy resources and distributed generation.
A common thread across industry discussions is the opportunity to unlock more value from infrastructure that already exists. Better utilisation of network capacity, combined with more active participation from consumer energy resources, has the potential to reduce system costs, improve reliability and provide greater flexibility as the energy transition accelerates.
That shift also requires a different approach to planning. Rather than relying solely on top-down forecasts, networks are increasingly looking to combine system-wide planning with much richer visibility of local demand patterns, constraints and emerging customer behaviour. As distributed resources continue to grow, understanding what is happening at the edge of the network becomes just as important as understanding what is happening at the centre.
The transition to a DSO model is ultimately much bigger than a technology upgrade. It represents a structural evolution in how distribution systems are planned, operated and coordinated with the wider electricity system. The details are still being worked through across the industry, but there is growing consensus that this direction of travel is becoming increasingly difficult to avoid.
Australian Energy Week didn’t answer every question facing the industry, but it did highlight a noticeable shift in mindset. The conversation has moved beyond whether change is coming. The focus now is on how networks adapt, build capability and deliver through it.
If these themes resonate with what you’re seeing in your own organisation, we’d be glad to continue the conversation.
At One51, we work with energy and infrastructure teams to turn complex operational and data challenges into practical, scalable capability—from IT/OT integration through to analytics, AI and modern network decision systems.
Get in touch with us to explore how your organisation can move from insight to impact.