Unlocking a Virtuous Cycle: Overcoming Barriers to AI in Australian Energy Systems
08.07.2026 - 02:49
Mandala's latest research, developed in partnership with Microsoft, examines the barriers to transformative AI adoption in Australia's electricity system. The research finds that AI is one of the few tools able to unlock capacity and efficiency from the existing grid without waiting on new transmission and generation capacity, yet adoption today remains incremental. Three soft barriers, a lack of shared strategy, weak investment incentives and siloed data, are constraining Australia's ability to capture this potential. Overcoming them will require joint action from government, the technology industry and energy utilities to prove AI's value, align policy settings and fund pilots through to deployment.
Australia's electricity grid is undergoing a profound transition. A system built around centralised, dispatchable generation must now integrate record volumes of renewables while absorbing rising demand from data centres and electrification.
AI is both a driver of and a solution to this challenge. Globally, AI-enabled tools have already unlocked 175 gigawatts of transmission capacity and could reduce electricity demand in advanced economies by 5 to 10 per cent if deployed widely. In Australia, however, most current AI use is incremental, sharpening forecasts or automating customer service, rather than the transformative applications that could reshape how the grid itself is run.
Mandala's research identifies three soft barriers holding the market back from transformative AI adoption.
- There is no shared ambition or strategy on AI adoption. The energy sector views AI through a risk-based lens, and market bodies have not set out clear positions on its role, leaving industry leaders conceptually keen but unsure where to start.
- Market incentives for AI investment are weak. Network revenue is set through a regulatory process with a strong bias toward capital expenditure, so AI, typically an operating expense, faces heightened scrutiny even where it is the cheaper, faster solution.
- Data is siloed and cumbersome to access. Operational data across generators, network businesses and retailers is fragmented and subject to regulatory disincentives to share, limiting the ability to train and scale AI models across the system.
These are not hard regulatory barriers. They are cultural, institutional and structural, which means they are addressable. Unlocking AI's potential in the grid will require joint action: government setting a clear national position and adapting regulatory settings, the technology industry bringing pilots and evidence, and utilities testing AI on live assets and building the case for reform over capital expenditure. With electricity demand accelerating and community concern about costs rising, the window to make this case, and to build social licence for further data centre investment, is now.
Read the full report here.
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