In Australia’s energy sector, AI in project management is driving fast evolution as developers embrace artificial intelligence tools to automate tasks, optimise operations, and support complex decision-making.
Global forecasts suggest AI could automate up to 80% of project management tasks by 2030, a trend that will extend to energy projects. This shift could speed up schedules and improve outcomes. However, it only works if human experts stay in charge of strategy and quality control.
AI in project management: automating the basics
Each year, governments and companies invest trillions into projects worldwide, yet according to the Standish Group, only 35% of projects globally meet time, budget, and scope targets. This is a statistic reflecting systemic challenges in project delivery.
In energy, where projects often span multiple years and involve vast civil works, small efficiency gains can save millions. Today, engineers use AI-driven platforms to handle repetitive tasks such as scheduling, document control, and risk flagging.
Recent research estimates that by 2030, automation could handle up to 80% of these process-driven tasks. This shift frees human managers to focus on high-value strategic work. In practice, AI engines ingest project data; such as budgets, timelines, and resource bookings, and generate real-time forecasts of potential delays or cost overruns. This accelerates reporting and also identifies risks earlier than manual methods.
In Australia’s renewable energy sector, AI and optimisation tools are already in pilot use to balance intermittent supply and demand. Project teams are piloting AI models to analyse solar and wind output alongside weather patterns. These tools support smarter dispatch decisions
Similarly, platforms combining AI with Internet of Things (IoT) sensors are revolutionising solar farm maintenance by predicting panel failures before they occur and significantly reducing downtime.
Such advancements extend to energy storage projects too. Engineers are exploring AI-driven systems to optimise grid-scale battery operations and automate responses to demand fluctuations. By handling these routine but critical decisions, AI tools deliver the consistency and speed that large-scale projects require.
Tools, teams, and trust
Despite AI’s efficiencies, human oversight remains essential.
“It’s a tool that does calculations that I can’t do in my head, and has access to information that I don’t have,” says our CEO Andrew Murdoch.
“It’s going to help me make better decisions — but I still have to make the decision. And I also have to error check.”
This mirrors how we use everyday AI, such as navigation apps, which calculate routes far faster than we could. Yet, we still decide whether to follow their advice.
Overreliance on AI in project management outputs can introduce blind spots if data inputs aren’t carefully vetted. In energy projects, flawed input data could lead an AI model to recommend unviable schedules or budgets.
The emerging model is one of collaborative intelligence, where AI virtual assistants handle routine queries, such as pulling reports, updating dashboards, and alerting teams to anomalies. Meanwhile, human experts focus on stakeholder engagement, regulatory strategy, and safety oversight. Such a hybrid team can scale up rapidly: once an AI-assisted process is validated, it can be deployed across dozens of similar projects with minimal training overhead.
Experts are now exploring how to integrate AI tools with distributed energy resources. State-led initiatives, such as South Australia’s Virtual Power Plant, coordinate rooftop solar and batteries to stabilise local grids. Project teams use AI alongside real-time network monitoring to forecast local demand surges. They can then pre-charge storage assets — automating grid resilience measures that once required manual intervention.
Of course, rolling out AI at scale brings governance challenges: data privacy, algorithmic bias, and cybersecurity risks must be managed through robust policies and cross-functional oversight. Industry bodies have stressed the need for interoperable standards and transparent AI “explainability” so that every recommendation can be traced back to its source data.
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