Artificial intelligence (AI) can be effectively deployed by renewable energy developers to address regularly encountered challenges.
We examine three key challenges facing developers and how AI can be strategically applied – and carefully managed – to unlock value while mitigating risk.
- Addressing the skills shortage: expanding capacity without additional headcount
The renewable energy sector faces ongoing recruitment challenges, with the Australian Government recognising that 40,000 additional workers are required by 2030. This shortage increases developer labour costs and extends project timelines.1
AI can bridge skills gaps through automation, transforming resource allocation and cutting time spent on routine analysis, which in turn allows teams to focus on strategic decisions that directly impact project returns. This expands existing team member capacity by redirecting time towards higher-value activities and increasing productivity.
However, AI cannot provide the contextual judgement that experienced professionals bring to complex projects. For example, AI processes cannot replace the ability of an experienced developer to understand stakeholder dynamics, adapt strategies based on relationship history, or apply the pattern recognition that comes from navigating multiple projects from conception to completion. Developers and legal advisers must combine AI’s efficiency gains with deep contextual knowledge of how renewable energy projects unfold over time, ensuring technology serves strategic objectives within an experience-driven framework, rather than simply processing information faster.
- Improving project management: combining data with commercial judgement
Renewable energy projects involve coordinating multiple interconnected workstreams such as procurement, construction scheduling, contractor management, regulatory compliance, and stakeholder engagement. Developers face rising material costs, supply chain disruptions and uncertain component availability. Such external pressures must be effectively managed before they contribute to higher delivery costs and longer project timelines.
AI-driven project management systems can analyse scheduling options, track milestone dependencies, compare supplier lead times, optimise resource allocation, and monitor budget variances to minimise delays and cost overruns. Effective project management requires expert assessment of the qualitative factors that AI cannot replicate, such as contractor reliability and performance history, supplier financial stability, contractual risk allocation, and the relationship dynamics that determine how issues are resolved when problems arise.
Developers can address the AI shortfall by combining high-quality AI-generated data with their deep understanding of project interdependencies, contractor and supplier relationships, and contractual structures to develop management strategies that balance efficiency with risk management.
- Streamlining regulatory compliance: accelerating approvals while managing risk
The planning and development landscape is increasingly complex and expensive. Australian states and territories operate individual regulatory systems with rigorous requirements and substantial financial commitments. Application and assessment fees are significant costs faced by developers during the pre-construction phase.2
Developers can deploy AI to process vast regulatory datasets and generate summaries of complex frameworks in minutes, where it would have previously taken days. Continuous monitoring of environmental and financial risks allows teams to identify critical changes as they emerge offering significant time savings.
Yet regulatory success also depends on factors that AI cannot assess. Securing approvals requires an understanding of how requirements apply to specific project circumstances, evaluating the likelihood of discretionary decisions, and navigating the political and stakeholder dynamics that may heavily influence outcomes. Experienced legal judgement is required to translate AI-generated regulatory analysis into advice tailored to each project.
Moving forward
The real-life examples outlined above demonstrate how AI can help developers navigate challenges. When applied strategically and guided by expert judgement, AI can be a valuable tool to enable developers to unlock value, reduce risk, and accelerate the delivery of successful projects.
The Hamilton Locke team advises across the energy project life cycle – from project development, grid connection, financing, and construction, including the buying and selling of development and operating projects. For more information, please contact Matt Baumgurtel.
1Clean Energy Council, Clean Energy Australia Report 2025 (Report, 2025) <https://cleanenergycouncil.org.au/getmedia/f40cd064-1427-4b87-afb0-7e89f4e1b3b4/clean-energy-australia-report-2025.pdf>.
2Australian Energy Market Operator, 2025 IASR Planning and Installation Cost Escalation Factors (Report, 2025) https://www.aemo.com.au/-/media/files/major-publications/isp/2025/stage-2/2025-iasr-planning-and-installation-cost-escalation-factors.pdf(https://www.aemo.com.au/-/media/files/major-publications/isp/2025/stage-2/2025-iasr-planning-and-installation-cost-escalation-factors.pdf).
