TLDR: Recent insightful discussions with energy sector executives revealed the sector's current AI maturity and a potential path to improvement. While adoption is in its early stages, the sector is yet to realise the full benefits of AI due to data management challenges and regulatory hurdles. However, risk modernisation, team restructuring, and a flexible approach to AI development can help make significant headway.
AI presents a significant opportunity to reduce costs, expand green energy initiatives, and support long-term sustainability goals in the Australian energy sector.
Some examples of how AI can potentially impact the energy sector include:
AI-driven forecasting and real-time grid simulation can anticipate shifts in supply and demand to improve system resilience.
Predictive controls can detect potential frequency or voltage excursions, enabling generation systems to proactively adjust output before instability occurs.
Intelligent demand management can anticipate stress events and trigger dynamic pricing, demand response offers, or customer alerts to reduce peak loads.
However, any AI initiative requires a foundational level of data maturity.
The Australian energy sector has an IT ecosystem constrained by legacy infrastructure and heavy regulations. Introducing AI-driven efficiencies requires executive leadership and a willingness to challenge traditional approaches.
This blog highlights current gaps and potential solutions to accelerate AI adoption in the energy sector.
AI potential in grid management
AI Adoption Inhibitors in the Energy Sector
The following AI adoption challenges are currently being faced by many enterprises in the energy sector.
Data Silos
A significant portion of the energy sector’s operational technology, particularly in generation and grid management, works in silos. Strict security protocols enforce airgapping, so valuable data collected by these systems remains inaccessible to AI and analysis. Data sharing efforts are frequently constrained by legacy hardware and regulatory complexities, limiting opportunities for automation and real-time decision-making.
Limited Cloud Adoption
While the energy sector is responsible for managing some of the nation’s most critical infrastructure, its data and supporting IT systems often reflect a more traditional posture. A highly risk-sensitive approach to data governance has limited the adoption of scalable cloud solutions. However, most AI use cases benefit from cloud infrastructure support for faster results.
Redundant Security Escalation
Potential and past security incidents have sometimes created more rigid approaches to IT. Relatively minor security alerts can trigger broad precautionary measures, including temporary data access restrictions across multiple projects. This impacts overall productivity, slowing project delivery and increasing AI adoption costs.
Rigid Team Structures
Conway’s law states that the authors and designers of a system’s component parts must communicate to ensure compatibility and proper system functioning. AI adoption also requires cross-functional teams with experts from different domains to collaborate to solve complex problems. However, traditional team and management structures within the energy sector can unintentionally limit this collaboration. It becomes challenging to mobilise the interdisciplinary teams needed to drive large-scale, high-impact AI initiatives.
Strategic Levers for Overcoming AI Adoption Challenges
Current AI adoption in the Australian energy sector is limited to “horizontal AI”, AI tools like Microsoft Copilot, Google Gemini, etc., for everyday back-office use cases. However, the real value of AI in the sector will come from “vertical AI” applications that can access domain-specific data to predict trends, send alerts, and automate operations in innovative ways.
AI adoption in the Australian energy sector
Potential strategies to overcome previously discussed vertical AI barriers include:
Modernise the Data Risk Approach
Consider taking a tiered security approach to risk management, similar to the aviation or banking industry. Rather than applying rigid, one-size-fits-all controls, organisations can align data access and handling protocols with clearly defined risk tiers. For instance, Tier 1 may represent mission-critical systems requiring the highest protection levels, while lower-tier data can be managed with proportionate controls. This approach reduces redundant security escalations while still meeting compliance requirements.
Run Your AI Projects as Team Topologies
Team topology is about designing teams that are modular and loosely coupled but aligned to organisational goals. AI initiatives have a flow-on effect to all organisational aspects, from marketing to customer service. Several subject matter experts must collaborate across initiatives. A team topology allows legal, marketing, and other experts to work across projects, resolving issues when they arise.
Don’t Let Data Constraints Stall Innovation
AI experimentation often begins with uncertainty. This challenge isn’t unique to the energy sector; it’s common across industries. A more pragmatic approach is to treat early-stage AI projects (or proofs of concept) as learning opportunities. Document any legal, security, or technical constraints as action items to be addressed later. Trying to check every security, compliance, and performance requirement from the start could derail momentum.
The Path Forward
Technologists within the energy sector should begin by crafting a compelling narrative around their AI initiative that clearly articulates how it addresses pressing business challenges. Highlighting these “needle shifters” early on ensures business buy-in and makes the initiative measurable against business goals.
Building a prototype or proof of concept is the next step, and allows developers to document any data constraints along the way. Once prototyping is successful and results are tangible, the initiative will be easier to scale.
The long-term ambition should be to evolve from predictive to generative AI use cases, and eventually toward agentic AI, where autonomous systems interact and collaborate to drive greater productivity and innovation. This journey can be navigated through a disciplined framework of minimum viable intelligence, only working towards as much intelligence as is necessary for the current use case.
AI adoption can resemble a game of Snakes and Ladders, marked by both leaps forward and occasional setbacks. Yet despite the challenges, the path is worth pursuing. Energy organisations should focus on steady progress, as the long-term rewards far outweigh the hurdles.
Do you work in the energy sector and would like to book a discovery call on how you can launch your next AI initiative? Contact us or reach out to Dr. Pete on LinkedIn.
