Getting to 82% Renewables: How AI secures our energy future

2026-03-11 · Source: Thoughtworks Insights · Field: Energy & Utilities — Renewable Energy Systems, Energy Markets & Policy, Utilities & Infrastructure · Depth: Intermediate, long

Summary

Australia's National Electricity Market (NEM) faces significant challenges in achieving its 82% renewables target by 2035, driven by the proliferation of distributed energy resources (DERs). The transition requires AI to address grid stability, but systemic bottlenecks hinder progress. These include data fragmentation conflicting with regulatory urgency (e.g., Consumer Data Right), the IT/OT convergence chasm for real-time data ingestion, forecasting shortcomings due to renewable intermittency, a talent and trust gap in AI deployment, and increased cyber vulnerability from un-audited DER connections. To overcome these, an actionable roadmap suggests anchoring AI initiatives in business criticality, prioritizing operator adoption and trust, establishing unified data foundations like data mesh, and adopting an evolutionary, platform-first approach. AI currently delivers value in retail customer experience, asset optimization, field force efficiency, and grid security, with future potential in integrated DER control and full digital twin ecosystems.

Key takeaway

For Directors of AI/ML or VPs of Engineering tasked with accelerating energy transition initiatives, prioritize foundational engineering over isolated AI pilots. Your strategy must address data fragmentation and IT/OT convergence by establishing a unified data foundation, potentially via a data mesh. Focus on building operator trust and adopting a platform-first approach to de-risk deployment and ensure continuous delivery of AI-driven solutions for grid resilience and net-zero targets.

Key insights

Scaling AI for energy transition requires addressing data fragmentation, IT/OT convergence, and trust through a platform-first, business-critical approach.

Principles

• AI adoption needs business-critical problem anchoring.
• Unified data foundations are prerequisite for scaling AI.
• Platform engineering accelerates AI innovation and delivery.

Method

Anchor AI decisions in business criticality, prioritize adoption and trust, establish a unified data foundation (e.g., data mesh), and adopt an evolutionary, platform-first approach for continuous delivery.

In practice

• Use AI for predictive maintenance of grid assets.
• Optimize field crew logistics with machine learning.
• Implement data mesh for secure, productized data assets.

Topics

• Renewable Energy Integration
• Australian NEM
• AI in Energy
• Data Mesh
• IT/OT Convergence
• Grid Cybersecurity

Best for: CTO, Executive, AI Architect, Director of AI/ML, VP of Engineering/Data, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Thoughtworks Insights.