Sponsored by IOTech Systems
As modern grids absorb unprecedented volumes of data from BESS, solar, wind, EV charging, and other distributed energy resources, the industry faces a critical challenge: how to transform raw industrial data into real-time operational intelligence. The shift toward decentralised, software-defined energy systems demands high-performance edge platforms capable of unifying diverse protocols, executing analytics at the point of control, and scaling across thousands of assets.
This expert panel brings together leaders in energy technology, grid operations, and edge computing to explore how AI-ready, standards-based data infrastructure is reshaping the future of distributed energy. Panelists will share insights from real deployments, discuss the architectural patterns that enable secure and interoperable DER fleets, and examine how edge intelligence is unlocking new value streams — from predictive maintenance and optimisation to autonomous control.
Attendees will gain a practical understanding of the technologies and strategies required to build scalable, resilient, and future-proof energy systems powered by industrial data.
Hamish Mackenzie operates as a Fractional Chief of Staff to space and deep-tech startup/scaleup CEOs, providing the focus, clarity, and execution leverage they need to turn complex technology into reliable growth — without adding full-time headcount.
With 20+ years in B2B technology and GTM strategy, he helps founders align their teams, sharpen their message, and build predictable momentum — by combining strategic GTM support with AI-enhanced operating systems that unlock leadership capacity across the business.
Sponsored by InfluxData
Virtual Power Plants coordinate tens of thousands of batteries, EVs, and smart devices as single grid resources—but the data complexity is staggering. Panelists will discuss handling unreliable edge connectivity, out of-order data, and the shift from site-level to fleet-level intelligence. Learn how companies architected for scale, maintained compliance with grid operator telemetry requirements, and turned distributed chaos into coordinated flexibility worth billions in grid services.
Sponsored by Adlib Software
Most energy enterprises have already invested in historians, ALM/ALIM, EHS platforms, content repositories, and even AI, yet teams still rely on people to manually clean, transform, and validate operational documents as they move between systems. That’s where errors creep in, cycle times stretch, and compliance exposure grows.
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In this session, we’ll break down an upstream-first approach to connect fragmented processes and enforce consistency automatically: ingest documents from anywhere, normalize formats, preserve/align metadata across systems, extract key fields, validate against business rules, and package “documents of record” that are ready for audit and safe to feed into analytics, RAG, and GenAI workflows.
What attendees will learn (practical takeaways)
• A reference architecture for cross-system interoperability (how to connect PLM/ALIM/ERP/EHS flows without ripping and replacing)
• The upstream “trust gates” that matter for AI: format normalization, metadata preservation/enrichment, content governance, and validation before anything hits RAG/LLMs
• How to turn document automation into compliance enforcement: rule-based validation, exception routing, and audit-ready packaging (instead of ad-hoc clean-up)
• Metrics that prove AI readiness (exception rate, validation pass rate, cycle time, audit prep time), plus what “good” looks like in regulated environments
The grid is the primary bottleneck for the global AI economy. With data center loads forecast to double by 2030 and interconnection queues stretching beyond a decade, the "business as usual" model of grid planning has collapsed. This session brings together the hyperscalers demanding power and the utilities tasked with delivering it to discuss the new commercial reality: who pays for rapid upgrades, how capacity is "reserved," and what happens to the rest of the queue when a Gigawatt-scale project moves in.
Sponsored by Phaseshift
As grid constraints intensify, utilities and energy operators are increasingly dependent on high-quality, consistent operational data to understand how assets perform across sites, vendors, and environments. IIoT platforms and AI systems rely on access to reliable, normalized data from the edge to enable accurate analysis of utilization, constraints, and operational risk. Without a common data foundation, scaling analytics and decision-making across fleets remains a significant challenge.
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This panel will examine how standardized, shareable operational data is enabling AI-driven insights that support planning and capital decisions. Speakers will discuss how data architectures that connect, normalize, and distribute information across stakeholders are being used to improve confidence in upgrade timing, investment prioritization, and long-term grid strategy. The focus is on how data infrastructure and AI together support decisions that extend grid capacity while maintaining reliability and control.
