From optimizing grid operations to predicting equipment failures and managing demand in real time, artificial intelligence and advanced analytics enable energy providers to operate smarter, safer, and more sustainably.
The energy sector stands at a turning point. Decades-old power grids and infrastructure are straining under unprecedented pressure. On one hand, growing demand from electric vehicles, data centers, and the electrified industry is driving load spikes; conversely, aggressive decarbonization goals and renewable build-out mean the grid must integrate highly variable sources. Many transmission lines and substations are already past their designed lifespan.
As a recent ABI Research analysis explains:
“The way we produce, deliver, and consume electricity is undergoing a seismic shift… energy networks built decades ago weren’t designed for this level of complexity….Digital transformation becomes essential”.
Indeed, innovation is the key to meeting these new challenges. Modern data-driven tools and analytics promise to improve reliability even as we add renewables, manage dynamic loads, and push toward net-zero emissions. Cloud platforms, edge IoT, artificial intelligence (AI) and machine learning (ML), and “digital twins” are now deployed to modernize aging grid and plant assets. IT leaders must now shepherd this transformation.
The Core Technologies Fueling Innovation
AI, ML, and data platforms are among the core technologies fueling innovation in the energy sector. Utilities are deploying advanced analytics for forecasting and optimization, alongside specialized data infrastructures.
According to ABI Research, companies like Schneider, Siemens, GE, and Honeywell are embedding AI and digital twins into their control systems, turning raw sensor and telemetry data into smarter operational decisions. The digital twins—real-time virtual replicas of a substation, wind farm, or pipeline—can continuously ingest live data to simulate behavior under stress, predict wear, and optimize performance. These AI-driven models help translate intermittent solar and wind generation into reliable capacity planning and embed forecasting directly into operational workflows.
A key enabler is real-time data processing at the edge and in the cloud. High-frequency IoT sensors and industrial PCs on the grid edge collect millions of data points per minute. Edge computing appliances (even GPU-based devices like NVIDIA’s Jetson Nano systems) can run ML models locally to analyze sensor data on-site. This reduces latency and bandwidth needs, allowing a rapid response to local anomalies.
For instance, Itron integrates NVIDIA Jetson edge processors into its Grid Edge Intelligence portfolio, letting utilities perform AI tasks (like outage detection or load balancing) right at the grid’s edge. Meanwhile, cloud platforms aggregate and store vast quantities of sensor, meter, and weather data for enterprise-wide analytics.
Major utilities are consolidating SCADA and IoT streams into cloud data lakes. Moving their legacy on-prem SCADA logs into services like AWS or Azure allows 24/7 centralized monitoring, scalable analytics, and machine learning on historical trends. By blending edge and cloud—sending curated data upstream while keeping critical control at the plant—modern platforms give operators global insight and local agility.
The old SCADA/OT (operational technology) systems are no longer isolated but interoperate with enterprise IT. Utilities are building hybrid IT/OT architectures so that telemetry from programmable logic controllers (PLCs) and remote terminal units (RTUs) can flow into advanced analytics software.
For example, Schneider Electric’s EcoStruxure ADMS combines SCADA, outage management, restoration, and DER management into a single dashboard. Likewise, sensors in pipelines, turbines, or transformers feed into cloud-based historians and data brokers (from vendors like OSIsoft/AVEVA, Siemens MindSphere, or AWS IoT) that standardize formats and make data findable.
As SCADA connects to the internet, IT and OT must be converged: cloud connectivity and IoT hubs can enable real-time optimization, but only with unified security and governance in place (more on this below). In short, modern energy platforms tie together SCADA telemetry, IoT sensor networks, and advanced data analytics into a single agile fabric, enabling true real-time insight into grid and plant operations.
Key Use Cases Driving Impact
Currently, some of the prominent use cases include:
- Predictive maintenance for turbines, pipelines, and substations
- Smart grid automation and real-time load balancing
- AI-driven forecasting for renewables (solar, wind) and demand planning
- Safety monitoring and automated risk detection
In each case, data and AI are turning traditional challenges—aging transformers, intermittent wind, remote sites—into opportunities for efficiency and resilience.
Data Governance and Security in the Energy Sector
With increased connectivity comes increased risk. Energy companies must evolve their systems to better secure assets and comply with regulations. Historically, OT was air-gapped, but today’s control systems are increasingly networked to IT databases and cloud services. This IT/OT convergence demands a unified security posture because grid devices become vulnerable to the same cyber threats that target enterprise networks as they go online. Energy companies must layer defense in depth: installing firewalls on substation networks, segmenting OT traffic, encrypting telemetry, and continuously monitoring for anomalies.
On the data side, governance is paramount. Energy companies often face data deluges across siloed systems. Without integration, disparate data platforms create blind spots, hindering effective decision-making and operational efficiency. For example, if outage data, feeder topology, and weather reports live in separate silos, operators lack a unified view to manage storms. The solution is a unified data model and governance framework: consolidating SCADA, AMI, GIS, and market data into common repositories with consistent tagging. Leading practice is to build a “unified namespace” or digital twin layer where all asset and sensor data are accessible in standard formats. This ensures data quality and consistency and streamlines compliance reporting (to regulators) and analytics across departments.
Building the Intelligent Energy Enterprise
The journey from vision to an intelligent enterprise involves several practical steps:
- Define Strategy and Use Cases. Align AI initiatives with business and sustainability goals—not just IT KPIs. What’s your most pressing challenge: downtime, forecasting, or carbon reduction?
- Align Stakeholders: Establish an executive steering committee to align priorities (green goals, reliability targets, customer service) so that IT, OT, and business strategies move in sync.
- Roll Out in Phases. Launch small proof-of-concepts, gather feedback, then iterate. The goal is to move rapidly from “sandbox” experiments to production applications within operations.
- Govern and Secure as You Go. From day one, embed data governance and cybersecurity into your rollout. Define clear data ownership, access policies, and security controls alongside each deployment.
- Invest in Change Management: Tools alone won’t transform your grid. Train teams, build trust in AI insights, and appoint cross-functional champions.
Ultimately, the intelligent energy enterprise is an achievable vision. It won’t happen overnight, but the path is clear.
If you need help, NRI can accelerate your digital transformation. We can help design your AI roadmap, unify disparate data sources into a single analytics platform, and ensure cybersecurity measures are in place from day one.
Schedule a custom consultation to learn more.
