Energy companies face pressure to secure critical infrastructure, improve operational resilience, and navigate volatile markets with real-time insights. At the same time, AI adoption across the energy sector is accelerating—placing new demands on data architectures that were never designed to support advanced analytics or machine learning at scale.
To begin making an impact with AI, energy organizations need more than data access. They need a unified, governed view of their most critical data and the ability to bridge traditionally siloed IT and OT systems in order to unlock new operational capabilities that improve reliability, efficiency, and long-term performance.
AI in the Energy Sector Requires a Strategic Data Approach
AI optimization in the energy sector depends on the ability to integrate and interpret massive volumes of industrial data tied to physical assets and real-world processes. Without context—such as asset hierarchies, metadata, and time-series data—AI models struggle to produce reliable or actionable outcomes.
To address this, energy organizations are adopting an Industrial DataOps approach to standardize, contextualize, and govern data before it reaches analytics and AI platforms. This approach requires seamless collaboration between ecosystem partners and the energy organizations they serve.
HighByte + Snowflake: Bridging Industrial Data and the AI Data Cloud
Snowflake’s Energy Solutions for the AI Data Cloud help energy organizations modernize how data is accessed, shared, and activated across oil and gas, power, utilities, and renewables. The solutions deliver the scale, security, and governance required to support AI-driven analytics across the enterprise.
HighByte Intelligence Hub supports this initiative by ensuring industrial data from IT and OT systems arrives in Snowflake with the structure and context AI requires. Deployed at the edge, HighByte Intelligence Hub standardizes data at the source—eliminating brittle pipelines and reducing reliance on custom code. Together, Snowflake and HighByte help energy organizations:
- Unify IT, OT, and IoT visibility: Consolidate business, operational, and market data into a secure platform for real-time insight across exploration, production, transmission, and distribution, asset performance, trading and risk management, and customer operations.
- Provide AI-driven asset health and performance: Apply AI and ML models to detect anomalies, reduce unplanned outages, and optimize operations and maintenance using long-term operational and sensor data.
- Improve safety, efficiency, and emissions outcomes: Combine field and enterprise data to reduce downtime, streamline operations, and support the protection of people, assets, and the environment.
- Secure, governed infrastructure at scale: Maintain data consistency, lineage, and compliance while scaling AI initiatives across complex and regulated energy environments.
- Collaborate across the energy ecosystem: Share data securely with suppliers, regulators, asset operators, and service partners using Snowflake Marketplace and native sharing capabilities.
AI-Driven Energy Use Cases
Organizations are already realizing measurable value from HighByte and Snowflake across upstream operations, power generation, and utilities. They are improving asset performance, modernizing manufacturing and grid operations, and enabling faster, more informed decision-making.
The examples below highlight real-world applications where customers are building a scalable industrial data architecture and delivering contextualized data into Snowflake to support AI use cases today.
- ConocoPhillips unified IT and OT data from multiple historians and SCADA systems into a standardized model delivered into Snowflake. By contextualizing operational data and reducing pipeline complexity, the team is able to accelerate AI and ML use cases to optimize well performance across global assets.
- Siemens Gamesa uses HighByte Intelligence Hub to construct and operate a unified namespace that provides engineers and business users with easy access to manufacturing process data. Standardized OT data delivered into Snowflake supports scalable Industry 4.0 and AI-driven manufacturing initiatives across sites.
- National Grid implemented the Intelligence Hub to deliver “real-time” PI System data into Snowflake, enabling AI-driven forecasting, asset performance analysis, and grid optimization—while reducing latency compared to legacy batch pipelines.
Delivering AI-Ready Industrial Data at Scale
As AI becomes embedded in day-to-day energy operations, success will be defined by how quickly organizations can move from insight to action—without sacrificing trust in the data behind those decisions.
By delivering industrial data with the Intelligence Hub into Snowflake with the structure and context required for AI, energy teams can reduce time spent preparing data, accelerate analytics and AI initiatives, and make faster, more confident operational decisions. Engineers, operators, and business leaders gain a shared foundation that supports real-time visibility, predictive insight, and scalable innovation across the energy value chain.
