How E.ON uses SAP S/4HANA to modernise the grid with AI

Standardising grid data through SAP S/4HANA allows E.ON to modernise infrastructure and execute AI deployments.

The utility giant manages infrastructure across three distinct domains: energy grids, customer solutions, and energy infrastructure solutions. Maintaining operations across this scope requires continuous capital expenditure on IT hardware and software maintenance.

Leadership initially questioned the business case supporting large-scale technology spending. The engineering team proved that persistent financial investment guarantees system stability, affordability, and resilience within a digitised energy network.

E.ON prioritises growth, sustainability, and digitalisation as primary corporate objectives. Falling behind in technical capabilities carries long-term financial costs.

Infrastructure standardisation drives uptime

E.ON executes a cloud ERP migration alongside its SAP S/4HANA implementation. Legacy ERP systems in the utility sector often suffer from extreme customisation. The engineering department rejects fragmented custom builds to avoid this technical debt. Developers integrate established software packages directly into a cohesive architecture. This design methodology guarantees data scalability across the enterprise.

The focus on foundational infrastructure delivers highly visible production outcomes. E.ON reports a 77 percent reduction in IT downtime over a five-year period. Achieving these uptime metrics requires standardising data tables and removing redundant middleware from the technology stack.

SAP S/4HANA uses an in-memory database architecture. This design choice accelerates query processing times compared to legacy relational databases. The utility provider leverages this speed to process telemetry data streaming from grid assets in real-time. Fast data processing serves as the prerequisite for deploying any machine learning models against operational data.

Technology leaders face intense pressure to match the pace of external software development. E.ON CIO Sebastian Weber notes this pressure creates tension. Consumer software sets expectations for enterprise application deployments. Weber finds consumer AI applications like ChatGPT solve domestic problems effectively, creating internal demands for similar workplace automation. The energy company must close the gap between external software capabilities and internal readiness.

Internalising data and cybersecurity operations

E.ON treats internal readiness as a primary business objective. The company expanded its internal engineering teams aggressively and hired over 1,000 specialists to bring technical capabilities in-house. The recruitment drive secured more than 500 data experts and 300 cybersecurity professionals.

Bringing data engineering in-house allows the utility provider to build proprietary data lakes and audit data governance internally. Retaining internal cybersecurity talent ensures the company maintains strict access controls over the operational technology systems managing the physical energy grid. Engineering now acts as the primary vehicle for achieving commercial targets in the European green energy sector.

Of course, managing digital ecosystems at this volume requires strict oversight. The technical team establishes centralised governance structures across all business units. Administrators deploy standardised contracting frameworks and unified IT system management consoles.

Having such an administrative architecture in place enforces security standards and cost discipline without restricting feature development. Standardising vendor contracts accelerates software procurement timelines while capping runaway licensing costs.

Deprecating isolated innovation hubs

Enterprises often isolate experimental technologies in separate business units. E.ON completely abandoned this methodology and deprecated experimental garages and isolated digital labs. Management integrates digital tools directly into active business processes.

Keeping innovation teams separated from production environments often prevents applications from surviving the transition to live servers. By forcing developers to build within the core architecture, the engineering department guarantees production viability.

“Bringing the system up to speed requires internal readiness,” explained Weber. “It means we must think deeply about investments, prioritisation, and most importantly, people and culture.”

Weber expects the operational velocity to remain high, noting the company will not return to previous delivery speeds. New software deployments require precise alignment with business requirements.

E.ON enforces a “BizDevOps” operating model. This framework forces developers to build features that generate exact commercial value. Engineers collaborate directly with business analysts during the initial architecture phase.

This methodology is paired with targeted employee training. Line workers and managers receive specific instruction on operating newly-deployed tools. This capacity building ensures staff can extract verifiable value from the modernised infrastructure.

E.ON is taking a pragmatic approach to AI

E.ON manages its AI deployments with deliberate caution and refuses to build proprietary AI platforms from scratch. Instead, leadership prefers to leverage partnerships with established technology vendors. This procurement strategy maintains flexibility across the corporate software portfolio.

Engineers explore specific, bounded use cases for machine learning applications. The technical roadmap targets customer service automation, predictive maintenance, and operational optimisation.

Applying predictive maintenance algorithms to energy grids prevents catastrophic hardware failures. Sensors detect voltage anomalies and transmit the data back to the central S/4HANA instance. Machine learning models analyse this telemetry to identify wear patterns on physical infrastructure. Maintenance crews receive automated dispatch orders before the equipment actually fails. This active mitigation strategy reduces emergency repair costs and prevents localised power outages.

Testing these applications via third-party providers prevents the company from overcommitting capital to unproven frameworks. E.ON embeds these automation features directly into core systems rather than treating them as optional add-ons. The technology serves a customer base of 47 million users. Processing user requests through automated customer service workflows reduces call centre loads and accelerates incident resolution.

“In essence, our experience highlights a broader truth about digital transformation,” Weber noted. He explained that pushing new software to production cannot compromise system stability, cybersecurity, or governance frameworks.

Without proper alignment with business requirements, advanced technologies fail to deliver value. The modernised architecture provides E.ON with the necessary foundation to scale green energy infrastructure reliably.

See also: Walmart’s AI workflows meet the realities of the balance sheet

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