How can AI elevate sustainability reporting?

Across industries, sustainability reporting has entered a new era. Regulations such as the Corporate Sustainability Reporting Directive (CSRD) in the EU expand stringent disclosure requirements.

Companies must now deliver robust information about Environmental, Social, and Governance (ESG) topics with speed and accuracy. Traditional manual methods increasingly struggle to cope with the scale and complexity of data management now required.

Leveraging environmental data intelligence

To address these challenges, we combine environmental expertise with a robust data infrastructure, and AI-enabled data intelligence.

We are using this approach to support our own operations and processes – for example to assist water risk assessments across our sites or to fill in gaps when reporting on weight of substances in chemicals, materials and components.

AI tech augments human expertise to help us better manage our own environmental footprint.

AI assistant for water risk assessment

Environmental compliance assessments increasingly rely on complex datasets, detailed questionnaires and expert knowledge. This often results in time-intensive processes and strong reliance on individual expertise. We developed the Siemens Environmental Reporting Assistant (SERA) as a response to this challenge.

SERA supports our experts throughout the assessment process. It provides contextual guidance, interprets data, integrates external information, and performs plausibility checks. Where discrepancies arise, it challenges inputs while the final judgment always remains with the expert.

The approach shows how AI-based assistance can reduce assessment effort while preserving transparency, expert oversight, and auditability. The insights paper Pioneering environmental protection through data intelligence provides further details about our approach.

Filling data gaps in environmental reporting with AI

Managing hazardous substances is one of the most critical and complex responsibilities in industrial operations. It directly affects environmental compliance, supply chain integrity, and product safety. New requirements, such as those introduced by CSRD, also demand disclosures that have not been reported before.

One of the main challenges is that the raw data behind disclosures is often fragmented and scattered across a multitude of internal and external systems.

To solve this, we developed the Siemens Environmental Knowledge Graph (SiEKG). It connects data from different sources and embeds regulatory requirements (e.g., CSRD, SVHC, restriction lists) directly into the model. SiEKG enables complex environmental assessments and KPI calculations in real-world data environments. More information about SiEKG can be found in our insights paper Pioneering environmental protection through data intelligence .

Scaling environmental intelligence

We leverage AI not only to solve isolated operational challenges. Our AI-empowered approach enables scalable, reliable, and audit-ready environmental reporting across a rapidly evolving regulatory landscape.

The insights paper Pioneering environmental protection through data intelligence explores this topic in more detail.