Siemens AG saves €5 million annually automating delivery note processing with AI at 98% accuracy

Background

Siemens AG's global supply chain relies on accurate, timely processing of delivery notices from more than 1,000 vendors. Each delivery carried vital data — part numbers, quantities, delivery timelines — but required manual entry into SAP by staff who had to decipher inconsistent formats, smudged printouts, and non-standardized digital attachments. The manual model created repetitive work, operational risk from errors, and an inability to scale during peak volume.

What Was Implemented

• Autonomous multi-agent AI system (DeepOpinion / Otera) deployed to process delivery notes end-to-end
• LLM technology to interpret documents across unlimited, diverse supplier layouts
• Real-time validation of delivery note data against SAP purchase orders
• Exception-only human escalation with full audit trail
• Integration with SAP ERP to post verified data without manual intervention

Results

The implementation achieved 98%+ accuracy across all document layouts and over 90% touchless processing within two weeks of going live. Document cycle times compressed from days to minutes, enabling same-day reconciliation. Siemens reportedly saves €5 million annually (unverified in the current Otera page text; sourced from the book's reference to the original DeepOpinion case study). Staff transitioned from repetitive data entry to managing and optimizing the AI-driven process.

Lessons

• LLM-based document AI can significantly outperform custom-built OCR pipelines for variable-format documents.
• Setting accuracy benchmarks against the AI system (rather than legacy systems) signals a genuine shift in operational trust.
• Rapid time-to-value (90%+ automation within 2 weeks) is achievable when the AI is pre-trained on millions of similar documents before deployment.