United StatesFood and restaurants2025 – ongoingSchreiber Foods
AI invoice matching and exception handling
Schreiber Foods reported an 80% reduction in European median invoice cycle time with OpenEnvoy
Problem
Finance employees maintained increasingly complex invoice-matching instructions as supplier formats changed. Routine matching work consumed time needed for financial oversight and exceptions.
Approach
Schreiber applied OpenEnvoy AI to payable and receivable processes. Models learned from how employees resolved exceptions during production work. Finance staff retained responsibility for approval policies and audit controls. Difficult invoices continued to require human judgment.
Results
Takeaway
The reported speed improvement covered European invoices with stable overall volumes. Some invoices still required several working days of human handling.
Categories
- Size
- Large enterprise
- Industry
- Food and restaurants
- Business function
- Finance, risk and legal
- Innovation type
- Process innovation
- AI technology
- Predictive analytics and ML
- Adoption
- Vendor solution
- Evidence
- Company's own claim
Sources
Source summaries
Schreiber reported an 80% decline in European median invoice cycle time and a 160% increase in U.S. automation. It described models trained on employees' exception decisions and continued human responsibility for financial controls.
Full details are in the original sources.