Invoice processing · Production

ABBYY Purpose-built AI Helps Scale and Streamline Invoice Processes Across 20 Markets and 14 Languages

The problem

A multinational snack food supplier struggled to scale invoice-to-pay processes across its global markets and multiple languages, with AP rules stored as undocumented tribal knowledge and invoice volume growing faster than headcount could manage.

Workflow diagram · grounded in source
1
Invoice intake across markets
Trigger
Invoices arrive across 20 markets in 14 languages with widely varying formats and AP business rules.
▾ source quote
“AP business rules and workflows varied widely across 20 markets in 14 languages—stored primarily as local tribal knowledge rather than formally documented”
2
NLP semantic analysis
Ai action
Natural language processing (NLP) analyzes the semantics of invoices to help the ERP make sense of the many variations of invoice content.
▾ source quote
“Natural language processing (NLP) analyzes the semantics of invoices to help the ERP make sense of the many variations of invoice content”
3
ML model training and inference
Ai action
Machine learning (ML) trains AI models on all different document types and uses inference to process new invoice formats.
▾ source quote
“Machine learning (ML) helps train AI models on all the different types of documents received and uses inference to process new invoice formats”
4
RPA downstream integration
Integration
The organization's robotic process automation (RPA) leverages the AI in ABBYY IDP components to streamline the flow of invoices and data downstream.
▾ source quote
“The organization's robotic process automation (RPA) leverages the AI in ABBYY IDP components to help streamline the flow of invoices and data downstream”
5
Straight-through ERP posting
Output
Many more invoices are posted directly to the ERP.
▾ source quote
“many more invoices posted directly to the ERP”
Reported outcome

ABBYY IDP delivered faster invoice approval times with greater accuracy, sending a much higher percentage of invoices straight through to the ERP, and automated and standardized AP processes globally.

Reported metrics
Headcount growth avoided50% or 75% more people
Invoice approval timefaster invoice approval times
Invoice processing accuracygreater accuracy
Straight-through processing ratemuch higher percentage of invoices straight through to the ERP
Reported stack
ABBYYIDPNLPMLRPAERP
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Source
https://www.abbyy.com/customer-stories/streamline-global-invoice-processing-abbyy-ai/
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Frequently asked questions

What did this team achieve with this AI workflow?

ABBYY IDP delivered faster invoice approval times with greater accuracy, sending a much higher percentage of invoices straight through to the ERP, and automated and standardized AP processes globally.

What tools did this team use?

ABBYY, IDP, NLP, ML, RPA, ERP.

What results were reported?

Headcount growth avoided: 50% or 75% more people; Invoice approval time: faster invoice approval times; Invoice processing accuracy: greater accuracy; Straight-through processing rate: much higher percentage of invoices straight through to the ERP (source-reported, not independently verified).

How is this invoice processing AI workflow structured?

Invoice intake across markets → NLP semantic analysis → ML model training and inference → RPA downstream integration → Straight-through ERP posting.

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