Use case type

Document processing automation

Document processing automation extracts, classifies, redacts, or routes information from large volumes of documents. It addresses the need to reduce manual handling, improve accuracy, and protect sensitive data.

Use cases

8

Examples

8

Industries

6

Timeline

4 mo

Data updated just now

Adoption over time

Documented cases per month

By case publish month · completed months only

5 cases documented across 33 months (Oct 23 – Jun 26), peaking at 3 in June 2026.

3 so far in July 2026 (in progress, not charted)

Each column counts every documented case of this type by its publish month, across the full corpus. The in-progress current month is excluded from columns and surfaced separately, and cases published before the charted window are summarized as earlier cases instead of plotted.

Company examples

Use cases of this type

8 shown from 8 use cases

MicrosoftJul 11, 2026

EY automates K-1 tax data extraction with Azure AI Document Intelligence

Global financial service provider EY accelerated work for clients with Azure AI Document Intelligence.Tax work is timely and requires absolute accuracy, and the forms are complicated, are in various formats, and often span hundreds of pages.EY was an early adopter of Microsoft Azure AI Document Intelligence and integrated Microsoft OpenAI within a secure, cloud-native elastic Azure architecture to automate structured and unstructured K-1 tax data processing at scale.

Ernst & YoungProfessional Services
Jul 8, 2026

CareMates: AI-powered patient admission forms to automate elderly care intake on Amazon Bedrock

CareMates is a German health tech start-up focused on elderly care and social services admissions.It digitizes largely paper-based patient intake so relatives fill forms online and AI extracts the relevant data into digital records.

CareMatesHealthcare
MicrosoftJul 7, 2026

DTI Group uses Azure AI Document Intelligence and Microsoft Foundry to deliver agentic document processing (COGNAiO)

DTI Group, based in Switzerland, built COGNAiO on Azure and Microsoft Foundry to analyze documents via meaning and context, validate information, and connect outputs to downstream systems.The platform supports document-heavy workflows across regulated sectors and is designed to automate extraction and validation across documents, emails, images, and data feeds.It uses Azure Document Intelligence, Azure Kubernetes Service, and Azure Database for PostgreSQL, and allows users to describe processes in natural language rather than code.

DTI GroupProfessional Services
Jun 30, 2026

IBS Software builds bilingual NER for cargo logistics emails using Amazon Bedrock managed distillation

IBS Software's cargo system processes thousands of bilingual cargo logistics email messages daily, extracting critical information such as air waybill numbers, flight details, weights, and delivery instructions in English and Japanese.The team built a production-ready bilingual named entity recognition solution to identify 23 entity types across the two languages while keeping inference cost low and supporting real-time processing.

IBS SoftwareLogistics
Jun 24, 2026

Huntington Bank: Redacting sensitive data from 400M+ documents with AWS

Huntington National Bank used AWS services to redact sensitive customer data across a repository of more than 400 million on-premises documents.The solution moved files into Amazon S3, used Amazon Textract and AWS Step Functions to detect and process sensitive fields at scale, and then replicated redacted outputs back to on-premises storage.The program was designed to meet strict PCI DSS and access-control requirements while handling varied document formats.

Huntington National BankFinance
GCPJun 3, 2026

HUB24: Using Document AI and Vision AI to extract advisory document data and reduce financial advice compliance cost

HUB24 Limited built Google Cloud-based document classification and extraction capabilities to manage financial advice compliance obligations and reduce the cost of advice.The solution uses Document AI and Vision AI to classify 21 document types, extract key fields such as investment recommendations, risk profiles, and fees, and save the data into warehouses for reporting and real-time comparison against outcomes.

HUB24 LimitedFinance
May 5, 2026

Kofile modernizes county records with AI on AWS (document intelligence with Amazon Bedrock)

Kofile Technologies modernized county records management for more than 3,000 county governments using AWS.The platform processes millions of historical public records with automated classification, semantic search, translation, analytics dashboards, and strict security controls.

Kofile TechnologiesPublic Sector
GCPOct 24, 2023

Orby AI automates document-centric processes with Document AI processors

Orby AI helps organizations automate document-centric repetitive tasks such as contract processing, email processing, and invoice validation.The platform uses Google Cloud Document AI processors, including Enterprise Document OCR and Form Parser, to extract text, layouts, key-value pairs, and tables, then learns customer workflows and generates automation suggestions after human validation.Orby AI follows an observe, learn, and automate approach that improves downstream recommendations and becomes more accurate over time from human feedback.

Orby AIOther

Common questions

Document processing automation at a glance

How many document processing automation use cases are documented?
The AI Use Case Hub documents 8 real document processing automation deployments across 6 industries, with 8 detailed company examples you can browse.
Which industries adopt document processing automation the most?
Document processing automation is most common in Finance (25%), Professional Services (25%) and Logistics (13%).
Which countries lead in document processing automation?
United States leads documented document processing automation deployments, followed by Switzerland and Germany.
What technologies are used for document processing automation?
Teams most often build document processing automation with Amazon S3, Amazon Bedrock and Azure AI Document Intelligence.
What AI capabilities power document processing automation?
Across the documented deployments, the most common capability patterns are Agent (13%), Multi-agent (13%) and Vision (13%).
What results do companies report from document processing automation?
Across the 8 deployments reporting outcomes, companies most often cite cost efficiency (63%), risk & compliance (50%) and speed & agility (50%). Where impact is quantified, the strongest evidence is in time & speed: a median −81.7% across 4 reported metrics.