1. Introduction

In today’s fast-paced business environment, enterprises are continually seeking ways to reduce manual work and enhance process efficiencies. One challenge is handling scanned documents. Even within robust systems like SAP, many processes involve scanning physical documents, such as invoices, contracts, or purchase orders, then ingesting them manually. Labor-intensive workflows lead to delays, errors, and inflated costs.

This paper explores a practical integration between SAP and a Python-based AI agent that transforms scanned PDF documents into actionable data. By leveraging SAP’s inbound email attachment processing capabilities and coupling them with AI-driven text extraction, companies can accelerate document processing workflows to drive efficiency in operations like sales order creation. The proposed system creates a pipeline that extracts the scanned PDF attachment from inbound email, converts it for AI consumption, interprets its contents, and reintegrates the recognizable text into SAP, to reduce manual intervention.

The real innovation here lies in bridging the gap between legacy process steps and modern AI capabilities. The following sections detail our research into the system design, implementation, and operational benefits of this integration.

2. Background

2.1. Process Challenges

Many organizations face persistent issues with manual document posting for example manual order creation in SAP, particularly when dealing with variable-quality PDFs:

• Manual Data Entry: This introduces avoidable human errors and takes valuable staff time.

• Inconsistent Document Quality: Scanned documents are often noisy or skewed, complicating automated extraction.

• Delays and Inefficiencies: Processing bottlenecks can affect revenue recognition and customer service.

2.2. Value Proposition of Automation

By automating this process, businesses stand to gain:

• Accuracy: Automated extraction ensures consistency, reducing the risk of costly mistakes.

• Speed: SAP orders are processed and fulfilled more quickly.

• Standardization: End-to-end processing becomes uniform, supporting operational scalability.

• Structured Error Handling: Entries with missing or ambiguous data are flagged for manual attention, ensuring nothing slips through the cracks.

Our solution brings together SAP’s enterprise-strength email handling with a modern Python extraction service, supporting a streamlined order-to-cash cycle.

3. System Architecture and Workflow

The architecture integrates SAP’s email processing and a Python-based document analysis service using secure REST APIs. The end-to-end process comprises:

1. Email Reception & Attachment Extraction: SAP monitors a designated inbox and extracts PDF attachments using standard interfaces.

2. Transmission to Python Service: PDF data is securely posted to an external Python service.

3. Document Analysis: The Python service decodes, applies OCR/NLP, and returns extracted DATA in readable format.

4. Validation & Order Creation: SAP validates the returned data, either creating the order or logging an error for follow-up.

5. No Use of ADS: The process is fully SAP native, and Python based avoiding additional middleware dependencies.

3.1. High-Level Business Process Flow

Figure 1. Business Process Flow

4. Implementation Details

Note: Below code is for reference purpose – it is not complete code.

4.1. SAP Implementation [1] [2]

Setup SMTP inbound message, inbound processing using T-Code SCOT

Copy class CL_EPM_ADS_INBOUND_LIST to ZZ_ EPM_ADS_INBOUND_LIST and assign to communication type “Internet Mail” and document class “PDF”

4.1.1. Email Monitoring and Extraction and Middleware Integration

Implement Method CREATE_INSTANCE

Implement method PROCESS_INBOUND to read email and extract attached PDF data:

4.1.2. Sales Order Creation and Error Handling

After receiving the JSON response, use the data to create sales order:

4.2. Python Service [2] [3]

A lightweight Python web service (Flask or FastAPI) is used for PDF processing:

4.3. Design without ADS

This solution is intentionally designed without requiring SAP ADS, keeping the architecture lightweight and reducing operational overhead. SAP’s built-in email and HTTP capabilities, paired with a dedicated external extraction service, ensure robust and maintainable integration.

5. Security, Testing, and Performance

5.1. Security

• Encryption: All data exchanges use HTTPS.

• Access Controls: Endpoints are protected via authentication tokens and SAP’s standard authorization checks.

• Logging: Both SAP and Python services keep detailed logs for traceability.

• Data Integrity: Careful error handling and validation at each step protect against data corruption.

5.2. Testing

• Unit Testing: Individual functions are tested separately in both ABAP and Python.

• Integration Testing: The end-to-end flow is validated using sample documents.

• Load and Fault Tolerance: The solution is tested under high volume and with intentionally malformed data to ensure resilience.

5.3. Performance

• Scalability: Both SAP and the Python service can be scaled horizontally.

• Caching: The architecture supports concurrent requests and temporary caching if needed.

6. Challenges and Opportunities for Improvement

6.1. Existing Limitations

• Document Quality: Poor scan quality still presents challenges for OCR.

• Flexible Field Extraction: Ongoing enhancement is required for robust parsing of non-standard document layouts.

• Processing Time: Complex OCR/NLP steps may introduce some latency under heavy loads.

6.2. Future Directions

• Advanced ML Models: Introducing deep learning could boost accuracy for both OCR and field extraction.

• Real-Time Processing: Moving to event-driven processing for lower latency.

• Wider Document Types: Supporting handwritten or mobile-generated POs.

• Proactive Correction: Leveraging order history to suggest corrections for incomplete fields.

7. Conclusions

This paper outlines a practical and robust method for automating SAP sales order creation from emailed PDF documents, leveraging only standard SAP and Python integration techniques. The architecture is designed for real-world enterprise environments—delivering accuracy, speed, and operational efficiency without increasing system complexity. The proposed solution reduces manual workload, increases reliability, and lays a scalable foundation for future enhancements in order automation.