2025 was intense for many SAP teams we worked with. Different industries, different landscapes, but surprisingly similar problems when it came to SAP AIF error handling.

What we kept seeing in real projects:

• Interfaces no one fully “owned” anymore

• Errors bouncing between teams because responsibility wasn’t clear

• The same issues coming back every month

• Fixes that should take minutes, taking hours or days

• A lot of knowledge locked in people’s heads, tickets, or old Confluence pages

None of this was caused by lack of skill. It was caused by scale, complexity, and manual processes that no longer fit today’s SAP landscapes.

A real example (one of many)

An invoice interface failed because of a missing value mapping. Classic AIF error.

What followed:

• Ticket created

• L1 analysis

• Escalation to L2

• Someone searched documentation

• Someone else checked configuration

• Fix applied

• Same error returned weeks later

Nothing “wrong” happened - but nothing improved either. That pattern repeated itself across:

• Finance postings

• IDoc integrations

• Proxy / RFC interfaces

• Country-specific eDocument scenarios

What we learned

1. Most AIF errors are predictable Not all, but many follow patterns: missing config, invalid master data, missing mappings, low quality of input data

2. Resolution knowledge already exists - just not where it’s needed In tickets, Jira, SharePoint, Confluence, chat messages… scattered and hard to reuse.

3. Humans shouldn’t be the first line of reaction anymore Especially for known, repetitive problems.

4. SAP finally has the building blocks to change this Document Grouding, Agents, Joule, AIF APIs - all inside the SAP ecosystem.

Where this led us

Instead of asking “how do we fix this error faster?” we started asking:

“Why does a human see this error at all?”

SAP AI agents for AIF monitoring

Customers started asking a simple question: “Can AIF tell us what to do before we open a ticket?” So we introduced an AI assisted AIF monitoring layer, built directly on top of SAP AIF.

AIF messages with AI predicted fields ( ex. Team Responsible ):

AIF error resolution supported by SAP AI

What it enables in practice:

• Automatic categorization of AIF errors

• Prediction of responsible team and direction

• Error explanations in human language (not SAP error codes)

• Resolution steps generated from past incidents and documentation

• Agent execution logs instead of ticket back-and-forth

• Learn from previous resolutions

• Involve a human only when needed

Behind the scenes, AI agents work with:

• SAP AIF context and message payloads

• Existing customer knowledge (Confluence, Jira, SharePoint)

• SAP AI Core + Joule + CAP/RAP applications

  
  

All inside the SAP ecosystem. The goal was to remove repetitive firefighting and let people focus on real problems. This is how AI starts to feel useful in SAP operations.

What we’re working towards in 2026

Based on what we faced in 2025 projects, our focus is on:

• Autonomous resolution of known AIF error patterns

• Real time reaction instead of ticket queues

• Using existing customer knowledge as a living knowledge base

• Predicting errors before business users notice them

• Keeping everything inside the SAP domain (no data leaving SAP)

  
  

If you’re responsible for SAP interfaces, AIF, or SAP operations and this sounds familiar - you’re definitely not alone. We are curious how others are approaching this in their landscapes.