Why Executives Should Care About Context Instrumentation
AI in SAP is often discussed in technical terms, but the real story is business impact. This is about speed, cost, and scalability.
AI in SAP is often discussed in technical terms, but the real story is business impact. This is about speed, cost, and scalability.
For the past few years, AI adoption in enterprises has been driven by one idea: write better prompts. In SAP environments, that phase is ending. We are entering a new paradigm: context instrumentation, where systems are designed so agents can build context and take action autonomously.
If you're building CAP or RAP applications, you've probably asked ChatGPT to find APIs in the SAP Business Accelerator Hub.
I tested ChatGPT with a straightforward request and found three distinct failure patterns that make general-purpose LLMs problematic for SAP API discovery.

We spent months studying the patterns in mistakes LLMs make while writing ABAP code. While many teams frame the risks in terms of "hallucination", we've learned that hallucination is not the core issue in ABAP.
The real problems stem from how LLMs generalize programming patterns and how ABAP fundamentally differs from mainstream programming languages.

This blog breaks down: