AI Needs Context: Why High-Quality SAP Master Data Is Crucial for Successful AI Implementation

01.08.2025

Topic: AI and Machine Learning | SAP

The digitalization of business processes is accelerating at an unprecedented pace—and artificial intelligence (AI) is playing a central role in this transformation. In particular, generative AI (GenAI), powered by large language models (LLMs), is opening up new possibilities in document processing and process automation within SAP environments. But as powerful as this technology may be, its effectiveness depends on one key factor: the quality of your data. More specifically: the quality of your SAP master data.

From Traditional AI to Generative AI

In the past, traditional AI in document management focused primarily on automating decision-making based on explicitly available data. GenAI goes a step further: it understands context, infers missing information, and proposes decisions—even when not all details are present. This makes it a game-changer for SAP-based processes.

Take invoice processing, for example. A conventional OCR-based AI might extract text from an invoice. GenAI, on the other hand, understands the process behind it. It identifies which additional details are required in SAP for downstream tasks—even if they’re not directly visible on the document.

Context Awareness Requires Data Awareness

For effective support in invoice processing with SAP, the following information is already suggested during document reading on the basis of GenAI:

• Purchase order type
• Internal material and document numbers
• Type of goods receipt (goods vs. services)
• Linkage to service entry sheet or goods receipt
• Account assignment details (cost center, G/L account, internal order)
• Responsible approvers

These intelligent suggestions reduce manual workload for experienced accounting teams—and act as a digital mentor for new employees by embedding expert knowledge directly into the process.

But how does GenAI know what to suggest? The answer lies in your ERP history—specifically in transaction data and, most importantly, your SAP master data.

Master Data: The Foundation of Intelligent Processes

GenAI learns from examples. It recognizes patterns, correlations, and contextual relationships. But this only works if the underlying SAP master data is accurate, complete, and consistent. Poor-quality data leads to flawed suggestions, which in turn require costly corrections or may even cause process failures.

If you’re serious about leveraging GenAI, you need to start with a rock-solid data foundation. That means:

• Consistent material and product master records
• Up-to-date supplier and customer master data
• Proper maintenance of account assignment objects
• Clear and enforceable workflows and role definitions in SAP

Even the most advanced GenAI model cannot make smart suggestions if the data it relies on is flawed—or worse, misleading.

Process Knowledge and Data Quality Must Go Hand in Hand

In addition to high-quality data, domain expertise is crucial. GenAI can only deliver meaningful value if it understands how to interpret the information it sees. That requires deep SAP knowledge and a firm grasp of business logic—both during solution development and throughout deployment.

Conclusion: Master Data Quality Has Become Mission-Critical

Master data has always been important—but in today’s AI-driven business landscape, it has become mission-critical. As SAP CEO Christian Klein emphasized at SAPPHIRE in May: data, applications, and AI together form the “flywheel” that will drive the digital enterprise of the future.

Our advice: Invest in your data quality. Because only with clean, structured, and consistent SAP master data can GenAI reach its full potential—and help transform digitalization into true business transformation.