After the SAP S/4HANA implementation comes the AI transformation

andreas.halusa

Oct 10

4 min read

What It's About

Laying the Foundation for AI: A successful S/4HANA transformation is the first step. Only high-quality, consistent, and business-understandable data enables the reliable use of AI.

Data Quality as a Key Success Factor: Incorrect, incomplete, or outdated data can compromise AI results. A systematic data quality analysis lays the foundation for reliable insights and well-informed decisions.

Enabling Sustainable AI: dataXcellence AG helps companies analyze, improve, and permanently secure their data in a targeted manner—to create a robust foundation for successful AI applications.

The next phase of transformation has begun

Many companies have successfully completed the migration from SAP ECC to SAP S/4HANA. Business processes have been modernized, systems consolidated, and data migrated.
At the same time, SAP is increasingly integrating artificial intelligence into business processes and applications. Solutions such as SAP Joule and AI agents are designed to support employees, improve decision-making, and make processes more efficient.
For AI to effectively support business processes, it requires more than just powerful models: it needs reliable data, domain-specific context, and clear governance.

SAP provides further information in the description of the SAP Business AI Platform.

This raises a crucial question for companies: Is the existing database ready for the productive use of AI?

S/4HANA is an important milestone—but no guarantee of good data

A successful S/4HANA implementation establishes a modern technological foundation. However, it does not automatically guarantee that the data is complete, consistent, and business-accurate.
In a system conversion, an existing SAP system is technically migrated to S/4HANA without necessarily involving a comprehensive business data cleansing process.
A greenfield migration, on the other hand, offers the opportunity to cleanse data in a targeted manner and start with high data quality. Over time, however, this quality can decline due to new data, changed processes, and inadequate maintenance.

Data quality is therefore not a one-time task, but an ongoing process. Regular checks and targeted improvements lay the foundation for reliable analyses, well-informed decisions, and AI applications.

Why AI Is Changing Data Quality Requirements

Experienced employees often recognize when numbers are implausible or results do not align with the business. They are familiar with processes, understand exceptions, and can question contradictory information.

AI applications analyze large amounts of data, identify patterns, generate forecasts, and support business processes. The quality of their results depends largely on the available information and its business context.

Typical data quality issues include:

Incomplete data: Missing attributes make analysis difficult and lead to incomplete results.
Duplicates: Multiple entries for the same business partner or material can skew key performance indicators.
Inconsistent data: Conflicting information leads to inconsistent results.
Outdated data: Out-of-date information can lead to inappropriate recommendations.
Erroneous data: Incorrect values can compromise analyses and downstream processes.

However, not every anomaly is an error. An unusual value may also reflect a rare, business-relevant situation. Therefore, data issues must be evaluated by experts and addressed in a transparent manner.

Many companies are already exploring specific AI use cases: Where can AI help reduce the workload on employees? How can forecasts be improved and decisions be made on a more informed basis?

With solutions such as the SAP Business AI Platform, the SAP Business Data Cloud, and the SAP Knowledge Graph, SAP aims to make corporate data more accessible to AI and to map its business contexts.

However, before this potential can be fully realized, the data foundation must be sound. After all, AI is only as reliable as the data on which it is based and the business context in which it interprets that data.

Building this foundation of reliable data quality involves five key steps:

Ensuring Data Quality Transparency: Systematically analyze data sets, highlight quality issues, and determine the need for action in a measurable way.
Detect and Evaluate Anomalies: Identify duplicates, missing values, inconsistencies, and outdated information, and classify them according to business context.
Targeted Data Improvement: Prioritize quality issues and resolve them where they have a significant impact on business processes and planned AI applications.
Ensure sustainable data quality: Establish clear responsibilities and regular checks to maintain quality even as changes occur.
Prepare data for AI: Ensure that data sources, business contexts, quality requirements, and access rights are appropriate for the specific use case.

The foundation for successful AI therefore consists of high-quality data, a clear domain context, and a sustainable approach to ensuring data quality. Companies that establish these fundamentals early on improve their ability to deploy AI applications in a reliable and transparent manner that delivers real business value.

How dataXcellence AG makes Companies AI-Ready

We help companies systematically analyze their data quality, identify weaknesses transparently, and develop targeted improvement measures.

Our approach combines three perspectives:

Analyze: Reveal the actual state of the data and quantitatively identify quality issues.
Improve: Address errors, duplicates, and inconsistencies in a targeted manner and classify them correctly from a business perspective.
Ensure: Establish rules and controls to maintain data quality over the long term..

AI can also help identify unusual patterns and prioritize potential errors for expert review. A transparent evaluation of the results remains crucial.

In this way, data quality evolves from an isolated IT issue into a strategic building block of digital transformation.

Conclusion: First the foundation, then the next step

The S/4HANA transformation lays the foundation for a modern IT landscape. The next step is to leverage this foundation for AI-powered analytics, intelligent assistance systems, and increasingly automated business processes.

To do this, companies need to know whether their data is sufficiently reliable, whether business relationships are clearly represented, and whether quality issues are identified in a timely manner.

A systematic data quality analysis creates transparency. Targeted improvements reduce vulnerabilities. Continuous monitoring ensures quality in the long term.