Preparing Your SAP Data for AI Initiatives
- October 16, 2026
SAP solutions capture information across business operations, from customer and supplier records to the transactions that support financial reporting. While that information can support valuable AI initiatives, years of transactions don’t automatically provide the reliable data an AI solution needs. Preparing it starts with understanding how it will be used and where its limitations could affect the result.
KEY TAKEAWAYS
- Start with a defined AI use case and identify the SAP data needed to support it.
- Profile critical records for completeness, consistency, duplicates, and freshness.
- Preserve business context, including units, currencies, document relationships, and status definitions.
- Connect SAP and non-SAP sources through governed, traceable data pipelines.
- Establish ownership and access controls before exposing information to AI applications.
- Test business outcomes and monitor data readiness continuously after deployment.
Preparing SAP data for AI means making relevant information accurate and understandable, with governed access that supports a specific business purpose. Addressing those foundations helps organizations enter pilots with clearer expectations about what the solution can achieve and what it will take to move into production.
Take a closer look at how you can prepare your SAP data for AI initiatives below.
HOW TO PREPARE YOUR SAP DATA FOR AI
Start With the Business Question
Data preparation becomes easier to prioritize when the intended outcome is specific. A broad goal such as “use AI in supply chain” offers little direction, while “predict which purchase orders are likely to arrive late” gives the team a defined problem to investigate. Understanding who will use that prediction helps establish how it should inform their work.
That business question should guide the data requirements. A late-delivery prediction might draw on purchase order details and supplier records, using promised dates alongside actual receipt events to assess delivery performance. Relevant external signals may help explain disruptions, while an assistant answering order-status questions needs current information that the user is authorized to access.
The broader risk is significant, with Gartner predicting that organizations would abandon 60% of AI projects unsupported by AI-ready data through 2026. That figure is a forecast about projects lacking suitable data, rather than an observed failure rate for all AI initiatives.
Before building the pilot’s dataset, define the expected improvement and acceptable error levels, including when a result should require human review. Those criteria give the team a practical basis for deciding whether the data is ready.
Assess the SAP Data Landscape
Once the use case is defined, identify where the required information resides. Relevant records may sit in SAP S/4HANA or older SAP solutions, with additional information held in SAP BW or connected applications such as SAP Ariba.
Document each dataset’s owner and business definition so the team understands what it represents. Its refresh frequency and access restrictions will help determine how it can be used, while known limitations should inform the pilot’s design. When information appears in several places, establish which source is authoritative.
The assessment also needs to explain how records connect across systems. Different customer identifiers can make it difficult to reconcile transactions, while inconsistent units of measure can distort material comparisons. A supplier record may represent an individual location rather than its parent company, affecting how performance is assessed even when the record appears complete.
SAP’s February 2026 guidance on building a data foundation for business AI recommends assessing the existing data landscape before preparing it for business AI, including identifying data owners and investigating integration gaps.
Improve Data Quality Where It Matters Most
Focus quality checks on the fields that influence the AI solution’s output. Missing identifiers or duplicate business partners can undermine record matching, while invalid dates and inconsistent codes can distort analysis. Stale records or broken document relationships may create further gaps in the information available to the solution.
For a purchasing model, the distinction between a supplier’s promised delivery date and the actual receipt date is particularly important. If teams populate those fields inconsistently, the model may learn patterns that reflect recording practices rather than supplier performance.
Prioritize corrections by business impact, assigning exceptions to data owners and documenting how they’re resolved. Automatically filling missing values or deleting unusual records can remove useful information: a missing date may indicate an incomplete process, while an outlier may reflect a disruption the model needs to recognize.
The pilot’s quality thresholds should reflect the consequences of an incorrect result. Monitoring completeness and duplicate rates can reveal problems with the records themselves, while checking document relationships and data freshness helps establish whether those records remain useful for the task.
Preserve the Meaning Behind the Data
Reliable AI outputs depend on understanding what the data represents. Currency and unit definitions need to remain attached to the relevant values, while organizational boundaries and fiscal periods help establish where those values apply. Status definitions should distinguish ordered quantities from delivered quantities, with equivalent clarity around invoiced and canceled amounts.
A revenue analysis, for example, needs a clear definition of gross versus net amounts and an explanation of how returns are handled. If currency conversion has been applied, that treatment should also be visible so users can interpret the result correctly.
A shared glossary supports consistent interpretation, while tracing important fields back to their sources and transformations gives business users a way to investigate unexpected outputs.
Build Governed Connections Across Systems
How information moves should reflect how the AI application uses it. Model development may require historical datasets, while an assistant may need to retrieve current records when a user asks a question. Establishing those requirements first helps teams select an integration approach that fits the use case.
SAP’s 2025 release of SAP Business Data Cloud describes an approach to bringing SAP and third-party data together while preserving business context through managed data products.
Evaluate the approach against your existing technology environment and refresh requirements, then validate record counts and document relationships after integration. Transformations should preserve the data’s intended meaning, supported by accountable owners and tested quality controls.
Establish Ownership and Access Controls
Business owners should approve data definitions and intended uses, with data stewards responsible for resolving quality issues. Technical owners maintain the pipelines that make the information available to the application.
Access controls need to reflect both the user and the task. An assistant answering customer questions should retrieve only authorized information, with equivalent protections applied to derived datasets and search indexes. Logs and exports also need appropriate controls because they may retain information beyond the original interaction.
Permission to read information should remain separate from permission to change transactions. When an AI initiative includes updates to business records, explicit authorization needs to govern those actions.
Validate the Pilot and Monitor Production
Test the pilot against business scenarios that could challenge its assumptions. Cancellations and partial deliveries can reveal weaknesses in how records are interpreted, while missing or conflicting information can show whether the solution handles uncertainty appropriately.
For predictive models, evaluate performance on historical periods excluded from training, using only information that would have been available when each prediction was made. For assistants, assess answer accuracy and source traceability, including whether responses reflect current information. Testing across user roles should confirm that access restrictions work as intended.
After deployment, monitor pipeline failures and quality exceptions alongside the solution’s business outcomes. Changes in data patterns may require adjustments, so ownership and escalation paths should make clear who responds when performance deteriorates.
BUILD AI READINESS INTO YOUR SAP DATA STRATEGY
Preparing SAP data for AI continues as the business changes and new use cases emerge. Clear ownership helps teams maintain data quality, while preserved context and governed access support appropriate use of that information.
That ongoing work helps employees understand the decisions informed by AI and assess whether they can trust the results. Maintaining the foundation as AI capabilities grow gives organizations a more credible path from a promising pilot to dependable business use.