The Four Data Capabilities SAP Customers Need to Trust GenAI Outputs

As SAP customers accelerate adoption of generative AI across ERP, analytics, and business process automation, a familiar challenge is re-emerging: AI outcomes are only as reliable as the data behind them. While much of the market conversation has focused on large language models, GPUs, and copilots, enterprise practitioners are increasingly finding that data readiness—not model sophistication—is the gating factor for GenAI success. According to NetApp, failures in AI initiatives are more often traced to fragmented data estates, governance gaps, and stale information than to algorithmic shortcomings.

For SAP landscapes, this challenge is amplified. Core transactional data in SAP S/4HANA, historical data in legacy systems, and rapidly growing volumes of unstructured content—documents, logs, contracts, product documentation, and customer interactions—must all be brought into scope. Without a cohesive approach to data preparation and protection, GenAI applications risk generating inaccurate responses, exposing sensitive information, or eroding user trust.

Four Capabilities That Enable Trustworthy GenAI

Arindam Banerjee, a Technical Fellow and VP at NetApp, outlines four foundational data capabilities that collectively determine whether GenAI outputs can be trusted in enterprise environments: data cataloging, data governance, real-time data streaming, and security. Each plays a distinct role in improving accuracy, relevance, and compliance.

• A data catalog provides visibility across the entire data estate by extracting metadata and creating a searchable global namespace. In SAP environments, this enables data engineers and AI teams to discover relevant datasets spanning SAP and non-SAP systems without manual investigation. However, NetApp emphasizes that metadata abstraction alone is insufficient if the underlying storage landscape remains fragmented. A unified storage operating system is needed to eliminate operational silos and ensure consistent data access, management, and performance.
• Data governance is equally critical as SAP customers incorporate GenAI into customer-facing and decision-support applications. Training or grounding models on poorly classified data can inadvertently expose personally identifiable information, financial data, or sensitive business plans. Governance frameworks help ensure that only appropriate datasets are used for specific AI use cases, reducing regulatory risk while improving model precision.
• Real-time data streaming addresses another pervasive problem: data staleness. NetApp notes that outdated datasets are a primary cause of hallucinations in AI systems, undermining confidence and usability. By continuously feeding models with up-to-date information, SAP customers can ensure that GenAI-driven insights reflect current products, pricing, policies, and operational conditions.
• Security underpins all of these capabilities. Beyond traditional access controls, AI data platforms must detect anomalous access patterns and protect against ransomware threats that could compromise training data or retrieval-augmented generation (RAG) pipelines. For enterprises deploying GenAI at scale, safeguarding data integrity becomes inseparable from safeguarding AI outcomes.

One of the more counterintuitive insights emerging from enterprise GenAI deployments is that more data and more infrastructure do not automatically yield better results. NetApp addresses this challenge directly through NetApp ONTAP, its unified storage operating system that serves as the foundation of the NetApp AI Data Platform.

What This Means for SAPinsiders

Trusted GenAI requires disciplined data foundations. For SAP technology leaders, this means shifting daily focus from model selection to data readiness, including cataloging unstructured content and enforcing governance across SAP and non-SAP sources. Teams deploying chatbots, copilots, or analytics assistants will spend more time defining data scope and less time troubleshooting inaccurate outputs.

Operational AI depends on data recency and relevance. Enterprises in manufacturing, retail, and financial services are increasingly pairing SAP data with real-time streams to keep AI-driven insights current, reducing errors tied to outdated pricing, inventory, or policy data. This trend reflects broader market momentum toward always-on data pipelines rather than batch-based AI training.

Provider evaluation must extend beyond AI features. SAP customers should prioritize platforms that integrate data cataloging, governance, streaming, and security as a unified capability rather than standalone tools. In day-to-day operations, this reduces integration overhead, improves auditability, and enables AI teams to deliver reliable outcomes faster and at lower cost.