SAP Moves to Acquire Dremio and Prior Labs to Strengthen Data Foundation and Enterprise AI Strategy

SAP has announced plans to acquire data platform provider Dremio and AI research firm Prior Labs, signaling a coordinated push to strengthen both the data and model layers anchoring its enterprise AI strategy.

The two transactions, announced on May 4, are expected to close later in 2026 pending regulatory approval. Financial terms were not disclosed. Together, the deals target two persistent constraints in enterprise AI: fragmented data environments and limited model performance on structured business data.

SAP CEO Christian Klein framed the moves as part of a unified strategy to turn enterprise data into actionable intelligence. He said the acquisitions build on SAP’s existing data foundation to help customers “turn data into trusted decisions and predictive insights.”

Addressing Data Fragmentation with Dremio

SAP’s planned acquisition of Dremio focuses on the data layer, where fragmentation and integration challenges continue to limit AI adoption.

According to SAP, many enterprise AI initiatives fail not because of model limitations but because underlying data is “fragmented, locked in proprietary formats and stripped of the business context that makes it meaningful.” This results in pilots that cannot scale, duplicated engineering effort, and increased compliance risk.

Dremio’s lakehouse platform is designed to address these issues by enabling unified access to SAP and non-SAP data without requiring data movement or format conversion. Once integrated, SAP Business Data Cloud will become an Apache Iceberg-native environment, allowing structured and unstructured data to coexist on a single open foundation.

“Enterprise AI doesn’t stall because the models aren’t good enough; it stalls because the data isn’t ready for AI agents,” said Philipp Herzig, CTO, SAP SE. “Dremio eliminates that bottleneck.”

The platform will also introduce a universal data catalog layer, combining metadata, lineage, and access controls into a single semantic framework. This is expected to form the foundation of SAP’s Knowledge Graph, embedding business context directly into data used for analytics and AI workloads.

Building a New AI Model Layer with Prior Labs

While Dremio addresses data readiness, SAP’s planned acquisition of Prior Labs targets the model layer, specifically AI performance on structured enterprise data.

LLMs have struggled with tabular data, which dominates enterprise systems such as finance, supply chain, and procurement. Prior Labs specializes in Tabular Foundation Models (TFMs), which are designed to understand and predict outcomes from structured datasets such as payment behavior, supplier risk, and customer churn.

SAP plans to invest more than €1 billion (approximately $1.17 billion) over four years to scale Prior Labs into a global frontier AI lab focused on this category. The unit will operate independently to maintain research velocity while integrating with SAP’s product stack through SAP AI Core, SAP Business Data Cloud, and the Joule agent layer.

“Early on, SAP recognized that the greatest untapped opportunity in enterprise AI wasn’t large language models; it was AI built for the structured data that runs the world’s businesses,” Herzig said.

Prior Labs’ technology, including its widely adopted open-source model TabPFN, enables instant predictions on tabular data without requiring extensive model training. SAP said this capability will allow business users to run “what-if” scenarios and generate predictions directly from enterprise datasets, with minimal data science expertise.

Connecting Data and AI into a Single Architecture

The two acquisitions reflect a broader architectural shift. Dremio extends SAP’s ability to unify and govern enterprise data across systems, while Prior Labs enhances its ability to generate predictive insights from that data.

Klein described this as building on SAP’s “strong data foundation,” combining open data infrastructure with advanced models tailored to business data.

The strategy also aligns with SAP’s broader push toward agentic AI, where systems not only analyze data but execute workflows and decisions autonomously. In that context, both data accessibility and model accuracy become critical prerequisites.

SAP said the combined capabilities will allow customers to move from “raw, fragmented data to governed, AI-ready intelligence” while improving time to value for AI initiatives.

What This Means for SAPinsiders

SAP is investing across the full AI stack, not just applications. The combined acquisitions of Dremio and Prior Labs show a coordinated strategy to control both data infrastructure and model innovation. For SAP customers, this signals that future differentiation will come less from standalone AI features and more from how tightly data, models, and applications are integrated across the landscape.

Structured data is emerging as the next battleground for enterprise AI. SAP’s focus on tabular foundation models highlights a key limitation in current AI approaches, particularly for finance, supply chain, and operational use cases. Improving predictive performance on structured data could unlock more immediate business value than general-purpose AI models that struggle with enterprise datasets.

Open data architecture is becoming foundational to AI adoption. With Dremio’s Iceberg-based lakehouse and federated access to SAP and non-SAP data, SAP is reinforcing a more interoperable approach to enterprise data. For customers, this reduces friction in unifying data across systems, but raises the importance of governance, data quality, and architectural discipline as prerequisites for scaling AI initiatives.