Within a few weeks, SAP has significantly expanded its data & analytics portfolio by acquiring three strategically important companies: Dremio, Prior Labs, and Reltio. These include a data lakehouse platform, an AI startup, and a master data management specialist. In light of SAP’s strategy, these acquisitions paint a clear picture: SAP is consistently expanding the technological foundation needed to bring the AI initiative announced at Sapphire 2026 to life.
Dremio defines itself as an Agentic Lakehouse, a data platform developed specifically for AI agents and based on the open standard Apache Iceberg. The key promise: high-performance queries across heterogeneous data sources – without the need to physically move data to a central location. This principle of federated architecture is particularly relevant when companies need to consolidate data from a variety of sources, such as ERP, HR, CRM, or production systems.
With regard to SAP Business Data Cloud (BDC), Dremio further expands the possibilities for integrating external data and formats. It also creates a unified governance layer for heterogeneous data landscapes. Ultimately, the acquisition of Dremio represents a logical continuation of the path toward an open data ecosystem and a complementary addition to SAP BDC. SAP and non-SAP data can be seamlessly integrated to make agent-based AI more quickly and effectively usable in enterprises.
The AI startup Prior Labs from Freiburg has developed a foundation model called TabPFN – not for text, but for tabular data. While large language models work well with unstructured content, they quickly reach their limits when dealing with classic ERP data – such as sales figures, inventory levels, or workforce planning. TabPFN closes this gap: The Tabular Foundation Model (TFM) delivers predictions based on structured business data without requiring extensive data science expertise or complex model training.
The acquisition of Prior Labs should also be viewed as a complement to SAP’s own foundation model, SAP-RPT-1. TabPFN and SAP-RPT-1 differ in architecture, objectives, and understanding of data. As a pre-trained relational transformer model, SAP-RPT-1 learns contextually to generate precise predictive insights from structured business data. Prior Labs’ foundation model is a specialized AI for tabular data and predictions. This strengthens SAP’s market position in the TFM sector.
The cloud-native Reltio platform for master data management is capable of consolidating, cleansing, and linking master data – such as customer, product, or supplier data – in real time. What at first glance sounds like “just” classic data management is of crucial importance in the AI era: No algorithm is better than the data it works with. Duplicate customer records, inconsistent product names, or outdated supplier information undermine the quality of any AI application.
With Reltio, SAP Business Data Cloud gains a master data layer. In combination with Dremio’s functionalities, this creates an architecture that not only connects data but also ensures that this data is reliable – a fundamental prerequisite for trustworthy AI scenarios in business-critical processes.
At Sapphire 2026, SAP clearly outlined its AI strategy: In the future, Business AI is to be deeply embedded in all business processes – with SAP Joule serving as the central interaction layer and SAP Business Data Cloud as the data foundation within the newly created SAP Business AI Platform. SAP made these three acquisitions with this vision in mind: Dremio provides universal data access, Prior Labs creates an intelligent analytics layer for structured data in tables, and Reltio ensures the data quality required for AI deployment. Together, they address three typical pain points in enterprise AI implementation: data silos, insufficient usability of structured data, and poor master data quality.
SAP’s acquisition activities follow a clear logic. The acquisitions are not a reaction to short-term market trends, but rather targeted investments in technological foundations without which Business AI can hardly scale in the SAP context. It remains to be seen how quickly SAP will integrate these platforms into SAP BDC and the entire SAP ecosystem. The direction is clear – but implementation will determine whether SAP can deliver on its AI promise.