According to Small Business Trends, SAP has announced plans to acquire Prior Labs as part of a broader push to strengthen its leadership in artificial intelligence focused on structured business data. The acquisition centers on Tabular Foundation Models (TFMs), a type of AI designed specifically to analyze and predict outcomes using tabular data commonly found in enterprise operations such as finance, supply chains, customer management, and logistics.
SAP said it plans to invest more than €1 billion over the next four years to scale Prior Labs into a frontier AI lab dedicated to structured data applications. The move reflects SAP’s strategy of building AI tools that move beyond traditional large language models and instead focus on the data systems that power day-to-day business operations.
The technology developed by Prior Labs is designed to help businesses predict outcomes such as payment delays, supplier risks, customer churn, and upsell opportunities. By integrating TFMs into SAP’s ecosystem, businesses may gain access to more advanced predictive analytics and decision-making tools tailored to operational data rather than general-purpose AI models.
A key feature of the technology is its ability to allow users to interact with data using natural language instead of requiring advanced technical or data science expertise. Businesses can run predictive analyses and “what-if” scenarios more easily, potentially improving operational efficiency and accelerating decision-making processes.
Prior Labs is also known for its open-source AI tool TabPFN, which has reportedly surpassed 3 million downloads among developers building AI applications for structured data. SAP said it intends to continue supporting the company’s open-source strategy as part of the acquisition.
Philipp Herzig, SAP’s Chief Technology Officer, said SAP identified structured business data as one of the largest untapped opportunities in enterprise AI, while Frank Hutter, CEO of Prior Labs, said joining SAP would provide the company with greater resources, customer reach, and access to enterprise-scale data environments.
The acquisition also highlights the growing importance of AI systems capable of understanding structured business information natively, rather than relying solely on text-based models. As businesses increasingly adopt AI-driven operations, demand is rising for tools that can generate predictive insights directly from enterprise data.