SAP has announced its decision to acquire Prior Labs to turn its structured enterprise data into predictive and actionable intelligence.
This acquisition will allow SAP to access Prior Labs’ deep expertise in tabular foundation models (TFMs), AI models designed specifically for business data such as transactions, customer records, and operational metrics.
By instilling TFMs across its AI and data stack, this will enable SAP to build upon its earlier work with SAP-RPT-1, its first proof point that foundation models trained on structured data can complement large language models.
Philipp Herzig, CTO at SAP, explained how this acquisition will ensure SAP’s global competitive advantage in the tabular foundation model sector.
“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,” he said.
“We built SAP-RPT-1 to prove that conviction for enterprise data. Prior Labs has built a leading TFM on public benchmarks and built one of the leading research teams in this category.
“Combining their frontier model work with enterprise data and customer reach is how we intend to lead this category globally.”
Why Enterprise AI Needs More Than LLMs
Most of the data that runs inside an enterprise exists in tables rather than in natural language, and whilst LLMs are effective for conversational experiences, summarization, and content creation, these models are not typically optimized for understanding complex relationships in structured business datasets.
By moving further toward TFMs, these models are designed to recognize patterns, correlations, and predictive signals across rows and columns of business data.
It can use structured historical data to make real-time predictions or recommendations, often with less task-specific training than traditional machine learning models.
This also includes understanding whether a customer is likely to churn based on declining order frequency, unresolved service issues, payment delays, and product usage trends across multiple systems.
As a result, TFMs address the limitations of generic generative AI pilots, with these solutions often focusing on user interaction rather than operational decision-making, unable to always predict what is likely to happen next or what actions should be taken.
TFMs can help close this gap by enabling predictive intelligence directly within business workflows, identifying high-risk accounts, uncovering why patterns are emerging, and deciding what intervention is most likely to improve retention.
These are also designed to handle business datasets that are incomplete or spread across multiple systems more efficiently, reducing implementation time and making predictive AI more accessible across business teams.
Scaling SAP’s TFM Strategy
Having already begun its TFM approach with SAP-RPT-1, this acquisition will allow SAP to position itself as one of the leading research teams in the field, gaining access to a proven, state-of-the-art model, and move faster into real-world enterprise applications.
Rather than absorbing the company completely, SAP plans to keep Prior Labs as an independent research unit, investing more than €1BN over the next four years to scale it into a global frontier AI lab based in Europe.
Once acquired, Prior Labs models will be integrated into SAP’s AI and data stack, interacting with systems such as SAP Business Data Cloud, SAP AI Core, and the Joule agentic AI layer.
For SAP enterprise customers, this will enable them to use natural language interfaces to run predictions, analyze scenarios, and generate insights directly from their structured data without needing data science expertise or retraining models for each use case.
This will allow SAP to differentiate its enterprise AI strategy by owning the foundation models that power business decision‑making, aiming to turn its enterprise data into predictive, explainable, and actionable intelligence at scale.
Who are Prior Labs?
Prior Labs is a research-driven AI company focused on building tabular foundation models, designed specifically to analyze and generate predictions from structured business data.
The German AI startup was founded in 2024 with the aim of making foundation models work natively with structured data such as spreadsheets, databases, and enterprise records, rather than only text or images.
Frank Hutter, CEO of Prior Labs, highlights how this acquisition gives it the scale, resources, and enterprise data needed to accelerate its research and widely deploy its TFMs.
“Over the last 18 months, Prior Labs has built an incredible team, increasing the velocity in tabular foundation models,” he said.
“Joining the SAP family gives us the resources, data environment and customer reach to take this category to its full potential.”
Predictive AI for Customer Experience
SAP’s acquisition of Prior Labs shifts toward AI that can actively predict customer behavior and recommend actions based on operational data.
By bringing TFMs into the CX ecosystem, SAP is enabling predictive and data-driven customer applications to find data patterns and identify signals that aren’t always visible through traditional, rule-based automation.
Inputting TFMs into SAP Customer Experience will allow the vendor to tackle areas such as churn prediction, next-best-action recommendations, and customer risk detection.
Customer service can benefit from TFMs by identifying which cases are likely to escalate before service levels are breached.
For marketing, these models can also improve segmentation and campaign effectiveness by identifying which customers are most likely to convert, which channels drive engagement, and which offers create margin without unnecessary discounting.
In commerce, this can also help predict buying intent, basket behavior, or return risk.
Furthermore, this also helps SAP differentiate its CX platform from competitors by combining conversational AI through SAP Joule with predictive intelligence from these models, with LLMs helping users ask questions and interact naturally with systems, while TFMs provide the underlying business reasoning.
For CX leaders, this means AI becomes more about improving retention, loyalty, customer lifetime value, and proactive service outcomes.
