Generative AI helps people to write, research, program, or evaluate content faster. A huge benefit, but one that also comes with a serious problem: according to a McKinsey study, while 80% of AI users report increased personal productivity, only six percent of companies say that AI significantly increased their operating profit.
Turn structured data into accurate, explainable predictions that your team and AI agents can act on
- How can this gap be closed? The answer lies in helping companies detect risks, defaults, losses, and liquidity pressures sooner—and manage them more effectively.
For Germany and Europe, it is not an abstract challenge, but the key to future prosperity. Our economic strength lies in industry: in mechanical engineering, in the automotive supply chain, in complex manufacturing and logistics processes. According to German Economic Institute estimates, AI can generate around €440 billion of additional gross value in Germany by 2034.
Good, but not in every case
To get the economy moving again, we need a different kind of AI. Because it is not enough to process and generate language; AI must also be able to deal with data that companies work with the most. And this is where common large language models (LLMs) reach their limits. Structured data—for example, numbers in tables—help companies understand what is happening right now and what may come next.
This requires AI models that understand the relationships among rows, columns, entities, and numbers. This is exactly what current research results show: several independent benchmarks from the year 2026 prove that even high-performance LLMs perform significantly weaker in table reasoning tasks—for example, in the ability to draw conclusions from tables and to recognize relationships—than in language tasks.
When data anticipates what’s next
Tables follow their own logic. Values do not stand next to each other in isolation, but in relationships between columns, rows, timeframes, and business transactions. Accordingly, tabular AI is designed to recognize patterns across columns as well as in the associated tables that make up a company system. The real added value lies in this networked context. Looking at a customer ID in isolation is one thing. Seeing which orders this customer places and predicting warehouse requirements from these data is a completely different thing.
A practical example: a manufacturer wants to identify early which customers are most likely to pay late. For this, a single piece of information is not enough. Among other things, relevant factors include payment history, order history, delivery data, and external industry information. Only the interaction of these data makes reliable predictions possible. That’s precisely what tabular AI models are built for.
What this means for AI investments
Classic machine learning methods are still used in many companies today for predictions based on structured data. They often deliver good results, but typically have to be developed, trained, and operated separately for each individual application: one model for demand forecasting, another for payment default risks or for prioritizing sales contacts, each with data preparation, training, monitoring, and ongoing maintenance.
Tabular foundation models change this equation. They do not have to be redeveloped for every business question but are universally applicable. This creates an immense scaling effect. In the future, instead of hundreds of specialized models, a single one—such as SAP-RPT-1.6, which we have just made available to customers—will suffice. With this, companies can build on historic examples and existing patterns from their business data in order to identify new risks and opportunities at an early stage.
For companies, this means a concrete shift: AI predictions will no longer have to be developed as complex individual projects in the future. In other words, the path from a specific question to the universal use of a model for any questions becomes shorter and cheaper.
In addition, since tabular foundation models are not pre-trained on individual use cases, but on large amounts of data, they can also recognize patterns across different use cases that specialized individual models, especially in limited data layers, often overlook. Therefore, in addition to the scaling effect, there is also a gain in knowledge, which previously did not exist in the analytical processing of tabular data.
New category for enterprise AI
At SAP, we are working on the next level of enterprise AI. With the acquisition of Prior Labs in July, we are further accelerating this path: combining Prior Labs’ basic research with the corporate reality of our customers, and the experience of SAP in the application of AI in business-critical business processes.
With TabPFN-3, Prior Labs has developed one of the most powerful open-source models for tabular predictions. Together with the tabular foundation models of the SAP-RPT family and SAP Knowledge Graph, this creates an infrastructure that securely generates structured predictions directly from the company-specific data with the individual business context.
In general, now we are seeing a trend toward various types of foundation models. In addition to tabular foundation models, models that have been trained on the basis of time series—for example, a large number of measurements—are being created. You can evaluate the measurements, recognize patterns, and thus also predict numerical values over time.
Conclusion
The first wave of generative AI is making employees more productive. The next one can make companies more forward-looking. The decisive factor here is not whether companies use LLMs or tabular foundation models. It is crucial to combine the two where they provide the greatest benefit: LLMs for interaction with language and knowledge, tabular models for forecasts based on structured business data.
Done right, AI becomes more than a productivity tool. It becomes a driver of better business decisions—and a foundation for a stronger economy.
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