German Software Giant SAP Says LLMs Struggle with Complex Enterprise Predictions, Advocates for 'Table AI' to Fill the Gap

German software company SAP recently stated via its official blog that relying solely on Large Language Models (LLMs) like ChatGPT is insufficient for solving complex business problems, particularly in data analysis and future predictions involving numbers and tables. SAP proposed a technology called 'Table AI' (Table Foundation Model) as a complementary solution aimed at enhancing prediction accuracy in areas such as supply chain and finance.

SAP explained that while LLMs excel in document writing, information retrieval, and programming, enterprises heavily rely on structured data like sales figures, inventory levels, and order records. Accurate analysis of this data requires not just identifying numbers but also understanding the complex relationships between them. In contrast, LLMs often produce inconsistent answers when table formats or row orders change. This limitation was also confirmed in a paper presented by researchers from IBM Research and Stanford University at an ACL workshop in July.

As a representative case study, SAP introduced 'SAP-RPT-1.6', recently provided to customers. This AI model is specifically designed to analyze numbers and tables to predict future events. For example, it can forecast customer payment timeliness, product popularity trends, or stockout times based on past transaction records and sales volumes. Traditionally, companies had to develop separate AI models for each prediction task, resulting in high costs and maintenance difficulties. SAP's proposed 'Table Foundation Model' allows a single general-purpose model to handle multiple prediction tasks, thereby reducing development and operational costs.