Technology Radar
Enterprise AI has focused heavily on text and vision, while much business data remains locked in relational databases. Foundation models adapt transformers to structured data, pretraining on an organization’s event sequences and transaction logs to learn reusable representations of business behavior.
Unlike traditional gradient-boosted models that require extensive feature engineering and retraining, these models can support multiple downstream tasks with lightweight fine-tuning. Tools such as TabPFN and TabFM can improve risk assessment, predictive analytics and customer retention directly from tabular or relational data. Given their potential to unlock business insights without complex feature pipelines, we suggest assessing this technique.
