Agentic AI has an unstructured data problem: IBM is unveiling a solution
Summary
At Think this week, IBM is radically simplifying the data-for-AI stack. Forty percent more accurate AI than conventional RAG, according to testing with IBM watsonx.data.1 Products and features expected to debut in June include: • Watsonx.data integration, software to orchestrate data access and engineering across diverse integration styles and formats in a single interface, with flexibility and scale at its core • Watsonx.data intelligence, software to transform how organizations curate, manage, and utilize meaningful data, leveraging the power of AI to simplify data governance • The addition of watsonx as an API provider within Meta’s Llama Stack, enhancing enterprises’ ability to deploy generative AI at scale and with openness at the core Watsonx.data integration and watsonx.data intelligence will be available as standalone products, and select capabilities will also be available through watsonx.data—maximizing client choice and modularity. To complement these products, IBM recently announced its intent to acquire DataStax, which excels at harnessing unstructured data for generative AI. With DataStax, clients can access additional vector search capabilities. Based on internal testing comparing the answer correctness of AI model outputs using watsonx.data Premium Edition retrieval layer to vector-only RAG on three common use cases with IBM proprietary datasets using the same set of selected open source commodity inferencing, judging and embedding models and additional variables.