The discussion in the session below highlights that data itself is not inherently problematic, but the lack of standardisation, interoperability, and consistency across data sources creates significant issues, particularly in the integration with artificial intelligence (AI).

The speakers compare the current state of AI to the early internet days of 1996, emphasising that while AI has significant potential, most firms’ data is not yet AI-ready due to its unstructured nature and siloed storage across multiple formats like PDFs, Excel, HTML, and media files.

The conversation also touches on the difficulties in ensuring data purity, reliability of APIs, and the consistent merging and processing of data for efficient client and advisor use. The panelists discuss the hype versus reality of AI applications, asserting the importance of good clean data for future AI use cases while noting the industry’s general lag due to regulatory constraints and trust issues. They stress the need for transparency in AI algorithms and the importance of data lineage, traceability, and user consent.

The conversation concludes by recognising the significant trust issues consumers have with both financial institutions and AI technologies, which necessitate careful data handling and robust governance frameworks.

Watch parts of the session here: