The Role of Accuracy and Validation Effectiveness in Conversational Business Analytics

Abstract

This study examines how conversational business analytics can bridge the skill gap of end users that hinders traditional self-service analytics. By leveraging generative AI, conversational business analytics enables end users to independently retrieve data, process it, and generate information. Using Text-to-SQL as an example, this study proposes theoretical models grounded in expected utility theory to examine two levels of AI support: partial support, where AI translates natural language requests into SQL and the generated information serves directly as the basis for decision-making, and full support, which includes an additional validation step. The models define conditions where AI-driven information generation surpasses human delegation. These conditions underscore the critical interplay between AI accuracy and validation effectiveness as pivotal factors for the successful integration of AI. The findings suggest that partial support is viable when the AI accuracy is sufficiently high. In contrast, full support necessitates both adequate accuracy and robust validation. Insufficient validation impairs decisions, highlighting the need for effective validation techniques to fully leverage conversational business analytics. Moreover, the dependence on user-driven validation introduces additional risks, as its effectiveness is contingent on the user's experience or familiarity with SQL and underlying data structures. This insight challenges conventional validation techniques for AI-generated information and highlights the need to use techniques that reduce the reliance on the technical expertise of end users.