The Role of Accuracy and Validation Effectiveness in Conversational Business Analytics
Adem Alparslan
TL;DR
The paper tackles when AI-enabled Conversational Business Analytics (CBA), via Text-to-SQL, can outperform traditional delegation to data engineers. It builds an expected-utility framework with accuracy $alpha$ and validation effectiveness $beta$, analyzing Partial Support (PS) and Full Support (FS) under a static horizon with KPI payoff $+1$ for a correct result, $-1$ for an incorrect one, and a delay penalty $v$. Key contributions include formal AI delegation thresholds for PS ($alpha^*_{PS} > (1+v)/2$) and FS ($beta^* > (1 - alpha) + v$, $alpha^*_{FS} > v$, $beta^{**} > alpha$), the identification of FS's potential boosting effect via validation, and the highlighting of risks from user-based validation. The work points to practical implications for deploying CBA, emphasizing robust validation mechanisms and proposing hybrid, automated validation methods to mitigate misclassification risks and improve reliability in real-world settings.
Abstract
This study examines conversational business analytics, an approach that utilizes AI to address the technical competency gaps that hinder end users from effectively using traditional self-service analytics. By facilitating natural language interactions, conversational business analytics aims to empower end users to independently retrieve data and generate insights. The analysis focuses on Text-to-SQL as a representative technology for translating natural language requests into SQL statements. Developing theoretical models grounded in expected utility theory, this study identifies the conditions under which conversational business analytics, through partial or full support, can outperform delegation to human experts. The results indicate that partial support, focusing solely on information generation by AI, is viable when the accuracy of AI-generated SQL queries leads to a profit that surpasses the performance of a human expert. In contrast, full support includes not only information generation but also validation through explanations provided by the AI, and requires sufficiently high validation effectiveness to be reliable. However, user-based validation presents challenges, such as misjudgment and rejection of valid SQL queries, which may limit the effectiveness of conversational business analytics. These challenges underscore the need for robust validation mechanisms, including improved user support, automated processes, and methods for assessing quality independent of the technical competency of end users.