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, the study identifies 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 independently of end users' technical competencies.

翻译：本研究探讨对话式商业分析，该方法利用人工智能解决阻碍终端用户有效使用传统自助分析的技术能力差距。通过促进自然语言交互，对话式商业分析旨在使终端用户能够独立检索数据并生成洞察。分析聚焦于Text-to-SQL作为将自然语言请求转换为SQL语句的代表性技术。基于期望效用理论建立理论模型，本研究确定了对话式商业分析通过部分或完全支持能够优于委托人类专家的条件。结果表明，当AI生成的SQL查询的准确性带来的利润超过人类专家表现时，仅关注AI信息生成的部分支持是可行的。相比之下，完全支持不仅包括信息生成，还包括通过AI提供的解释进行验证，并且需要足够高的验证有效性才能可靠。然而，基于用户的验证存在挑战，例如误判和拒绝有效的SQL查询，这可能会限制对话式商业分析的有效性。这些挑战突显了需要强大的验证机制，包括改进用户支持、自动化流程以及独立于终端用户技术能力评估质量的方法。