Enhancing Discoverability in Enterprise Conversational Systems with Proactive Question Suggestions

TL;DR

This paper introduces a framework that improves question suggestions in enterprise conversational AI by generating proactive, context-aware questions, thereby aiding new users in task completion and feature discovery.

Contribution

It presents a novel approach combining user intent analysis and session-based question generation to enhance discoverability in enterprise AI systems.

Findings

Improved question suggestion relevance and usefulness.

Enhanced feature discoverability for new users.

Validated effectiveness using real-world enterprise data.

Abstract

Enterprise conversational AI systems are becoming increasingly popular to assist users in completing daily tasks such as those in marketing and customer management. However, new users often struggle to ask effective questions, especially in emerging systems with unfamiliar or evolving capabilities. This paper proposes a framework to enhance question suggestions in conversational enterprise AI systems by generating proactive, context-aware questions that try to address immediate user needs while improving feature discoverability. Our approach combines periodic user intent analysis at the population level with chat session-based question generation. We evaluate the framework using real-world data from the AI Assistant for Adobe Experience Platform (AEP), demonstrating the improved usefulness and system discoverability of the AI Assistant.