主动建议问题，帮企业用户更快发现AI助手功能。
Enhancing Discoverability in Enterprise Conversational Systems with Proactive Question Suggestions
- 结合用户群体意图与对话上下文，生成主动提问建议。
- 在Adobe AEP真实数据上验证，显著提升功能可发现性。
- 适合刚上手的企业级AI助手用户和产品设计者。
企业对话式AI系统在营销、客户管理等日常任务中日益普及。但新用户常难以提出有效问题，尤其在功能尚不熟悉或持续演进的系统中。本文提出一种框架，通过生成主动且上下文感知的问题建议，既满足即时用户需求，又提升系统功能可发现性。方法结合用户群体层面的周期性意图分析与会话级别的问题生成。基于Adobe Experience Platform（AEP）AI助手的真实数据评估，结果表明该框架显著提升了建议的有用性与系统可发现性。
原文摘要 · Abstract (English)
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.
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