In a white paper a few years back, I opined that adoption of Virtual Digital Assistants platforms was being slowed down by the fact that building and maintaining its ‘knowledge’ was still a predominantly manual task and therefore expensive and slow. Over the last 20 years, many companies have adopted Virtual Assistants on their website, and some with a reasonable success, that showed consistent ROIs and improved customer satisfaction. However, Virtual Assistants never became pervasive during last decade. Many early adopters were excited about the possibilities, envisioned new uses for them, not just for customer support but also as hostess, marketing/product advisors. But one of the issues that muted this excitement was the inefficiency of the knowledge management process. These solutions relied on rule-based systems that required manual intervention to retrain the virtual assistants. While these systems did use machine learning for certain features, e.g., to adjust keyword weights or remember frequently used responses to queries, but the creation and modification of the dialogs was pretty much a manual effort. If in doubt, they accepted the alternative of just placing a static FAQ or search engine that indexed the knowledge automatically, even if the user experience was not nearly as good. This attitude has changed over the last few years, and companies are now recognizing the importance of the conversational user experience to retain customers. For the virtual assistant platforms still around, this could be a renewed opportunity for a breakthrough. The rule-based Virtual Assistants are better for businesses than the modern ML-based models, for two critical reasons: 1) amount and quality of data available to build reliable ML-based systems, 2) simplicity and predictability of rule-based systems. |
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