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May 9, 2019 | By Brent | Share this article

Given the hype around AI, you would think this technology could do everything by now, from walking your dog to cooking your next meal. While we’re not quite there yet, Conversational AI is paving the way to enabling seamless human-machine interaction – a vital step towards Artificial General Intelligence (AGI). Virtual Assistants are an embodiment of Conversational AI done right – though people often think they are the same as a chatbot. The two are probably as similar as a learning toddler and a talking doll, and while the former is gaining increasing adoption across front and back office automation tasks, the latter is sliding into oblivion.

While the jury is still out on the nature versus nurture debate, what is not up for debate is how Conversational AI is more of an ecosystem than a technology. While technology is a vital pillar in the Conversational AI ecosystem, there are two other often overlooked pillars, and without them, Conversational AI flounders. These two other pillars are Conversational Journeys and Content. Together, the three pillars are bound by the behavioural data generated, as users interact with the Conversational AI platform. In turn, the behavioural data serves as fodder to enable data driven optimization for the AI, journey design, and content.

AI optimization flowchart

To put things into perspective, a perfect AI which performs with 100% accuracy will cease to satisfy end-users if the journey design isn’t optimal. In the same vein, a perfect AI with an optimal conversational journey design will still fail to engage or satisfy users if the content to be served as a resolution isn’t relevant, is poorly formatted or worse – doesn’t even exist to begin with.

In this regard, we’re excited for the release of Wysdom Studio – a one-stop shop that combines journey design, deep behavioral insights and optimization tools, all under one umbrella. Studio brings together Wysdom’s operational expertise, along with new age tooling that provides deep insights on user behaviour, journey adoption and KPI performance, in an easy to understand format for the business. The insights gained in turn drive the optimization of the AI, conversational journey and content, paving the way for rapid automation at scale in the Conversational AI learning process.

With the commoditization of machine learning algorithms on the horizon, algorithms will slowly but surely cease to be the differentiating factor in the Conversational AI ecosystem. To heed some words of Wysdom, it is not about how smart your AI is – it is about how fast your AI can learn, which will ultimately determine the success or failure of your Virtual Assistant.