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An in-depth guide into the trusted use of AI in customer service automation
Find out everything you need to know about establishing "Explainable AI" based on the AIC4 criteria catalogue.
In its short time span, how has Conversational AI already evolved and how is it transforming customer service automation?
Derek Roberti, VP of Technology at Cognigy shares his thoughts on this during a Modern CTO podcast, including the ultimate outcomes of automation, the future of Natural Language Understanding (NLU), and the next chapter of customer service automation. See highlights below or listen to the full episode.
Automatic Speech Recognition and Text to Speech technologies are remarkably good. Natural Language Understanding is imperfect but good enough for many applications. So, why don’t we see consistently impressive automated conversational interactions?
In conversational development we are very early on: we’ve addressed the technological hurdles sufficiently, but we haven’t yet created reference experiences that we can model for diverse use cases. Now we are at that next step, devising best practices and educating people on what good conversational interactions look like. Developers, product managers, and business analysts have never had to design a conversation before, so a lot of what we see on the web and hear on the phone aren't examples of a good user experience.
What we can look forward to is a new chapter in the industry when we start foregrounding the user experience and looking at how can we create conversations that solve problems for people – that don't frustrate people, that understand people enough to help them reach their goals and leave them feeling whole when the conversation is done. This means recognizing the unique skillsets required for conversational design – it's not about hiring a developer or hoping a customer experience manager can tackle it on their own.
Once we get to a point where that user experience gap has been bridged, the whole mentality around what conversational automation is and the value it produces will change.
What we (at Cognigy) have found is that the technology behind the scenes, whether it's AI or something else, is less important than the outcomes that we're going for. We’ve invested heavily in very unique tooling, NLU, analytics, and much more, but we don’t foreground a technology story.
We try to reorient people to understand that, while AI and machine learning are all important, none of these really point to an outcome. The objective is to automate business processes and the outcome is the two-fold benefit for customers and for businesses. For users, we make it easier and faster for customers and employees to access the information and systems they need 24 hours a day. For business, the benefit is efficiency and cost reduction.
We always tell customers to focus their human resources on scenarios where human empathy and human decision-making are important and automate anything where people are essentially order takers, performing data entry, or doing simple look-ups of information.
We are Cognigy.AI, that’s the name of our platform, but conversational automation is really our mission above all else.
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