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Conversational AI Interfaces
The use of NLP to conduct a conversation with a customer is, by far, the most impactful use of AI in a contact center. We're seeing major shifts in labor utilization, with anywhere from 10% to 70% decreases in contact center labor requirements depending on the use case. Decreased customer effort typically accompanies this given that a bot is faster than a human.
Often, these implementations engage all of the technologies discussed above in a seamless, noninvasive way (as long as you don't consider the "crowding out" of your IVR invasive). What I mean by noninvasive is that the bot, Web chatbot, or voice assistant becomes a user on your Web chat, voice telephony, and information infrastructure. Nothing has to break in order to implement either interface.
By integrating the bot as a user on these systems, you provide a seamless method for transferring interactions to the human agents when necessary. The bot knows what it doesn't know, and the bot can detect negative sentiment faster than a human. Under these, and some other conditions, the bot can transfer a customer interaction to a human for resolution.
A cognitive processing platform can use both Web chat and voice interfaces, as well as the new class of voice computing interfaces -- Amazon Alexa, Apple iHome, and Google Home, for example. In general, the interfaces are abstracted from the cognitive processor, but some are limited by the media they use. For instance, you can't send a graphics file to a voice interface. However, you can orchestrate the use of different media in what some call visual IVR. Nuance's implementation for American Airline is a perfect example of this. In this use case, the interaction may start in voice, but the platform can push a URL to a mobile device or email interface to support seat selection. In the American implementation, the URL brings up a seating map for seat selection.
Web chat integrations are usually based on Web Services integration. The only trick is getting the transfer function to work. Most of the larger players have prebuilt transfer functions into their APIs; however, if you use Web chat software with a lesser-known NLP solution, then you'll be writing some code.
Voice interfaces are a little trickier than Web chat. You should be able to reuse your IVR for ASR/TTS, but doing so will typically require some re-engineering and re-configuration -- all the way back into the PSTN. High-quality sound nets high-quality results. If you're using a high-compression codec, then you're asking for a low-performing speech recognition solution. In cases of IVR reuse, the ASR/TTS processor sits behind the legacy IVR. This way you can migrate one new voice NLP automated process at a time with minimal risk.
Alexa-type implementations can provide the same voice computing interface; however, they're architecturally different. In these cases, the device itself, and not the IVR, may host the ASR/TTS. Further, to transfer interactions to a legacy voice system will require use of compatible codecs. These new devices favor the open-standard Opus codec, which is also available on some voice systems, with WebRTC, and in many session border controllers.
By the way, Alexa use isn't limited to Amazon devices. Amazon introduced Alexa for Windows 10 last year, and any place you can run the Amazon app you can run Alexa. Further, you don't have to say "Alexa" all the time. You can tune the interface to respond to your company's name with little difficulty. For instance, you can say: "Open ABC Company product ordering" after you press the Alexa button and/or register the "skill" on your device.
You can use omnichannel and conversational AI together, but planning the dialog flow is very important. You must pay special attention to the failure-case situations that the bot will encounter. Visual IVR also creates some failure-case challenges that you'll need to plan out well in advance of deployment.
All of the major contact center CRM and telephony providers are offering or have a plan for making AI-based solutions available to their contact center customers. Further, more than 1,000 RPA, conversational AI, and NLP analytics platforms are on the market today.
In many ways contact center operators have never had it so good. AI-based automation tools are reducing labor requirements and customer effort. The tools are out there. If you make a good plan and execute you will succeed on many levels and discover things about your business and your customers that will lead to greater success.
