The Artificial Intelligence Journey in Contact Centers
I would like to share some thoughts pulled together in discussions with developers, customer care system integrators and experts, along one of the many possible journeys to unleash all the power of Artificial Intelligence (AI) into a modern customer care architecture.
First of all, let me emphasize what the ultimate goal of Artificial Intelligence is:
“Artificial intelligence is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”
AI in the Contact Center
In a Customer Care (CC) environment, one of the primary objectives of any AI component is to support and assist agents in their work and potentially even replace some of them, so we should ask ourselves how far are we from such a goal today? Let me answer by posting here a recent demo on stage from Google:
Google Duplex: A.I. Assistant Calls Local Businesses To Make Appointments – YouTube
Seen it? While clearly Google Duplex is still in its infancy, I think it’s evident that a much wider application range is behind the corner and we are not too far away since the moment where a virtual agent controlled by an AI engine will be able to replace the real agents in many contexts and with a natural conversation and flow like humans would have with each other.
“Gartner projects that by 2020, 10% of business-to-consumer first level engagement requests will be taken by VCAs… today that is less than 1%….”
Google Duplex Natural Technology
The interesting piece, which leading players such as Cisco (Cognitive Collaboration Solutions) will be able to turn into an advantage for their Contact Center architecture, is related to the way Google Duplex works. It is essentially made of the 3 building blocks:
- The incoming audio goes into an Automatic Speech Recognition (ASR) able to translate audio into text
- A Recurrent Neural Network (RNN) formulates a text answer based on the input text
- Other inputs and finally a Text to Speech (TTS) converts the answer back in audio using speech synthesis technology (Wavenet)
For those of you dealing with customer care it’s rather clear how such an architecture would fit very well into an outbound contact center delivering telemarketing campaigns: this is the way Google is already positioning the technology in combination with their Google assistant.
A part the wonderful capabilities offered by the front and back end text to audio converters, the intelligence of the system is in the Recurrent Neural Network (RNN) able to analyze the input text and context, understand it and formulate a text answer in real time, de facto emulating the complex behavior and processes of human beings.
The most of the CHAT BOTs used today in CC are not even close to this approach as they are doing the dialogue management in a traditional way, managing flows with certain rules, similar to a complex IVR, struggling with the complexity of natural language. Duplex (Tensorflow) or other solutions, such as open sources communities in the world of AI developers (Rasa Core), are adopting neural networks, properly trained and tuned in a specific context, to offer incredibly natural dialogue management. The RNN needs training, which was in the case of Duplex done using a lot of phone conversations data in a specific context as well as features from the audio and the history of the conversation.
CISCO and AI in Contact Centers
Some of the unique approaches explained above could make the customer care solutions of those vendors able to adopt them quite innovative and well perceived, especially when there is an underlying, solid and reliable architectural approach as a foundation. In news out last year, Cisco announced a partnership with Google:
“Give your contact center agents an AI-enhanced assist so they can answer questions quicker and better. …. we are adding Google Artificial Intelligence (AI) to our Cisco Customer Journey Solutions … Contact Center AI is a simple, secure, and flexible solution that allows enterprises with limited machine learning expertise to deploy AI in their contact centers. The AI automatically provides agents with relevant documents to help guide conversations and continuously learns in order to deliver increasingly more relevant information over time. This combination of Google’s powerful AI capabilities with Cisco’s large global reach can dramatically enhance the way companies interact with their customers.”
This whole AI new paradigm demands further thoughts:
- The most of AI technology is OPEN SOURCE so the value is not in the code itself but more in the way it is made part of a commercial solution to meet customer needs, especially when it is based on an architectural approach, such as the Cisco Cognitive Collaboration.
- The above point is further driven by the fact that it is difficult to build a general-purpose AI solution, as it will always need customizations according to the specific context of a customer or another. The use cases change and the speed of innovation is probably faster than in the mobile devices’ world, so it is difficult to manage this via a centralized, traditional R&D. This fits more into a community approach made of developers, system integrators and business experts such as the Cisco Ecosystems.
- Rather then coding AI software, the winning factor is the ability of vendors like Cisco to leverage an environment made of Ecosystem partners, System Integrators and Customer experts to surround and enrich the core Customer Care architecture offerings.
The availability of ecosystem partners, able to package specific CONVERSATIONAL engines, specialized for certain contexts and the role of system integrators, able to combine those engines into the Cisco Customer Care architecture to meet the customer needs, are KEY CISCO competitive advantages in the coming AI revolution.
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