The Moving Parts of Google Call Center AI
Google CCAI consists of several different products:
- Dialogflow, which automates basic chat and voice interactions
- Agent assist, which makes human agents more effective
- CCAI Insights, which unlock insights about call drivers
Dialogflow CX depicts the relationships between caller interactions. Agent assist features include live transcription, an advisor to guide conversation flow, knowledge assist with FAQ answers, sentiment analysis, and smart reply and compose for suggested responses based on historic data. CCAI Insights analyzes conversations and digs into their details. With these insights, government agencies can understand trends and improve citizen services.
Citizens seeking help from government agencies initiate a conversation either by call, chat or other means, and Google CCAI serves as an omnichannel platform that conducts those conversations. The Google CCAI products integrate with telephony platform providers such as Avaya, Cisco, Mitel and others, as well as contact center desktop systems. The products can pass inquiries between each other, depending upon the requirements of the citizen caller.
Citizens have largely indicated that they would like self-service options, and Google CCIA may provide them with functionality including password reset, phone and address updating, data searches and more.
3 Key Elements for Adopting Cloud Center Automation
Adopting cloud contact center automation actually involves three key elements:
Architecting the contact center to be driven around an AI experience. To re-engineer the contact center to be driven around an AI contact center experience, architects must map call flows, chat and web integrations, email systems, and quality and workforce management tools. They must ensure the call center customer and employee journey remains uninterrupted throughout the automated experience.
Adopting AI and cloud architectures. How the call center is designed determines how a call center will train a natural language module, how to design and deploy virtual agents and chatbots, and how to surface real-time information.
Data and software development. Virtual agents and agent-assisted insights are more valuable when integrated into backend data sources. Custom microservices may control the logic of a virtual agent and the automated experience. The functions of those microservices are determined by steps taken in building the previous elements. How has the AI natural language module been designed? And how does contact center integration happen?
Increasing volume to handle high demand or to decrease hold times for citizens has traditionally been an expensive burden for states. Google Contact Center AI may eliminate that tradeoff.