The mobile parts of the Google call center AI
Google CCAI consists of several different products:
- Dialogflow, which automates basic chat and voice interactions
- Agent assistance, which makes human agents more efficient
- CCAI Insights, which unlocks information about call drivers
Dialogflow CX describes the relationships between caller interactions. Agent Assistance Features include live transcription, an advisor to guide conversation flow, knowledge assistance with FAQ answers, sentiment analysis, and smart reply and suggested reply composition in based on historical data. Insights from the IACC analyzes the conversations and digs into their details. With this information, government agencies can understand trends and improve services to citizens.
Citizens seeking help from government agencies initiate a conversation via call, chat, or other means, and Google CCAI serves as the omnichannel platform that drives these conversations. Google’s CCAI products integrate with telephony platform vendors such as Avaya, Cisco, Mitel and others, as well as contact center desktop systems. Products can pass requests to each other, depending on the requirements of the citizen caller.
A gateway can facilitate communication with backend applications, providing data to service delivery applications such as ServiceNow, Salesforce, SAP, and others.
Citizens have widely indicated that they would like self-service options, and Google CCIA can provide them with features such as password reset, phone and address update, data lookups and Moreover.
RELATED: How can conversational AI help improve government call centers?
3 Keys to Adopting Cloud Center Automation
Adopting cloud contact center automation actually involves three key elements:
Design the contact center to be guided around an AI experience. To revamp the contact center to be driven around an AI contact center experience, architects need to map call flows, chat and web integrations, messaging systems, and contact management tools. quality and workmanship. They must ensure that the customer and call center employee journey remains uninterrupted throughout the automated experience.
Embrace 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 information in real time.
Data and software development. Virtual agents and agent-assisted insights are most valuable when integrated into backend data sources. Custom microservices can control a virtual agent’s logic and automated experience. The fun characteristics of these microservices are determined by the steps taken in building the previous elements. How was the AI natural language module designed? And how is the contact center integration going?
Increasing volume to meet high demand or to reduce citizen wait times has always been a costly burden on states. Google Contact Center AI can eliminate this compromise.
This article is part of StateTechIs CITizen blog series. Please join the discussion on Twitter using the #StateLocalIT hashtag.