Genesys Agent Copilot (EE32) for Genesys Cloud

From Genesys Documentation
Jump to: navigation, search
This topic is part of the manual Genesys Cloud CX Use Cases for version Current of Genesys Use Cases.
Automatically surface contextually relevant information from a knowledge base during customer conversations.

What's the challenge?

Agents balance many tasks simultaneously: speaking to customers (sometimes more than one at a time), reviewing data, finding answers, planning what to say and do next, and capturing notes. As consumer preference and increasingly advanced self-service options move “easy” questions out of the contact center, agents are left with complex issues to solve, for customers who have expectations that are higher than ever before.  

To deliver standout customer experiences, agents must have useful data and insights, within the work space that they’re already using, at the moment of need.  

What's the solution?

Genesys Cloud Agent Copilot empowers contact center agents with AI-driven guidance during and after customer interactions. Genesys Cloud Agent Copilot determines customer intent, automatically surfaces knowledge and guides agents to their next best actions, summarizes interactions, and predicts wrap-up codes. 

Use Case Overview

Story and Business Context

Positive customer experiences can only happen when agents can answer a customer's request, provide empathetic, personalized service, and deliver on the requested outcome. In many contact centers, agents must navigate multiple processes and tools to look up knowledge and FAQs to find answers and resolve customer inquiries which takes time, leaves customers sitting on hold, and causes high average handle time and long waits.

With Agent Copilot, companies can leverage the power of artificial intelligence (AI) to support agents as they serve customers digitally, or on the phone. Genesys Cloud Agent Copilot determines customer intent, automatically surfaces relevant knowledge, and guides agents to their next best actions – such as what to say next, what workflow to kick off, how to follow up, and more. It then summarizes interactions by generating text and predicts wrap-up codes. Agents spend time personalizing the assistance they offer each customer, and providing superior answers based on  suggested results, rather than digging for information and writing call notes.

It is possible to create rules based on events like starting an interaction, ending a conversation, or transferring it to another queue or agent. When an event like this happens, it will be possible to configure the appearance of an article from the knowledge base, triggering a script or a canned response.

Agent Copilot also allows to train a NLU model that detects Intents based on utterance. The detection of these intents will allow the user get an article from the knowledge base, triggering a script or a canned response, as with the events described above.

After the completion of the conversation between the agent and the user, a summary of the conversation is generated. This summary can be reviewed, modified, copied and pasted as part of the notes of the interaction.

Genesys offers Agent Copilot as a native AI capability fully integrated into Genesys Cloud CX.


Use Case Benefits*

The following benefits are based on benchmark information captured from Genesys customers and may vary based on industry, lines of business or Genesys product line:

Use Case Benefits Explanation
Improved Employee Satisfaction Agents tackle more complex business inquiries with AI assistance.
Improved Employee Utilization A constantly-evolving knowledge base trains agents in real time.
Improved First Contact Resolution Present relevant suggestions in real-time to help the agent resolve the caller's inquiry.
Reduced Handle Time By empowering agents to more effectively provide answers and taking care of interaction notes, customers enjoy a quicker, more positive experience. Also the agent will have a summary of the interaction, the resolution of it and a reason for that contact.
*You can sort all use cases according to their stated benefits here: Sort by benefits

Summary

During customer interactions, Genesys Cloud Agent Copilot assists agents by presenting relevant, next best actions to the agent in their workspace. Agent Copilot understands customers’ intents and provides agents with relevant answer highlighted knowledge, canned responses, and scripts. When agents move to after call work, Genesys Cloud AI creates an interaction summary and predicts wrap codes.


Use Case Definition

Business Flow

Agent Copilot

Business Flow Description 1.Genesys connects the user to the live agent.

2.The agent sees the context (for example bot intents and slots) of the users journey in the agent desktop.

3.Genesys Agent Copilot monitors the conversation.

4.During the voice conversation, the following happens:

For Voice Interactions:

  • Real-time audio of the voice interaction is streamed to Genesys Transcription service.
  • Agent Copilot displays the real-time transcription of the voice call.
  • Agent Copilot uses Natural Language Understanding to understand Customers Intents
  • Agent Copilot service returns real-time next best actions (knowledge, Canned Response, Script).
  • The suggested action is displayed to the agent automatically in a live stream of suggestions during the conversation.

For Digital Interactions:

  • Agent Copilot uses Natural Language Understanding to understand Customers Intents
  • Agent Copilot service returns real-time next best actions (knowledge, Canned Response, Script).
  • The suggested next best action is displayed to the agent automatically in a live stream of suggestions during the conversation.

5.The agent can do the following with the live stream of suggestions:

  • Click to expand knowledge suggested content to read more or copy to the digital interaction.
  • Click to launch suggested Script pages to follow a script.
  • Click to expand Canned Responses or copy to the digital interaction.

6.The agent can rate (upvote/downvote) to improve the AI suggestions model over time. The more that Agent Copilot is used and content rated by agents, the better the suggestions will be in the future. (BL3, BL4).

7.Once the conversation is completed, it generates a summary of it, the reason the customer contacted and a resolution for that interaction.


Business and Distribution Logic

Business Logic

BL1: Review knowledge: The agent performs a high-level assessment to ensure the information returned from Agent AssistAgent Copilot is appropriate and relevant to the current conversation. 

BL2: Leverage suggestion: The agent communicates relevant information to the end-customer, or they use the information to perform the required "back-end" actions to resolve the customer issue. 

BL3: Rate suggestions: Agent Copilot may provide an agent with multiple pieces of information during the interaction. Agents should rate the information using the thumbs up / thumbs down buttons to verify as relevant or irrelevant. 

BL4: Resolve issue or continue conversation: If the end-customer issue is not adequately resolved, the agent continues the conversation with the end-customer to trigger Agent Copilot to surface additional information. If Agent Copilot is unable to provide appropriate information to resolve the end-customers issue, Agents should follow their corporate escalation policy to ensure that expectations are fulfilled. 

BL5: End Coversation: Agent reviews the AI generated note and Wrap Up Codes. Agent selects the wrap up code from the AI suggested or finds the appropriate code.  


Distribution Logic

Since the end-customer is already speaking with an agent in real time, any subsequent call steering is likely to be manually directed by the agent. 

User Interface & Reporting


Agent UI

N/A

Reporting

Real-time Reporting

The Agent Copilot Performance dashboard for Genesys Agent Copilot gives overview about knowledge base article activities. Genesys Agent Copilot metrics and reporting provides insight about presented, opened and copied articles. For more information, see https://rcstaging.wpengine.com/?p=280180  

Historical Reporting

In the knowledge optimizer dashboard, you can analyze the effectiveness of your knowledge base. In this view, you can see the following metrics:

  • All queries in a specific time frame and the breakdown, in percentages, of answered and unanswered queries.
  • All answered queries in a specific time frame and the breakdown, in percentages, of the application from which the conversation originated.
  • All unanswered queries in a specific time frame and the breakdown, in percentages, of the application from which the conversation originated.
  • Top 20 articles and the frequency in which an article appeared in a conversation.
  • Top 20 answered queries and the frequency in which each answered query appeared in a conversation.
  • Top 20 unanswered queries and the frequency in which each unanswered query appeared in a conversation.

see https://help.mypurecloud.com/articles/knowledge-optimizer-overview/

Customer-facing Considerations

Interdependencies

All required, alternate, and optional use cases are listed here, as well as any exceptions.

All of the following required: At least one of the following required: Optional Exceptions
None

Digital

Inbound

Self-Service and Automation

Workforce Engagement

None


General Assumptions

Customers and/or Genesys Professional Services are responsible for managing the Copilot NLU, rules engine and uploading their own knowledge base content into Genesys Knowledge Workbench to be used by Agent Copilot. 

Customer Responsibilities

Customer needs to provide a KB or the articles that will be the elements of the Knowledge Base.



Document Version

  • Version v 1.0.0 last updated July 16, 2024

Comments or questions about this documentation? Contact us for support!