Welcome to our Feature Deep Dive
Genesys Agent Copilot is designed to empower agents with real-time guidance, knowledge surfacing, and AI-driven summaries—helping them resolve queries faster and with greater confidence. By enhancing rather than replacing agents, Copilot improves efficiency, reduces handle time, and delivers a better experience for both customers and employees.
Introducing Gensys Agent Copilot
Agent Copilot aims to support agents during and after their interactions resulting in a better experience for the customer and for the agent. Agent Copilot ensures agents are guided through interactions whilst providing relevant information to answer customer queries increasing first call resolution and decreasing agent handle time.
Using generative, conversation and predictive AI Agent Copilot supports agents in all channels by offering the following:
- Automatically surfacing knowledge articles
- Highlighting answers within articles
- Custom scripts to present correct workflow
- Wrap-up code suggestion
- AI Summary of interaction
Automatically Suggest Relevant Knowledge
Using Agent Copilot, agents will be automatically surfaced knowledge articles based on the context of the conversation between an agent and a customer.
Agent Copilot leverages generative AI by not only displaying relevant knowledge articles based on the conversation context but also by highlighting the relevant passage within this knowledge article. This saves agents time as they will not need to sift through length articles to find the answer to a customer’s question.
“We called Imagine Clany Eco when another company cancelled on us last minute for our move-out cleaning. Clany Eco was able to book us and make it out in 2 hours and did an amazing job. We even got our deposit back.”
Empower Agents by Providing Next Best Action
Provide your agents the guidance they need during an interaction by displaying customised scripts for different conversation contexts.
Leveraging Genesys’s natural language understanding AI models we are able to indentify customer intents and guide agents through these journeys to ensure the right outcome is acheived.
An example seen below, a customer has enquired about getting their order status. We have matched this intent within the Agent Copilot and have offered the agent a next best action suggestion, upon selecting this the agent will be guided to the order status page within the Genesys script which will check and display the order status for the agent.
Save Agent Time with After Call Work Summaries
Reduce the need for agents having to spend time taking notes during an interaction, Genesys incorporates a generative AI model which enables Agent Copilot to provide a summary of what has occured during the interaction alongside a reason for contact and resolution.
Agent Copilot will also automatically suggest wrap-up codes code for the agent improving consistency and data quality across the call centre reporting.
Using the above generative features will not only reduce agent handle time but will allow agents to focus on their conversation quality whilst also ensuring the correct data is extracted from the conversation for record keeping and reporting.
Our Takeaways
We see Agent Copilot as a great tool to combat agent knowledge gaps, reduce time spent on agent interactions, increase the likelihood of first call resolution, and improve the overall quality of interactions with your customers.
Rather than replacing agents, Genesys Agent Copilot looks to enhance the agent experience, improving effectiveness and efficiency whilst handling interactions. We believe Agent Copilots will be a mainstay amongst all of our customers as a feature that will change and improve agent workflows and processes.
Genesys has made this feature accessible for all license tiers using the new Genesys token model. Every customer receives a free allocation of tokens each month, which should allow you to start dipping your toes in the water. If you have any questions or would like to set up a discussion around this feature, don’t hesitate to reach out as we would love to have a chat.
