Genesys Cloud - Main

Over the past months, we've been working on the implementation of Genesys Agent Copilot in a fintech environment, and I'd like to share some learnings from this journey.

The Copilot rollout started with 20 agents in a chat and messaging operation, allowing us to validate the strategy, fine-tune configurations, and measure impact before scaling.

Knowledge Base as the foundation

We began with a strong focus on Knowledge Management, creating articles tied to the highest-volume customer contact reasons and gradually expanding to less frequent topics.
This prioritization was key to ensuring that Copilot delivered relevant and actionable suggestions where they mattered most during live interactions.

Predefined responses to support agent agility

We configured predefined responses covering essential parts of the service script, such as:

• Opening messages

• Closing messages

• Mandatory compliance communications

This significantly improved agent speed and consistency during conversations.
It's important to note that this approach complemented, rather than replaced, the native Canned Responses feature, which agents continued to use through its dedicated interface during interactions.

Automated summaries integrated with CRM

For interaction summaries, we implemented an automation using triggers and workflows to retrieve the Copilot-generated summary and automatically store it in the customer case/occurrence within the company's CRM.
This eliminated the need for agents to manually document the interaction, freeing up time and reducing after-contact work.

Tabulation optimization and accuracy

We also conducted a deep review of disposition codes:

• Removed obsolete and redundant options through a tabulation "hygienization" process

• Applied a Pareto analysis to retain clear and objective descriptions covering ~80% of interactions

This restructuring significantly increased the accuracy of Copilot's tabulation suggestions, improving both data quality and agent confidence.

Measurable operational impact

With all these actions combined, the operation achieved a reduction in Average Handling Time (AHT) from approximately 15 minutes to around 9 minutes.

Scaling and expansion

Once the process was stabilized, Copilot was expanded to 100% of chat and messaging agents, followed by the rollout to voice operations, applying the same best practices validated earlier.

Today, this fintech operates with Copilot enabled across all customer-facing positions, using it as a core component of agent support and operational efficiency.

It's also important to highlight that none of these results would be possible without strong agent adoption and engagement.

From the beginning, we understood that Copilot is only effective if agents truly incorporate it into their daily workflow. To support this, we implemented a custom gamification model using Genesys data related to individual Copilot usage per agent.

Based on these insights, we created a ranking focused on Copilot adoption and usage, and the top 3 agents with the highest engagement were recognized and rewarded.
This approach proved to be a key success factor, significantly increasing engagement, adherence, and trust in the Copilot recommendations.

By combining technology, process optimization, and people engagement, Copilot moved beyond being just a feature and became a natural part of the agent experience.

I'd love to hear from others in the community:
What insights or best practices have you applied when using Agent Copilot in your operations?
Looking forward to exchanging experiences and learning how we can continue evolving this approach together.

#SuccessStories

------------------------------
Mateus Nunes
CX Tech Leader at Solve4ME
------------------------------

Over the past months, we've been working on the implementation of Genesys Agent Copilot in a fintech environment, and I'd like to share some learnings from this journey.

The Copilot rollout started with 20 agents in a chat and messaging operation, allowing us to validate the strategy, fine-tune configurations, and measure impact before scaling.

Knowledge Base as the foundation

We began with a strong focus on Knowledge Management, creating articles tied to the highest-volume customer contact reasons and gradually expanding to less frequent topics.
This prioritization was key to ensuring that Copilot delivered relevant and actionable suggestions where they mattered most during live interactions.

Predefined responses to support agent agility

We configured predefined responses covering essential parts of the service script, such as:

• Opening messages

• Closing messages

• Mandatory compliance communications

This significantly improved agent speed and consistency during conversations.
It's important to note that this approach complemented, rather than replaced, the native Canned Responses feature, which agents continued to use through its dedicated interface during interactions.

Automated summaries integrated with CRM

For interaction summaries, we implemented an automation using triggers and workflows to retrieve the Copilot-generated summary and automatically store it in the customer case/occurrence within the company's CRM.
This eliminated the need for agents to manually document the interaction, freeing up time and reducing after-contact work.

Tabulation optimization and accuracy

We also conducted a deep review of disposition codes:

• Removed obsolete and redundant options through a tabulation "hygienization" process

• Applied a Pareto analysis to retain clear and objective descriptions covering ~80% of interactions

This restructuring significantly increased the accuracy of Copilot's tabulation suggestions, improving both data quality and agent confidence.

Measurable operational impact

With all these actions combined, the operation achieved a reduction in Average Handling Time (AHT) from approximately 15 minutes to around 9 minutes.

Scaling and expansion

Once the process was stabilized, Copilot was expanded to 100% of chat and messaging agents, followed by the rollout to voice operations, applying the same best practices validated earlier.

Today, this fintech operates with Copilot enabled across all customer-facing positions, using it as a core component of agent support and operational efficiency.

It's also important to highlight that none of these results would be possible without strong agent adoption and engagement.

From the beginning, we understood that Copilot is only effective if agents truly incorporate it into their daily workflow. To support this, we implemented a custom gamification model using Genesys data related to individual Copilot usage per agent.

Based on these insights, we created a ranking focused on Copilot adoption and usage, and the top 3 agents with the highest engagement were recognized and rewarded.
This approach proved to be a key success factor, significantly increasing engagement, adherence, and trust in the Copilot recommendations.

By combining technology, process optimization, and people engagement, Copilot moved beyond being just a feature and became a natural part of the agent experience.

I'd love to hear from others in the community:
What insights or best practices have you applied when using Agent Copilot in your operations?
Looking forward to exchanging experiences and learning how we can continue evolving this approach together.

#SuccessStories

------------------------------
Mateus Nunes
CX Tech Leader at Solve4ME
------------------------------

Hello Mateus,

Thank you for your experience sharing.

I'm referring about reduction in Average Handling Time (AHT) from approximately 15 minutes to around 9 minutes : do you have the differentiation between Talk Time reduction (thanks to Agent Assit) and After Call Work especially related to Call Summarization ?

Already thanks,

Olivier

Over the past months, we've been working on the implementation of Genesys Agent Copilot in a fintech environment, and I'd like to share some learnings from this journey.

The Copilot rollout started with 20 agents in a chat and messaging operation, allowing us to validate the strategy, fine-tune configurations, and measure impact before scaling.

Knowledge Base as the foundation

We began with a strong focus on Knowledge Management, creating articles tied to the highest-volume customer contact reasons and gradually expanding to less frequent topics.
This prioritization was key to ensuring that Copilot delivered relevant and actionable suggestions where they mattered most during live interactions.

Predefined responses to support agent agility

We configured predefined responses covering essential parts of the service script, such as:

• Opening messages

• Closing messages

• Mandatory compliance communications

This significantly improved agent speed and consistency during conversations.
It's important to note that this approach complemented, rather than replaced, the native Canned Responses feature, which agents continued to use through its dedicated interface during interactions.

Automated summaries integrated with CRM

For interaction summaries, we implemented an automation using triggers and workflows to retrieve the Copilot-generated summary and automatically store it in the customer case/occurrence within the company's CRM.
This eliminated the need for agents to manually document the interaction, freeing up time and reducing after-contact work.

Tabulation optimization and accuracy

We also conducted a deep review of disposition codes:

• Removed obsolete and redundant options through a tabulation "hygienization" process

• Applied a Pareto analysis to retain clear and objective descriptions covering ~80% of interactions

This restructuring significantly increased the accuracy of Copilot's tabulation suggestions, improving both data quality and agent confidence.

Measurable operational impact

With all these actions combined, the operation achieved a reduction in Average Handling Time (AHT) from approximately 15 minutes to around 9 minutes.

Scaling and expansion

Once the process was stabilized, Copilot was expanded to 100% of chat and messaging agents, followed by the rollout to voice operations, applying the same best practices validated earlier.

Today, this fintech operates with Copilot enabled across all customer-facing positions, using it as a core component of agent support and operational efficiency.

It's also important to highlight that none of these results would be possible without strong agent adoption and engagement.

From the beginning, we understood that Copilot is only effective if agents truly incorporate it into their daily workflow. To support this, we implemented a custom gamification model using Genesys data related to individual Copilot usage per agent.

Based on these insights, we created a ranking focused on Copilot adoption and usage, and the top 3 agents with the highest engagement were recognized and rewarded.
This approach proved to be a key success factor, significantly increasing engagement, adherence, and trust in the Copilot recommendations.

By combining technology, process optimization, and people engagement, Copilot moved beyond being just a feature and became a natural part of the agent experience.

I'd love to hear from others in the community:
What insights or best practices have you applied when using Agent Copilot in your operations?
Looking forward to exchanging experiences and learning how we can continue evolving this approach together.

#SuccessStories

------------------------------
Mateus Nunes
CX Tech Leader at Solve4ME
------------------------------

Over the past months, we've been working on the implementation of Genesys Agent Copilot in a fintech environment, and I'd like to share some learnings from this journey.

The Copilot rollout started with 20 agents in a chat and messaging operation, allowing us to validate the strategy, fine-tune configurations, and measure impact before scaling.

Knowledge Base as the foundation

We began with a strong focus on Knowledge Management, creating articles tied to the highest-volume customer contact reasons and gradually expanding to less frequent topics.
This prioritization was key to ensuring that Copilot delivered relevant and actionable suggestions where they mattered most during live interactions.

Predefined responses to support agent agility

We configured predefined responses covering essential parts of the service script, such as:

• Opening messages

• Closing messages

• Mandatory compliance communications

This significantly improved agent speed and consistency during conversations.
It's important to note that this approach complemented, rather than replaced, the native Canned Responses feature, which agents continued to use through its dedicated interface during interactions.

Automated summaries integrated with CRM

For interaction summaries, we implemented an automation using triggers and workflows to retrieve the Copilot-generated summary and automatically store it in the customer case/occurrence within the company's CRM.
This eliminated the need for agents to manually document the interaction, freeing up time and reducing after-contact work.

Tabulation optimization and accuracy

We also conducted a deep review of disposition codes:

• Removed obsolete and redundant options through a tabulation "hygienization" process

• Applied a Pareto analysis to retain clear and objective descriptions covering ~80% of interactions

This restructuring significantly increased the accuracy of Copilot's tabulation suggestions, improving both data quality and agent confidence.

Measurable operational impact

With all these actions combined, the operation achieved a reduction in Average Handling Time (AHT) from approximately 15 minutes to around 9 minutes.

Scaling and expansion

Once the process was stabilized, Copilot was expanded to 100% of chat and messaging agents, followed by the rollout to voice operations, applying the same best practices validated earlier.

Today, this fintech operates with Copilot enabled across all customer-facing positions, using it as a core component of agent support and operational efficiency.

It's also important to highlight that none of these results would be possible without strong agent adoption and engagement.

From the beginning, we understood that Copilot is only effective if agents truly incorporate it into their daily workflow. To support this, we implemented a custom gamification model using Genesys data related to individual Copilot usage per agent.

Based on these insights, we created a ranking focused on Copilot adoption and usage, and the top 3 agents with the highest engagement were recognized and rewarded.
This approach proved to be a key success factor, significantly increasing engagement, adherence, and trust in the Copilot recommendations.

By combining technology, process optimization, and people engagement, Copilot moved beyond being just a feature and became a natural part of the agent experience.

I'd love to hear from others in the community:
What insights or best practices have you applied when using Agent Copilot in your operations?
Looking forward to exchanging experiences and learning how we can continue evolving this approach together.

#SuccessStories

------------------------------
Mateus Nunes
CX Tech Leader at Solve4ME
------------------------------

Over the past months, we've been working on the implementation of Genesys Agent Copilot in a fintech environment, and I'd like to share some learnings from this journey.

The Copilot rollout started with 20 agents in a chat and messaging operation, allowing us to validate the strategy, fine-tune configurations, and measure impact before scaling.

Knowledge Base as the foundation

We began with a strong focus on Knowledge Management, creating articles tied to the highest-volume customer contact reasons and gradually expanding to less frequent topics.
This prioritization was key to ensuring that Copilot delivered relevant and actionable suggestions where they mattered most during live interactions.

Predefined responses to support agent agility

We configured predefined responses covering essential parts of the service script, such as:

• Opening messages

• Closing messages

• Mandatory compliance communications

This significantly improved agent speed and consistency during conversations.
It's important to note that this approach complemented, rather than replaced, the native Canned Responses feature, which agents continued to use through its dedicated interface during interactions.

Automated summaries integrated with CRM

For interaction summaries, we implemented an automation using triggers and workflows to retrieve the Copilot-generated summary and automatically store it in the customer case/occurrence within the company's CRM.
This eliminated the need for agents to manually document the interaction, freeing up time and reducing after-contact work.

Tabulation optimization and accuracy

We also conducted a deep review of disposition codes:

• Removed obsolete and redundant options through a tabulation "hygienization" process

• Applied a Pareto analysis to retain clear and objective descriptions covering ~80% of interactions

This restructuring significantly increased the accuracy of Copilot's tabulation suggestions, improving both data quality and agent confidence.

Measurable operational impact

With all these actions combined, the operation achieved a reduction in Average Handling Time (AHT) from approximately 15 minutes to around 9 minutes.

Scaling and expansion

Once the process was stabilized, Copilot was expanded to 100% of chat and messaging agents, followed by the rollout to voice operations, applying the same best practices validated earlier.

Today, this fintech operates with Copilot enabled across all customer-facing positions, using it as a core component of agent support and operational efficiency.

It's also important to highlight that none of these results would be possible without strong agent adoption and engagement.

From the beginning, we understood that Copilot is only effective if agents truly incorporate it into their daily workflow. To support this, we implemented a custom gamification model using Genesys data related to individual Copilot usage per agent.

Based on these insights, we created a ranking focused on Copilot adoption and usage, and the top 3 agents with the highest engagement were recognized and rewarded.
This approach proved to be a key success factor, significantly increasing engagement, adherence, and trust in the Copilot recommendations.

By combining technology, process optimization, and people engagement, Copilot moved beyond being just a feature and became a natural part of the agent experience.

I'd love to hear from others in the community:
What insights or best practices have you applied when using Agent Copilot in your operations?
Looking forward to exchanging experiences and learning how we can continue evolving this approach together.

#SuccessStories

------------------------------
Mateus Nunes
CX Tech Leader at Solve4ME
------------------------------

Over the past months, we've been working on the implementation of Genesys Agent Copilot in a fintech environment, and I'd like to share some learnings from this journey.

The Copilot rollout started with 20 agents in a chat and messaging operation, allowing us to validate the strategy, fine-tune configurations, and measure impact before scaling.

Knowledge Base as the foundation

We began with a strong focus on Knowledge Management, creating articles tied to the highest-volume customer contact reasons and gradually expanding to less frequent topics.
This prioritization was key to ensuring that Copilot delivered relevant and actionable suggestions where they mattered most during live interactions.

Predefined responses to support agent agility

We configured predefined responses covering essential parts of the service script, such as:

• Opening messages

• Closing messages

• Mandatory compliance communications

This significantly improved agent speed and consistency during conversations.
It's important to note that this approach complemented, rather than replaced, the native Canned Responses feature, which agents continued to use through its dedicated interface during interactions.

Automated summaries integrated with CRM

For interaction summaries, we implemented an automation using triggers and workflows to retrieve the Copilot-generated summary and automatically store it in the customer case/occurrence within the company's CRM.
This eliminated the need for agents to manually document the interaction, freeing up time and reducing after-contact work.

Tabulation optimization and accuracy

We also conducted a deep review of disposition codes:

• Removed obsolete and redundant options through a tabulation "hygienization" process

• Applied a Pareto analysis to retain clear and objective descriptions covering ~80% of interactions

This restructuring significantly increased the accuracy of Copilot's tabulation suggestions, improving both data quality and agent confidence.

Measurable operational impact

With all these actions combined, the operation achieved a reduction in Average Handling Time (AHT) from approximately 15 minutes to around 9 minutes.

Scaling and expansion

Once the process was stabilized, Copilot was expanded to 100% of chat and messaging agents, followed by the rollout to voice operations, applying the same best practices validated earlier.

Today, this fintech operates with Copilot enabled across all customer-facing positions, using it as a core component of agent support and operational efficiency.

It's also important to highlight that none of these results would be possible without strong agent adoption and engagement.

From the beginning, we understood that Copilot is only effective if agents truly incorporate it into their daily workflow. To support this, we implemented a custom gamification model using Genesys data related to individual Copilot usage per agent.

Based on these insights, we created a ranking focused on Copilot adoption and usage, and the top 3 agents with the highest engagement were recognized and rewarded.
This approach proved to be a key success factor, significantly increasing engagement, adherence, and trust in the Copilot recommendations.

By combining technology, process optimization, and people engagement, Copilot moved beyond being just a feature and became a natural part of the agent experience.

I'd love to hear from others in the community:
What insights or best practices have you applied when using Agent Copilot in your operations?
Looking forward to exchanging experiences and learning how we can continue evolving this approach together.

#SuccessStories

------------------------------
Mateus Nunes
CX Tech Leader at Solve4ME
------------------------------

Over the past months, we've been working on the implementation of Genesys Agent Copilot in a fintech environment, and I'd like to share some learnings from this journey.

The Copilot rollout started with 20 agents in a chat and messaging operation, allowing us to validate the strategy, fine-tune configurations, and measure impact before scaling.

Knowledge Base as the foundation

We began with a strong focus on Knowledge Management, creating articles tied to the highest-volume customer contact reasons and gradually expanding to less frequent topics.
This prioritization was key to ensuring that Copilot delivered relevant and actionable suggestions where they mattered most during live interactions.

Predefined responses to support agent agility

We configured predefined responses covering essential parts of the service script, such as:

• Opening messages

• Closing messages

• Mandatory compliance communications

This significantly improved agent speed and consistency during conversations.
It's important to note that this approach complemented, rather than replaced, the native Canned Responses feature, which agents continued to use through its dedicated interface during interactions.

Automated summaries integrated with CRM

For interaction summaries, we implemented an automation using triggers and workflows to retrieve the Copilot-generated summary and automatically store it in the customer case/occurrence within the company's CRM.
This eliminated the need for agents to manually document the interaction, freeing up time and reducing after-contact work.

Tabulation optimization and accuracy

We also conducted a deep review of disposition codes:

• Removed obsolete and redundant options through a tabulation "hygienization" process

• Applied a Pareto analysis to retain clear and objective descriptions covering ~80% of interactions

This restructuring significantly increased the accuracy of Copilot's tabulation suggestions, improving both data quality and agent confidence.

Measurable operational impact

With all these actions combined, the operation achieved a reduction in Average Handling Time (AHT) from approximately 15 minutes to around 9 minutes.

Scaling and expansion

Once the process was stabilized, Copilot was expanded to 100% of chat and messaging agents, followed by the rollout to voice operations, applying the same best practices validated earlier.

Today, this fintech operates with Copilot enabled across all customer-facing positions, using it as a core component of agent support and operational efficiency.

It's also important to highlight that none of these results would be possible without strong agent adoption and engagement.

From the beginning, we understood that Copilot is only effective if agents truly incorporate it into their daily workflow. To support this, we implemented a custom gamification model using Genesys data related to individual Copilot usage per agent.

Based on these insights, we created a ranking focused on Copilot adoption and usage, and the top 3 agents with the highest engagement were recognized and rewarded.
This approach proved to be a key success factor, significantly increasing engagement, adherence, and trust in the Copilot recommendations.

By combining technology, process optimization, and people engagement, Copilot moved beyond being just a feature and became a natural part of the agent experience.

I'd love to hear from others in the community:
What insights or best practices have you applied when using Agent Copilot in your operations?
Looking forward to exchanging experiences and learning how we can continue evolving this approach together.

#SuccessStories

------------------------------
Mateus Nunes
CX Tech Leader at Solve4ME
------------------------------

Hi @Mateus Nunes,

Thanks for sharing your success story! I am a Product Manager at Genesys and we are making some updates to the Agent Copilot Performance Dashboard in particular adding the ability to filter by Copilots as well as queues and the ability to do a comparison on those in order to calculate Average Handle Time (AHT) and After Call Work (ACW) on the Copilot/Queue using Copilot and the Copilot/Queue not using Copilot to show the reduction in time.

I am interested in finding out what did you compare, e.g. the same queues, different queues, single or multiple Copilots, did you filter by agents, did you need to do anything to ensure it was a fair comparison, e.g. make sure the number of interactions were balanced. It would be great to find out more about how you calculated this so we can keep this in mind when updating our dashboard.

Thanks,

Nicola

Over the past months, we've been working on the implementation of Genesys Agent Copilot in a fintech environment, and I'd like to share some learnings from this journey.

The Copilot rollout started with 20 agents in a chat and messaging operation, allowing us to validate the strategy, fine-tune configurations, and measure impact before scaling.

Knowledge Base as the foundation

We began with a strong focus on Knowledge Management, creating articles tied to the highest-volume customer contact reasons and gradually expanding to less frequent topics.
This prioritization was key to ensuring that Copilot delivered relevant and actionable suggestions where they mattered most during live interactions.

Predefined responses to support agent agility

We configured predefined responses covering essential parts of the service script, such as:

• Opening messages

• Closing messages

• Mandatory compliance communications

This significantly improved agent speed and consistency during conversations.
It's important to note that this approach complemented, rather than replaced, the native Canned Responses feature, which agents continued to use through its dedicated interface during interactions.

Automated summaries integrated with CRM

For interaction summaries, we implemented an automation using triggers and workflows to retrieve the Copilot-generated summary and automatically store it in the customer case/occurrence within the company's CRM.
This eliminated the need for agents to manually document the interaction, freeing up time and reducing after-contact work.

Tabulation optimization and accuracy

We also conducted a deep review of disposition codes:

• Removed obsolete and redundant options through a tabulation "hygienization" process

• Applied a Pareto analysis to retain clear and objective descriptions covering ~80% of interactions

This restructuring significantly increased the accuracy of Copilot's tabulation suggestions, improving both data quality and agent confidence.

Measurable operational impact

With all these actions combined, the operation achieved a reduction in Average Handling Time (AHT) from approximately 15 minutes to around 9 minutes.

Scaling and expansion

Once the process was stabilized, Copilot was expanded to 100% of chat and messaging agents, followed by the rollout to voice operations, applying the same best practices validated earlier.

Today, this fintech operates with Copilot enabled across all customer-facing positions, using it as a core component of agent support and operational efficiency.

It's also important to highlight that none of these results would be possible without strong agent adoption and engagement.

From the beginning, we understood that Copilot is only effective if agents truly incorporate it into their daily workflow. To support this, we implemented a custom gamification model using Genesys data related to individual Copilot usage per agent.

Based on these insights, we created a ranking focused on Copilot adoption and usage, and the top 3 agents with the highest engagement were recognized and rewarded.
This approach proved to be a key success factor, significantly increasing engagement, adherence, and trust in the Copilot recommendations.

By combining technology, process optimization, and people engagement, Copilot moved beyond being just a feature and became a natural part of the agent experience.

I'd love to hear from others in the community:
What insights or best practices have you applied when using Agent Copilot in your operations?
Looking forward to exchanging experiences and learning how we can continue evolving this approach together.

#SuccessStories

------------------------------
Mateus Nunes
CX Tech Leader at Solve4ME
------------------------------