Adoption and change for customer service
Enterprise AI adoption for customer service streamlines workflows by integrating with existing software, allowing representatives to access information quickly and provide informed answers, thus increasing first contact resolution and reducing repeat contact rates, as seen when a customer service representative uses the system to resolve a issue.
How often do customer service teams ask the same questions, only to get the same answers, without ever resolving the underlying issue, and what can be done to change this pattern? The answer lies in the way new capabilities are integrated into existing workflows, and it is a problem that affects many organisations, including those that have implemented enterprise AI solutions for customer service.
Attaching new capability to a routine that already exists
One of the main reasons capable systems go unused is that they are not attached to a routine that already exists. For example, a customer service representative may have to navigate multiple systems to find the information they need, which can be time-consuming and frustrating. By attaching the new capability to a routine that already exists, such as the customer service software, the representative can access the information they need quickly and easily.
This can be achieved by integrating the enterprise AI system with the existing customer service software, allowing representatives to ask questions and get answers in real-time, without having to leave the system they are already using. This can be a powerful way to increase adoption and change the way customer service teams work.
Who needs to be able to ask, not just who needs the answer
Most people get wrong the idea that only certain people need to be able to ask questions, but the reality is that anyone who interacts with customers needs to be able to get answers quickly and easily. This includes not just customer service representatives, but also sales teams, and even managers who need to escalate issues.
By giving more people the ability to ask questions, organisations can ensure that customers get the answers they need, when they need them. This can be particularly important in large organisations, where different teams may have different levels of access to information. By using an enterprise AI system, organisations can provide a single source of truth, that anyone can access.
The customer service data this depends on
The ability to ask questions and get answers depends on having access to a wide range of customer service data, including ticket history, knowledge base, product and order records, and escalation history. This data needs to be up-to-date and accurate, in order for the system to provide reliable answers.
For example, a shift supervisor checks the maintenance log to see if a particular issue has been reported before, and if so, what the resolution was. This information can be used to provide a better answer to the customer, and to prevent the same issue from being reported multiple times. By integrating this data with the enterprise AI system, organisations can provide a more complete and accurate picture of the customer's issue.
What it changes about the same issue answered from scratch each time
One of the most significant changes that enterprise AI can bring to customer service is the ability to learn from previous interactions, and to use that knowledge to provide better answers. This means that the same issue is not answered from scratch each time, but rather the system can draw on its previous experience to provide a more informed answer.
This can also help to identify root causes of issues, and to feed that information back to the product team, so that they can make changes to prevent the issue from happening in the first place. For example, if a particular product is consistently causing issues, the product team can use that information to make changes to the product, to prevent those issues from happening in the future. To learn more about how this can be applied in operations, visit our operations page.
Measured with first contact resolution, time to answer, repeat contact rate
The success of an enterprise AI system for customer service can be measured in a number of ways, including first contact resolution, time to answer, and repeat contact rate. These metrics can provide a clear picture of how well the system is working, and where improvements can be made.
For example, if the first contact resolution rate is low, it may indicate that the system is not providing accurate answers, or that the customer service representatives are not using the system effectively. By tracking these metrics, organisations can make changes to improve the system, and to provide better service to their customers. If you have any questions about how to implement an enterprise AI system for customer service, please contact us.
What this does not do
This is not a replacement for human customer service representatives, but rather a tool to help them provide better service to customers. It is also not a way to simply automate existing processes, but rather a way to provide a more informed and personalized answer to customers.
By understanding what enterprise AI can and cannot do, organisations can make informed decisions about how to use the technology to improve their customer service. It is wrong to think that enterprise AI is a silver bullet that can solve all customer service issues, but rather it is a powerful tool that can be used to provide better service to customers, when used correctly.
Frequently asked questions
How can enterprise AI improve customer service workflows?
Enterprise AI can improve customer service workflows by providing quick access to information and allowing representatives to ask questions and get answers in real-time.
What are the benefits of integrating enterprise AI with existing customer service software?
Integrating enterprise AI with existing customer service software allows representatives to access information quickly and provide informed answers, increasing first contact resolution and reducing repeat contact rates.
Who can benefit from using an enterprise AI system for customer service?
Anyone who interacts with customers can benefit from using an enterprise AI system, including customer service representatives, sales teams, and managers who need to escalate issues.
How can the success of an enterprise AI system for customer service be measured?
The success of an enterprise AI system for customer service can be measured by tracking metrics such as first contact resolution, time to answer, and repeat contact rate.