A readiness checklist for customer service AI
Implementing AI in customer service requires a readiness checklist to ensure connected data, agreed-upon definitions, and decided permissions, allowing a shift supervisor to check the maintenance log and provide accurate answers, thus improving customer experience and reducing frustration.
Can we really answer customer queries in real time, or are we just going to end up apologising for the delay? This question is often raised in meetings where the implementation of AI in customer service is being discussed. The truth is, without the right groundwork, the answer will be the latter. In my experience, the biggest obstacle to overcome is not the technology itself, but rather the preparation and planning that needs to take place beforehand.
Connected data is crucial
The data that must be connected includes customer information, order history, and product details. Without this data being properly linked, the AI system will not be able to provide accurate or relevant responses to customer queries. I have seen this happen time and time again, where the AI system is unable to access the necessary data, resulting in a poor customer experience. The mechanism by which this data is connected is also important, as it needs to be done in a way that allows for real-time updates and access.
A shift supervisor, for example, needs to be able to check the maintenance log and see the current status of a customer's order. If this data is not connected, the supervisor will not be able to provide an accurate answer, leading to frustration for the customer. This is not just a matter of convenience, but rather a necessity for providing good customer service.
Definitions need to be agreed upon
Most people get wrong the idea that definitions can be agreed upon on the fly, but this is not the case. Definitions need to be agreed upon in writing before the AI system is implemented. This includes definitions of terms such as "order status" and "product category". Without these definitions being clearly outlined, the AI system will not be able to understand the context of the customer's query, leading to confusion and miscommunication.
I have seen companies try to implement AI without first defining these terms, and it has ended in disaster. The AI system was unable to provide accurate responses, and the customer service team was left to deal with the fallout. It is essential that these definitions are agreed upon and documented before the AI system is implemented.
Permissions need to be decided
Permissions are another crucial aspect that needs to be decided before the AI system is implemented. This includes deciding who has access to the AI system, and what level of access they have. For example, a customer service representative may need to have access to the AI system in order to answer customer queries, but they may not need to have access to the underlying data. Deciding these permissions beforehand is essential for ensuring the security and integrity of the data.
This is particularly important in large organisations, where there may be many different teams and individuals who need to access the AI system. By deciding permissions beforehand, companies can ensure that the right people have access to the right information, and that sensitive data is protected. For more information on how to manage permissions, see our operations guide.
A first question worth answering
A good first question to answer is one that customer service already waits on, such as "what is the status of my order?". This type of question is ideal because it is a common query that customer service teams receive, and it is something that the AI system can easily answer. By starting with a question like this, companies can test the AI system and ensure that it is working correctly before moving on to more complex queries.
For example, a category manager may want to compare two different suppliers, and the AI system can provide them with the necessary information to make an informed decision. By starting with a simple question like this, companies can build confidence in the AI system and ensure that it is providing accurate and relevant responses. If you have any questions about how to get started with AI in customer service, please contact us.
What this does not do
This AI checklist for customer service does not provide a magic solution for all customer service problems. It is not a replacement for human customer service representatives, but rather a tool to help them provide better service. It is also not a guarantee that every customer query will be answered correctly, but rather a way to improve the accuracy and relevance of responses. Companies need to be realistic about what the AI system can achieve, and not expect it to solve all their customer service problems overnight.
In some cases, the AI system may not be the right choice for customer service. For example, if the company has a very complex product or service, the AI system may not be able to provide accurate or relevant responses. In these cases, it may be better to stick with human customer service representatives who can provide more nuanced and detailed responses. Ultimately, the decision to implement an AI system in customer service depends on the specific needs and goals of the company.
Frequently asked questions
What are the key elements of a readiness checklist for customer service AI?
Connected data, agreed-upon definitions, and decided permissions are crucial for a successful implementation.
Why are definitions important for customer service AI?
Definitions need to be agreed upon in writing to ensure the AI system understands the context of customer queries, preventing confusion and miscommunication.
How can permissions be decided for customer service AI?
Permissions should be decided beforehand, ensuring the right people have access to the right information and sensitive data is protected.
What type of question is ideal for testing customer service AI?
A good first question to answer is one that customer service already waits on, such as "what is the status of my order?", allowing companies to test the AI system and build confidence.