• Written by Muhammed Abdulla NC, CEO

The notion that AI can reduce the same issue being answered from scratch each time is often met with skepticism, with many believing it is a pipe dream, but this objection is misguided as it overlooks the potential of AI to learn from an organisation's existing data. In reality, AI can be a powerful tool in reducing repetitive queries, but only if the right data is in place. A shift supervisor checks the maintenance log to see if a similar issue has been resolved before, and this is where AI can help, by providing quick access to this information.

What has to be in the data for this to be possible

To reduce the same issue being answered from scratch each time, the data must include a comprehensive ticket history and knowledge base. This allows the AI to learn from previous interactions and provide accurate responses to similar queries. For instance, a customer service representative can use AsscherAi to quickly search the ticket history and knowledge base to find relevant information, rather than starting from scratch. The ticket history provides a record of previous issues and their resolutions, while the knowledge base contains information on common problems and their solutions.

A well-maintained knowledge base is crucial, as it provides the foundation for the AI to learn and improve its responses. This includes not only the solutions to common problems but also the context in which they occur, allowing the AI to provide more accurate and relevant responses. By having this information in place, organisations can ensure that their AI system is equipped to handle repetitive queries efficiently.

The mechanism, stated plainly

The mechanism by which AI reduces the same issue being answered from scratch each time is straightforward: the AI system is trained on the organisation's data, including the ticket history and knowledge base, and uses this training to provide responses to new queries. When a new query is received, the AI system searches the data to find relevant information and provides a response based on this information. This process is often faster and more accurate than a human representative starting from scratch, as the AI can quickly search and retrieve relevant information from the data.

Most people get wrong the idea that AI will somehow magically fix all their customer service issues, but this is wrong, as AI is only as good as the data it is trained on. If the data is incomplete or inaccurate, the AI system will not be able to provide accurate responses, and may even provide misleading information. Therefore, it is essential to ensure that the data is well-maintained and up-to-date.

What it will not fix

While AI can reduce the same issue being answered from scratch each time, it will not fix underlying issues with the organisation's operations. For example, if a particular issue is occurring repeatedly due to a problem with the organisation's operations, the AI system will not be able to fix this problem, but rather will simply provide a response to the query. To truly resolve the issue, the organisation must address the underlying problem, rather than just treating the symptoms.

This is a common mistake that organisations make, trying to use AI as a band-aid to fix deeper issues, rather than taking the time to address the root cause of the problem. By doing so, they may be able to reduce the number of queries they receive, but they will not be able to provide a better service to their customers.

How you would know it worked: first contact resolution

One way to measure the effectiveness of AI in reducing the same issue being answered from scratch each time is to track the first contact resolution rate. This measures the percentage of queries that are resolved on the first contact, without the need for further follow-up. If the AI system is working effectively, this rate should increase, as the AI is able to provide accurate and relevant responses to queries, resolving the issue on the first contact. A category manager compares two suppliers to see which one provides the best service, and first contact resolution is a key metric in this comparison.

By tracking this metric, organisations can see the tangible benefits of using AI to reduce repetitive queries, and can make adjustments as needed to further improve the system. This may involve updating the knowledge base or providing additional training to the AI system, to ensure that it is equipped to handle the types of queries that are being received.

What this does not do, or when it is the wrong choice

AI is not a replacement for human customer service representatives, and there are certain situations where it is not the best choice. For example, in complex or emotionally charged situations, a human representative is often better equipped to handle the query, as they can provide empathy and understanding in a way that AI systems currently cannot. If you are looking for a more personal touch, you can contact us directly, and we will be happy to help.

In these situations, the AI system may actually do more harm than good, providing responses that are insensitive or unhelpful. Therefore, it is essential to carefully consider when to use AI and when to use human representatives, to ensure that customers receive the best possible service. By doing so, organisations can provide a better experience for their customers, and improve their overall satisfaction with the service.