What AI actually costs in customer service
Implementing AI in customer service requires careful consideration of integration and data work costs, which can be substantial, and evaluating the run cost against query volume to optimise the AI model and reduce costs, as a customer service manager would when working with a data analyst to ensure accurate and up-to-date data.
A customer service dashboard that nobody opens is a common sight in many organisations, a testament to the fact that the cost of implementing AI in customer service is not always as straightforward as it seems. The cost of AI in customer service is often misunderstood, with many assuming it is solely related to the cost of the AI model itself. However, this is not the case, as the majority of the cost is actually attributed to integration and data work.
Integration and data work as the dominant line
The cost of integrating AI into a customer service system can be substantial, and it is often the largest line item in the budget. This is because integrating AI requires significant data work, including data cleaning, data processing, and data storage. The data work required to implement AI in customer service is often underestimated, and it can be a major contributor to the overall cost. For example, a customer service manager at a large retail company may need to work with a data analyst to ensure that the data being fed into the AI model is accurate and up-to-date.
This can be a time-consuming and labor-intensive process, requiring significant resources and expertise. Furthermore, the cost of integration and data work can vary widely depending on the specific requirements of the organisation and the complexity of the AI model being implemented. A shift supervisor checking the maintenance log may notice that the AI system is not performing as expected due to poor data quality, highlighting the importance of getting the data work right.
Run cost against query volume
Another important factor to consider when evaluating the cost of AI in customer service is the run cost against query volume. The cost of running an AI model can be significant, especially if it is handling a large volume of queries. For instance, a category manager comparing two suppliers may need to consider the cost of running the AI model against the volume of queries it is handling. This can help to identify areas where the AI model can be optimised to reduce costs and improve efficiency.
By analysing the run cost against query volume, organisations can gain a better understanding of the true cost of implementing AI in customer service. This can help to inform decisions about how to allocate resources and optimise the AI model to achieve the best possible results. For more information on how to optimise operations, visit our operations page.
The cost of a wrong answer nobody caught
The cost of a wrong answer that nobody caught can be significant, and it is often overlooked when evaluating the cost of AI in customer service. If an AI model provides an incorrect answer to a customer query, and this error is not caught, it can lead to a range of negative consequences, including damage to the organisation's reputation and loss of customer trust. In some cases, this can even lead to financial losses, such as the cost of resolving a dispute or providing a refund.
For example, a customer service representative may use an AI model to provide an answer to a customer query, but if the answer is incorrect, it can lead to further problems down the line. This highlights the importance of having robust quality control processes in place to ensure that AI models are providing accurate and reliable answers. To get in touch with our team and discuss how we can help with this, visit our contact page.
Attributing spend to a process rather than a model
When evaluating the cost of AI in customer service, it is common for organisations to attribute spend to the AI model itself, rather than the process it is supporting. However, this can be misleading, as the true cost of AI is often related to the process it is supporting, rather than the model itself. For instance, the cost of implementing an AI model may be relatively low, but the cost of the process it is supporting, such as data collection and processing, can be much higher.
By attributing spend to the process rather than the model, organisations can gain a more accurate understanding of the true cost of implementing AI in customer service. This can help to inform decisions about how to allocate resources and optimise the AI model to achieve the best possible results. Most people get wrong the fact that the cost of AI is solely related to the cost of the model, but this is not the case, and it is wrong to assume so.
When AI is the wrong choice
While AI can be a powerful tool in customer service, there are times when it is the wrong choice. For example, in situations where the query volume is low, or the queries are highly complex and require a high degree of human judgement, AI may not be the best solution. In these cases, the cost of implementing and maintaining an AI model may outweigh the benefits, and alternative solutions, such as human customer support, may be more effective.
In addition, AI may not be suitable for situations where the customer data is sensitive or confidential, or where there is a high risk of error or inaccuracy. In these cases, the potential risks and consequences of using AI may outweigh the benefits, and alternative solutions should be considered. By carefully evaluating the costs and benefits of AI in customer service, organisations can make informed decisions about when to use AI and when to use alternative solutions.
Frequently asked questions
What are the main costs associated with implementing AI in customer service?
The main costs are integration and data work, which can be substantial, and run costs, which depend on query volume.
How can organisations optimise their AI models to reduce costs?
By analysing the run cost against query volume and identifying areas for optimisation, organisations can reduce costs and improve efficiency.
What are the consequences of a wrong answer provided by an AI model?
A wrong answer can lead to damage to the organisation's reputation, loss of customer trust, and financial losses, highlighting the importance of robust quality control processes.
When is AI not the best solution for customer service?
AI may not be suitable for situations with low query volume, complex queries, or sensitive customer data, and alternative solutions, such as human customer support, may be more effective.