• Written by Azhar Aliyar, Director

The service desk ticket queue for the AsscherAi platform is a critical component of our support infrastructure, and one of the key metrics we use to measure its performance is the AI SLA, or service level agreement, which defines the expected performance of the system in terms of response times and accuracy. An AI SLA is a formal agreement between the provider of an AI system, such as Harns Technologies, and the user of that system, which outlines the expected performance of the system in terms of specific metrics, such as response times, accuracy, and availability.

Definition of an AI SLA

An AI SLA is a specific type of service level agreement that is designed to address the unique challenges of AI systems, which can be complex and difficult to predict. It provides a clear understanding of what the system is capable of, and what the user can expect in terms of performance and support. This includes details such as the expected response time for queries, the accuracy of the results, and the procedures for escalating issues.

The AI SLA is an essential component of the AsscherAi platform, as it provides a clear understanding of what the system is capable of, and what the user can expect in terms of performance and support. This helps to manage expectations and ensure that the system is used effectively.

Difference from other SLAs

One of the key challenges in understanding AI SLAs is that they are often confused with other types of service level agreements, such as those used for IT infrastructure or application support. However, AI SLAs are distinct in that they address the unique challenges of AI systems, which can be complex and difficult to predict. While traditional SLAs may focus on metrics such as uptime and response times, AI SLAs must also consider factors such as data quality, model accuracy, and explainability.

For example, a traditional SLA for a website might guarantee a certain level of uptime and response time, but an AI SLA for a natural language processing system would need to consider factors such as the accuracy of the results, the ability of the system to handle ambiguity and uncertainty, and the procedures for updating and refining the models.

Applicability in a large organisation

In a large organisation, an AI SLA can be a critical component of the overall IT strategy, as it provides a clear understanding of what the AI system is capable of, and what the user can expect in terms of performance and support. This can help to manage expectations, ensure that the system is used effectively, and provide a clear understanding of the costs and benefits of the system. For instance, a category manager using the AsscherAi platform to compare suppliers can rely on the AI SLA to ensure that the results are accurate and reliable.

The AI SLA can also help to ensure that the AI system is integrated effectively with other systems and processes, and that it is aligned with the overall goals and objectives of the organisation. This can involve working closely with stakeholders across the organisation, including IT, business units, and external partners, to ensure that the AI system is meeting the needs of the organisation and providing the expected benefits. To learn more about how AsscherAi can support your organisation, visit our website at www.asscher.ai.

Limitations of an AI SLA

While an AI SLA can provide a clear understanding of what the AI system is capable of, and what the user can expect in terms of performance and support, it is not a guarantee of success. There are many factors that can affect the performance of an AI system, including the quality of the data, the complexity of the models, and the level of user expertise. Additionally, AI systems can be sensitive to changes in the underlying data or environment, which can affect their performance and accuracy.

Most people get wrong about AI SLAs that they think it is a magic solution that can solve all the problems related to AI systems, but they are wrong. AI SLAs are just a tool to manage expectations and ensure that the system is used effectively, but it is not a substitute for careful planning, testing, and evaluation of the AI system.

When an AI SLA is not the right choice

In some cases, an AI SLA may not be the right choice for an organisation. For example, if the organisation is just starting to explore the use of AI, or if the AI system is still in the experimental phase, an AI SLA may not be necessary. In these cases, the organisation may want to focus on developing a proof of concept, or testing the feasibility of the AI system, rather than investing in a formal AI SLA.

Alternatively, if the organisation has a simple AI system, such as a chatbot, an AI SLA may not be necessary. In these cases, the organisation may want to focus on developing a clear understanding of the system's capabilities and limitations, and ensuring that it is aligned with the overall goals and objectives of the organisation. If you have any questions about AI SLAs or how AsscherAi can support your organisation, please contact us to learn more.