• Written by Azhar Aliyar, Director

When a category manager uses AsscherAi to compare two suppliers, they need to know what was asked, what data was retrieved, and what answer was given. This is what is meant by AI observability, which refers to the ability to understand the internal workings of an AI system, including the questions it receives, the data it accesses, and the responses it generates.

Definition of AI Observability

AI observability is not just a vague concept, but a specific set of capabilities that allow users to track and understand the interactions between the AI system and the organisation's data. This includes being able to see the exact questions that were asked, the data that was retrieved, and the answers that were generated. For example, a shift supervisor checking the maintenance log can use AI observability to understand why a particular answer was given, and what data was used to generate that answer.

This level of transparency is essential for building trust in the AI system, and for ensuring that the answers provided are accurate and reliable. By providing a clear understanding of how the AI system works, AI observability helps to reduce errors and improve overall performance.

Difference from Related Concepts

AI observability is often confused with data transparency, but the two are not the same. Data transparency refers to the ability to see the data that is being used by the AI system, whereas AI observability refers to the ability to see the internal workings of the AI system itself. While data transparency is important, it is not enough on its own to provide a complete understanding of how the AI system is working.

For instance, a data analyst may be able to see the data that is being used to generate a particular answer, but without AI observability, they may not be able to understand why that answer was given, or what other factors may have influenced the result. AI observability provides a more complete picture of how the AI system is working, and helps to identify potential issues or biases.

Applications in a Large Organisation

In a large organisation, AI observability can be applied in a variety of ways. For example, it can be used to monitor the performance of the AI system, and to identify areas where improvement is needed. It can also be used to provide training and support for users, by giving them a clear understanding of how the AI system works and how to use it effectively.

One of the key benefits of AI observability is that it allows organisations to build trust in their AI systems. By providing a clear understanding of how the AI system works, AI observability helps to reduce errors and improve overall performance. This is particularly important in large organisations, where the consequences of errors or biases can be significant. For more information on how AsscherAi can help your organisation, visit our website.

Limitations of AI Observability

While AI observability is a powerful tool, it is not a solution to all problems. It does not, for example, provide a guarantee of accuracy or reliability. Nor does it provide a complete understanding of the underlying data or algorithms used by the AI system. These are complex issues that require careful consideration and expertise.

Furthermore, AI observability is not a substitute for human judgment and oversight. While it can provide valuable insights and information, it is still important for users to critically evaluate the answers provided by the AI system, and to use their own expertise and experience to inform their decisions. If you have any questions about how AI observability can be used in your organisation, please contact us for more information.

When AI Observability is Not the Answer

There are some situations where AI observability is not the right choice. For example, in situations where the AI system is being used for simple, routine tasks, AI observability may not be necessary. In these cases, the benefits of AI observability may not outweigh the costs and complexity of implementation.

In other cases, the limitations of AI observability may make it less useful. For instance, if the AI system is using complex algorithms or proprietary data, AI observability may not be able to provide a complete understanding of how the system is working. In these cases, other approaches may be needed to provide the necessary transparency and accountability.