• Written by Azhar Aliyar, Director

A procurement manager at a large manufacturing company must decide this week which supplier to use for a critical component, and they currently base their decision on a manual review of the purchase order table and the supplier performance log. AI evaluation is the process of testing whether the answers provided by a system like AsscherAi are correct, by verifying them against the actual data in systems like the purchase order table and the supplier performance log.

Definition of AI Evaluation

AI evaluation is a critical step in ensuring that a system like AsscherAi provides accurate and reliable answers to questions about an organisation's data. It involves checking the answers provided by the system against the actual data in the relevant systems, such as the purchase order table or the maintenance log. This process helps to identify any errors or inconsistencies in the system's responses, and ensures that the organisation can trust the information provided by the system.

The evaluation process typically involves a detailed review of the system's responses to a set of test questions, and a comparison of these responses to the actual data in the relevant systems. This review may be performed by a team of evaluators, who use their knowledge of the organisation's data and systems to verify the accuracy of the system's responses.

Difference from Related Concepts

AI evaluation is often confused with the concept of natural language processing (NLP) evaluation, which focuses on the fluency and coherence of a system's responses, rather than their accuracy. While NLP evaluation is an important aspect of developing a system like AsscherAi, it is not a substitute for AI evaluation. A system may provide fluent and coherent responses that are nonetheless incorrect, and it is the role of AI evaluation to identify such errors.

For example, a shift supervisor may use AsscherAi to ask about the status of a particular machine, and receive a response that is grammatically correct and easy to understand, but nonetheless incorrect. AI evaluation would involve checking this response against the actual data in the maintenance log, to ensure that it is accurate and reliable.

Applications in a Large Organisation

AI evaluation has a number of important applications in a large organisation, particularly in areas where data-driven decision making is critical. For example, a category manager may use AsscherAi to compare the performance of different suppliers, and rely on the system's responses to make informed decisions about which suppliers to use. In this context, AI evaluation is essential for ensuring that the system's responses are accurate and reliable, and that the organisation can trust the information provided by the system.

AI evaluation may also be used to evaluate the performance of different systems and processes within the organisation, and to identify areas for improvement. For example, a procurement manager may use AsscherAi to analyse the purchase order table and identify trends and patterns in the organisation's purchasing behaviour. AI evaluation would involve checking the system's responses against the actual data in the purchase order table, to ensure that they are accurate and reliable.

Limitations of AI Evaluation

While AI evaluation is a critical step in ensuring the accuracy and reliability of a system like AsscherAi, it is not a panacea for all the organisation's data-related problems. AI evaluation does not replace the need for careful data management and governance, and it is not a substitute for human judgement and expertise. For example, a system like AsscherAi may provide accurate and reliable responses to questions about the organisation's data, but it is still up to the organisation's staff to interpret and act on this information.

Organisations can learn more about how to implement AI evaluation effectively by visiting our website at www.asscherai.com, or by contacting us directly at www.asscherai.com/contact-us. Our team of experts can provide guidance and support to help organisations get the most out of their AI evaluation efforts.

When AI Evaluation is Not the Right Choice

There are some situations in which AI evaluation may not be the right choice, or in which it may need to be supplemented with other approaches. For example, in situations where the organisation's data is highly complex or nuanced, AI evaluation may not be sufficient to ensure the accuracy and reliability of the system's responses. In these cases, the organisation may need to use a combination of AI evaluation and human expertise to ensure that the system's responses are accurate and reliable.

In other cases, the organisation may need to use alternative approaches, such as manual review or auditing, to ensure the accuracy and reliability of the system's responses. For example, in situations where the organisation's data is subject to strict regulatory or compliance requirements, the organisation may need to use manual review or auditing to ensure that the system's responses meet these requirements.