• Written by Sonali Subhadarsini, Product Manager

While enterprise AI systems like AsscherAi are designed to provide insights and answer questions about an organisation's own data, the reality is that these systems often struggle to deliver in practice, particularly in complex and highly regulated industries like chemicals, where the shift supervisor and category manager have to navigate yield drift, feedstock variability, and plant-to-plant recipe differences, which can make it harder to maintain consistent and reliable processes, and where the consequences of error can be severe, making it essential to have controls in place that can hold up when the system is used by people who did not build it.

Chemicals industry challenges

The chemicals industry presents a unique set of challenges for enterprise AI systems, primarily due to the continuous nature of the processes involved, which require tight yield and safety envelopes, and where even small variations in feedstock or recipe can have significant effects on the final product, and the category manager has to be able to compare different suppliers and make informed decisions, while the shift supervisor has to be able to check the maintenance log and respond quickly to any issues that arise, all of which can be difficult to capture and model using traditional AI approaches.

For example, a category manager comparing two suppliers might need to consider factors like cost, quality, and reliability, as well as the potential impact on yield and safety, and the shift supervisor checking the maintenance log might need to be able to identify potential issues before they become major problems, all of which requires a deep understanding of the underlying processes and data, and where the enterprise AI system needs to be able to provide accurate and relevant information to support these decisions.

Enforcing permission at retrieval

One of the key challenges in implementing enterprise AI systems in the chemicals industry is ensuring that the right people have access to the right information, and that sensitive data is protected, which is why it is essential to enforce permission at retrieval, rather than relying on the model to determine who should have access to what data, as this can be difficult to control and may not always reflect the current permissions and access rights, and where the system needs to be able to integrate with existing security and authentication mechanisms to ensure that only authorised users can access sensitive data.

This approach also helps to ensure that the system is auditable, and that it is possible to track who has accessed what data, and when, which is essential for maintaining regulatory compliance and ensuring that the system is being used in a responsible and secure manner, and where the audit trail can be used to identify potential security issues and respond quickly to any incidents that may arise, and for more information on how AsscherAi can support auditability and security, please visit our website.

Auditability

Auditability is a critical aspect of enterprise AI systems in the chemicals industry, as it provides a clear record of what was asked, what was returned, and on what data, which is essential for maintaining regulatory compliance and ensuring that the system is being used in a responsible and secure manner, and where the audit trail can be used to identify potential security issues and respond quickly to any incidents that may arise, and the system needs to be able to provide a clear and transparent record of all interactions, including the questions asked, the data used, and the results returned.

This requires a deep understanding of the underlying data and processes, as well as the ability to track and record all interactions with the system, and where the system needs to be able to integrate with existing security and authentication mechanisms to ensure that only authorised users can access sensitive data, and for more information on how AsscherAi can support auditability and security, please visit our website, or contact us to discuss your specific needs and requirements.

Routing to a human

There are certain categories of questions that should always be routed to a human, rather than being answered by the enterprise AI system, such as those that require a deep understanding of the underlying processes and data, or those that have significant safety or regulatory implications, and where the system needs to be able to identify these types of questions and escalate them to a human operator, who can provide a more nuanced and informed response, and where the system needs to be able to integrate with existing workflows and processes to ensure that these questions are handled in a timely and effective manner.

For example, a question about the potential impact of a new feedstock on yield and safety might require a detailed analysis of the underlying data and processes, as well as a deep understanding of the regulatory requirements and safety protocols, and where the system needs to be able to identify this type of question and escalate it to a human operator, who can provide a more informed and nuanced response, and where the system needs to be able to provide clear and transparent information about the data and processes used to support the response.

Limitations and wrong choices

While enterprise AI systems like AsscherAi can provide significant benefits in terms of insights and decision support, they are not a replacement for human judgment and expertise, and there are certain situations where they may not be the best choice, such as those that require a deep understanding of the underlying processes and data, or those that have significant safety or regulatory implications, and where the system needs to be able to identify these types of situations and escalate them to a human operator, who can provide a more nuanced and informed response.

In these situations, it is essential to have a clear understanding of the limitations and potential biases of the enterprise AI system, as well as the potential risks and consequences of relying solely on the system for decision support, and where the system needs to be able to provide clear and transparent information about the data and processes used to support the response, and where the human operator needs to be able to review and validate the results to ensure that they are accurate and reliable, and for more information on how AsscherAi can support decision support and regulatory compliance, please visit our website.