• Written by Sonali Subhadarsini, Product Manager

A shift supervisor at a power plant checks the maintenance log to plan an outage, a task that requires careful consideration of regulatory requirements, asset availability, and personnel resources, all while ensuring the continuity of electricity supply to households and businesses, and it is in this complex environment that the use of enterprise AI governance in energy and utilities must be carefully managed to ensure that controls are in place to mitigate risks and ensure compliance with regulatory requirements.

Unique challenges in energy and utilities

The energy and utilities sector presents a distinct set of challenges that can make the implementation of enterprise AI governance more complicated, including the need for outage planning, which involves coordinating with multiple stakeholders to ensure that maintenance activities do not disrupt the supply of electricity, as well as the regulatory reporting burden, which requires the submission of detailed reports to regulatory bodies, and the management of geographically dispersed assets, which can make it difficult to ensure that all assets are properly maintained and operated.

For instance, a category manager at an energy company may need to compare the maintenance schedules of different power plants, taking into account factors such as the age and condition of the assets, the availability of personnel and equipment, and the regulatory requirements that must be met, all while ensuring that the maintenance activities do not disrupt the supply of electricity to households and businesses, and this requires the use of sophisticated systems and processes to manage and analyze large amounts of data.

Enforcing permission at retrieval

One of the key challenges in implementing enterprise AI governance in energy and utilities is ensuring that permissions are enforced at the point of data retrieval, rather than relying on instructions to the model, which can be complex and difficult to manage, particularly in an environment where multiple stakeholders are involved and where data is scattered across different systems and locations.

By enforcing permissions at retrieval, organizations can ensure that sensitive data is only accessed by authorized personnel, and that the use of AI systems is properly controlled and monitored, which is particularly important in the energy and utilities sector, where the consequences of a security breach or data loss could be severe, and where regulatory requirements are stringent, as can be seen by visiting our website for more information.

Auditability

Auditability is a critical component of enterprise AI governance in energy and utilities, as it enables organizations to track and monitor the use of AI systems, including what questions were asked, what data was used, and what results were returned, which is essential for ensuring that AI systems are being used in a responsible and transparent manner.

For example, a shift supervisor may use an AI system to analyze data on equipment performance, and the audit trail would show what data was used, what questions were asked, and what results were returned, which would enable the organization to track and monitor the use of the AI system, and to ensure that it is being used in accordance with regulatory requirements and internal policies, and for further assistance, please visit our contact us page.

Routing to a human

There are certain categories of questions or requests that should be routed to a human by design, rather than being handled by an AI system, such as requests that involve sensitive or confidential information, or requests that require a high level of judgment or expertise, and these categories should be carefully defined and implemented as part of the enterprise AI governance framework.

For instance, a request to access sensitive information about a customer's energy usage should be routed to a human customer service representative, rather than being handled by an AI system, to ensure that the customer's privacy is protected and that the request is handled in accordance with regulatory requirements, and this requires careful consideration of the types of requests that should be routed to a human, and the development of clear policies and procedures for handling these requests.

Limitations and exceptions

While enterprise AI governance is a critical component of the energy and utilities sector, there are certain limitations and exceptions that should be considered, such as situations where the use of AI systems is not feasible or practical, or where the benefits of using AI systems do not outweigh the costs and risks, and in these cases, alternative approaches may be necessary, such as the use of traditional data analysis techniques or the development of custom-built systems.

Ultimately, the implementation of enterprise AI governance in energy and utilities requires a careful consideration of the unique challenges and requirements of the sector, as well as the development of clear policies and procedures for managing and controlling the use of AI systems, and this requires a deep understanding of the sector and its requirements, as well as the ability to adapt and evolve in response to changing circumstances and regulatory requirements.