• Written by Azhar Aliyar, Director

When a maintenance crew shuts down a power plant to perform routine repairs, the shift supervisor checks the maintenance log to ensure all necessary work is completed before bringing the plant back online. However, if the maintenance log is not properly integrated with the outage planning system, the crew may inadvertently cause a longer-than-expected outage, leading to regulatory issues and potential fines. This type of integration failure is common in the energy and utilities sector, where asset-heavy operations and regulatory scrutiny make connecting to systems of record a complex task.

Challenges unique to energy and utilities

The energy and utilities sector presents unique challenges when it comes to integrating AI systems with existing infrastructure. Outage planning, for example, requires careful coordination between multiple systems to ensure that maintenance is performed during periods of low demand, minimizing the impact on customers. The regulatory reporting burden also adds complexity, as companies must provide detailed reports on their operations, making it essential to have accurate and up-to-date data. Additionally, geographically dispersed assets, such as power transmission lines and substations, require specialized systems to manage and maintain, further complicating integration efforts.

These challenges are exacerbated by the fact that energy and utilities companies often have legacy systems that are difficult to integrate with newer technologies. As a result, companies must carefully plan and execute their integration strategies to avoid disrupting critical operations.

Read paths, write paths, and the need for careful planning

When integrating AI systems with existing infrastructure, it is essential to consider both read paths and write paths. Read paths refer to the flow of data from the system of record to the AI system, while write paths refer to the flow of data from the AI system back to the system of record. While read paths are relatively straightforward, write paths require more care, as they can potentially introduce errors or inconsistencies into the system of record.

A category manager, for example, may use an AI system to analyze data from the purchase order table and identify trends in supplier performance. However, if the AI system is not properly configured to write data back to the purchase order table, it may cause inconsistencies or errors, leading to problems downstream. To avoid such issues, companies must carefully plan and test their write paths to ensure data integrity and accuracy.

Reconciling differing definitions between systems

One of the significant challenges in integrating AI systems with existing infrastructure is reconciling differing definitions between systems. For instance, the definition of a "customer" may vary between the customer relationship management system and the billing system. To overcome this challenge, companies must establish a common data model that defines key terms and concepts in a consistent manner.

This requires careful analysis and mapping of data fields between systems, as well as ongoing monitoring and maintenance to ensure that definitions remain consistent over time. By establishing a common data model, companies can ensure that their AI systems are working with accurate and consistent data, enabling them to make better decisions and drive business outcomes.

Latency budgets and precomputation

In the energy and utilities sector, latency can have significant consequences, particularly in real-time systems such as those used for grid management. To mitigate latency, companies can establish latency budgets, which define the maximum acceptable latency for a given system or process. By prioritizing processes and allocating latency budgets accordingly, companies can ensure that critical systems remain responsive and effective.

In some cases, precomputation can also help reduce latency. For example, an AI system can precompute complex analytics on historical data, reducing the processing time required for real-time queries. By leveraging precomputation and latency budgets, companies can optimize their AI systems for performance and responsiveness, enabling them to make better decisions and drive business outcomes. For more information on how to implement latency budgets and precomputation, visit our resource center.

Limitations and when not to use AI systems integration

While AI systems integration can bring significant benefits to energy and utilities companies, it is not a solution for every problem. In some cases, the costs and complexities of integration may outweigh the potential benefits, particularly for small or simple systems. Additionally, AI systems integration may not be suitable for systems that require extremely low latency or high levels of customization.

In such cases, companies may need to consider alternative solutions, such as custom development or manual data integration. To determine whether AI systems integration is right for your organization, contact our experts at AsscherAi to discuss your specific needs and challenges.