• Written by Sonali Subhadarsini, Product Manager

On a typical Monday morning, a shift supervisor at a power plant checks the maintenance log to plan the day's work, taking into account the ongoing outage of a nearby transmission line, which is still awaiting regulatory approval for its repair schedule. This complex interplay of asset management, regulatory compliance, and operational planning is a common challenge in the energy and utilities sector, where AI process automation can help streamline decision-making around these processes, as seen when using platforms like AsscherAi to query an organisation's own data.

Unique challenges in energy and utilities

The energy and utilities sector presents a unique set of challenges that can make process automation more difficult, including the need for careful outage planning to minimize disruptions to service, a heavy regulatory reporting burden that requires meticulous record-keeping, and geographically dispersed assets that can make it hard to coordinate maintenance and repairs. For instance, a category manager at a utility company may need to compare the maintenance schedules of different suppliers to ensure compliance with regulatory requirements.

These challenges can be particularly daunting in an industry where safety and reliability are paramount, and where the consequences of failure can be severe. As a result, energy and utilities companies often have to balance the need for efficiency and cost savings with the need for careful planning and risk management.

Mapping the current process

Before automating any part of the process, it's essential to map out the current workflow in detail, including all the steps involved in planning, executing, and reviewing maintenance and repairs. This can help identify bottlenecks and areas where automation can have the greatest impact. A worked example of this might involve a maintenance manager using AsscherAi to query the organisation's data on past outages and identify patterns that can inform future planning.

By taking a thorough and systematic approach to process mapping, energy and utilities companies can ensure that their automation efforts are targeted and effective, and that they address the specific challenges and pain points of their industry. This might involve working with a consultant or specialist to identify areas for improvement and develop a tailored automation strategy.

Handoffs and delays

One of the main sources of delay in energy and utilities operations is the handoff between different teams and departments, such as between maintenance and operations, or between planning and execution. These handoffs can be prone to errors and miscommunication, and can cause significant delays and inefficiencies. For example, a shift supervisor may need to wait for approval from a regulatory specialist before proceeding with a repair, which can hold up the entire process.

By automating the steps around these handoffs, energy and utilities companies can reduce the risk of errors and delays, and improve the overall efficiency and effectiveness of their operations. This might involve implementing automated workflows and approval processes, or using data analytics to identify and mitigate potential bottlenecks.

Exception handling

Exception handling is a critical aspect of process automation in energy and utilities, as it allows companies to respond quickly and effectively to unexpected events and disruptions. In many cases, exception handling can be the majority of the real work involved in automation, as it requires careful planning and coordination to ensure that the right people and systems are in place to respond to emergencies. A category manager, for instance, may need to develop contingency plans for supply chain disruptions or equipment failures.

By prioritizing exception handling and developing robust and flexible automation strategies, energy and utilities companies can minimize the impact of disruptions and ensure continuity of service. This might involve working with stakeholders to identify potential risks and develop mitigation strategies, or using data analytics to identify patterns and trends that can inform exception handling.

For companies looking to get started with AI process automation, it's a good idea to reach out to a specialist who can provide guidance and support throughout the process.

Limitations and wrong choices

While AI process automation can be a powerful tool for energy and utilities companies, it's not a silver bullet, and there are certain situations where it may not be the best choice. For example, automation may not be suitable for processes that require a high degree of human judgment or creativity, or where the rules and regulations are subject to frequent change. In these cases, energy and utilities companies may need to rely on other approaches, such as process re-engineering or lean management, to achieve their goals.

Additionally, automation may not be effective in situations where the underlying processes are poorly defined or inefficient, as it can simply automate existing inefficiencies and make them more entrenched. In these cases, energy and utilities companies may need to take a step back and re-examine their processes before attempting to automate them.

By understanding the limitations and potential pitfalls of AI process automation, energy and utilities companies can make informed decisions about where and how to apply this technology, and ensure that their automation efforts are targeted, effective, and aligned with their overall business goals.