• Written by Sonali Subhadarsini, Product Manager

A shift supervisor at a power plant spends a significant amount of time reviewing maintenance logs and scheduling outages, a task that is crucial to ensuring the reliability of the energy supply, but also takes away from more strategic activities, and this is a common challenge in the energy and utilities sector where asset-heavy operations and regulatory scrutiny can make it difficult to implement new technologies and processes, including those that utilise AI productivity in the enterprise.

Unique challenges in energy and utilities

The energy and utilities sector presents a unique set of challenges that can make it harder to implement AI productivity solutions, including the need for careful outage planning to minimise disruptions to the energy supply, a heavy regulatory reporting burden that requires significant time and resources, and geographically dispersed assets that can make it difficult to coordinate maintenance and other activities, all of which can limit the potential benefits of AI productivity solutions, and require careful consideration of the specific constraints and challenges of the sector.

For example, a category manager at a utility company may need to compare the performance of different suppliers of equipment and services, taking into account factors such as cost, reliability, and regulatory compliance, a task that can be time-consuming and require significant expertise, and may not be easily automated using AI productivity solutions, although tools such as AsscherAi can help to provide quick and easy access to relevant data and information.

The difference between time saved and time redeployed

One of the key challenges in implementing AI productivity solutions in the energy and utilities sector is understanding the difference between time saved and time redeployed, as while AI productivity solutions may be able to automate certain tasks and save time, this time may not necessarily be redeployed to more strategic activities, but rather may be absorbed by other tasks and responsibilities, and this can make it difficult to measure the impact of AI productivity solutions on productivity and efficiency.

A good example of this is a maintenance team that uses AI productivity solutions to automate routine maintenance tasks, but then finds that the time saved is absorbed by additional regulatory reporting requirements, or by the need to respond to customer inquiries and other operational issues, and this highlights the need to carefully consider how AI productivity solutions will be used and how the time saved will be redeployed, and to have a clear understanding of the specific challenges and constraints of the sector, and how these can be addressed using AI productivity solutions, and for more information on how AsscherAi can help with this, please visit our website at www.asscherai.com.

Why self-reported time savings overstate the effect

Self-reported time savings are often used to measure the impact of AI productivity solutions, but these can overstate the effect, as they are based on estimates and perceptions rather than actual data and measurement, and may not take into account the complexities and nuances of the energy and utilities sector, where tasks and responsibilities can be highly interdependent and subject to a range of external factors and constraints.

For example, a team may report that they have saved a significant amount of time using an AI productivity solution, but this may not be borne out by actual data and measurement, and may not take into account the time and resources required to implement and maintain the solution, or the potential impact on other tasks and responsibilities, and this highlights the need for careful and rigorous measurement and evaluation of AI productivity solutions, and for a clear understanding of the specific challenges and constraints of the sector, and how these can be addressed using AI productivity solutions.

The tasks worth removing first

When implementing AI productivity solutions in the energy and utilities sector, it is important to identify the tasks that are worth removing first, and to prioritise these based on their potential impact on productivity and efficiency, and their alignment with the overall goals and objectives of the organisation, and this requires a careful analysis of the specific challenges and constraints of the sector, and a clear understanding of how AI productivity solutions can be used to address these.

A good example of this is a task such as data entry or reporting, which can be time-consuming and prone to error, and which may be a good candidate for automation using AI productivity solutions, and by removing or automating these tasks, organisations can free up time and resources for more strategic activities, and can improve productivity and efficiency, and for more information on how AsscherAi can help with this, please contact us to discuss your specific needs and requirements.

When AI productivity solutions are not the right choice

While AI productivity solutions can be highly effective in improving productivity and efficiency in the energy and utilities sector, there are certain situations where they may not be the right choice, such as where tasks require a high degree of human judgement or expertise, or where there are significant regulatory or compliance requirements that must be met, and in these situations, other solutions or approaches may be more appropriate, and may be better suited to addressing the specific challenges and constraints of the sector.

For example, a task such as outage planning or maintenance scheduling may require a high degree of human judgement and expertise, and may not be well-suited to automation using AI productivity solutions, and in this case, other approaches such as process improvement or training and development may be more effective, and may be better suited to addressing the specific challenges and constraints of the sector, and this highlights the need for a careful and nuanced approach to the implementation of AI productivity solutions, and for a clear understanding of the specific challenges and constraints of the sector, and how these can be addressed using a range of different solutions and approaches.