• Written by Muhammed Abdulla NC, CEO

One common objection to the use of AI in energy and utilities is that it will replace human workers, but this concern is misplaced, as the actual impact of AI is on the day-to-day work in offices, not on the operational side, where physical work is handled, and according to the US Census Bureau's Business Trends and Outlook Survey, 13.7% of US utilities reported using AI in the previous two weeks, which is a significant figure, but it only applies to the US and does not reflect the global situation.

Where AI has actually landed in energy and utilities so far

AI has largely been applied to functions whose output is text, such as customer service, billing, and reporting, rather than to those that handle physical work, like maintenance and operations, and this is because text-based functions are more easily automated, and the benefits of AI are more readily apparent, as seen in the 14.3% of US businesses that used AI in sales and marketing in the previous six months, according to the US Census Bureau's Business Trends and Outlook Survey.

This is not to say that AI has no potential in operational areas, but rather that its impact has been more limited so far, and it will likely take more time and development to see significant changes in these areas, and as the US Census Bureau's Business Trends and Outlook Survey notes, 22.4% of US businesses reported using AI in the two weeks to July 2026, but this figure includes all industries, not just energy and utilities.

Outage planning

When the people dealing with outage planning can ask the data in SCADA, the outage management system, asset management, and billing a direct question, instead of waiting for a report, it can significantly change the way they work, and enable them to make more informed decisions, and respond more quickly to outages, and this is an area where AI can have a real impact, as it allows for more efficient and effective use of data, and as discussed in our previous article, this can lead to improved customer satisfaction and reduced downtime.

For example, a shift supervisor can check the maintenance log and ask the system when the last inspection was done, and what the results were, and use this information to plan the outage, and ensure that all necessary work is done, and this can help to reduce the risk of errors, and improve the overall efficiency of the process, and this is something that can be achieved through the use of AI-powered tools, such as those provided by our operations team.

Regulatory reporting burden

The decisions that currently wait on a person stitching together two systems, and what an answer in the moment does to them, are significant, as regulatory reporting is a major burden for energy and utilities companies, and AI can help to alleviate this burden, by providing answers to questions in real-time, and enabling companies to respond more quickly to regulatory requests, and this can help to reduce the risk of non-compliance, and improve the overall efficiency of the reporting process.

For instance, a category manager can compare two suppliers and ask the system which one has the better track record, and use this information to make a decision, and this can help to improve the overall quality of the reporting, and reduce the risk of errors, and as noted in the US Census Bureau's Business Trends and Outlook Survey, 1.7% of US businesses used AI in production of goods in the previous six months, which includes the energy and utilities sector.

Geographically dispersed assets

Geographically dispersed assets are a challenge for energy and utilities companies, and AI can help to address this challenge, but it is not a simple solution, and requires careful planning and implementation, as the assets are often in remote locations, and the data may be incomplete or inconsistent, and this can make it difficult to get accurate answers, and as discussed in our contact us page, we can help companies to navigate these challenges, and implement AI solutions that meet their needs.

This is because the data from these assets may not be easily accessible, or may be in a format that is not easily usable, and this can make it difficult to get the answers that are needed, and as a result, the benefits of AI may be limited, and it may be necessary to invest in additional infrastructure, or to develop new data management strategies, and this can be a significant challenge, and one that requires careful consideration, and according to the US Census Bureau's Business Trends and Outlook Survey, 0.9% of US businesses used AI in distribution in the previous six months.

Why most adopters have not changed anything underneath

Most adopters of AI in energy and utilities have not changed anything underneath, as noted by the US Census Bureau's Business Trends and Outlook Survey, which found that 64.3% of US businesses that used AI made no change of any kind to adopt it, and only 6.8% changed how they collect or manage data, and this is a significant limitation, as it means that the benefits of AI may be limited, and it may not be possible to achieve the full potential of the technology.

This is because the underlying systems and processes may not be well-suited to the use of AI, and may require significant changes, or investment in new infrastructure, and this can be a major challenge, and one that requires careful consideration, and as a result, the benefits of AI may be limited, and it may be necessary to invest in additional training, or to develop new strategies for implementation, and this can be a significant challenge, and one that requires careful planning, and according to the US Census Bureau's Business Trends and Outlook Survey, this is a common issue across US businesses.

What a sensible first quarter looks like for a energy and utilities business

A sensible first quarter for an energy and utilities business that is implementing AI looks like one question, one system, and one team, as this allows for a focused and targeted approach, and enables the company to test the technology, and develop a deeper understanding of its potential, and this can help to reduce the risk of errors, and improve the overall efficiency of the implementation process, and as discussed in our operations section, this can be a key factor in achieving success with AI.

For example, a company might start by implementing AI in a single area, such as customer service, and use it to answer a specific question, such as "what is the status of my outage?", and this can help to provide a clear and focused approach, and enable the company to develop a deeper understanding of the technology, and its potential, and this can be a key factor in achieving success with AI, and as noted in the US Census Bureau's Business Trends and Outlook Survey, this is a common approach across US businesses.

What this does not do, or when it is the wrong choice

AI is not a solution for every problem, and there are times when it is the wrong choice, such as when the data is incomplete, or inconsistent, or when the question is too complex, or requires a high level of human judgment, and in these cases, it may be better to use other approaches, such as manual analysis, or expert judgment, and this can help to ensure that the company is using the right tool for the job, and achieving the best possible results, and as discussed in our previous article, this can be a key factor in achieving success with AI.

For instance, a company might find that AI is not well-suited to a particular task, such as predicting the outcome of a complex event, or providing a high level of human judgment, and in these cases, it may be better to use other approaches, such as expert judgment, or manual analysis, and this can help to ensure that the company is using the right tool for the job, and achieving the best possible results, and according to the US Census Bureau's Business Trends and Outlook Survey, this is a common issue across US businesses, and one that requires careful consideration.