COVID-19 has forced many organizations to temporarily close their offices and ask employees to work from home until the virus subsides and a vaccine is readily available. Millions of people are now working remotely, and many find it challenging – including those electric and utility company workers who thrive in productive and engaging workplaces.
Historically, power, energy and electric utility organizations have had most of their employees working on-site—in the corporate offices or plants—or out in the field. Now, the corporate hallways are vacant, and employees are sequestered in their homes, which resulted in executives asking: 1) Are my employees at home actually working? 2) Are they working productively? 3) What can we do to increase our productivity while working remotely?
Like senior leaders from other industries forced into the remote work maelstrom, utility executives can no longer pop into an employee’s office for an impromptu chat or walk through the cube farm to get a sense if people are working. While that was not an exact way to measure productivity, it did give utility executives some comfort knowing that their core teams were. Now, that it is impossible to get that level of visibility and transparency, it has made leaders wonder: how did we know they were productive before? Just being able to see employees was a scapegoat for the real question.
Recognizing that they cannot help when they cannot see, power, energy and utility executives are turning to AI to gain visibility and insights that are humanly impossible to do. The application of AI as an employee productivity monitoring tool is a breakthrough result of COVID-19. It is being used to accurately measure and improve productivity.
The two primary uses are helping remote workers become as (or more) productive as they were in the office, and engineers move beyond the limitations of average.
In much the same way that AI is being used to eliminate machine downtime given it was killing productivity, AI is being used to transform employee productivity. Most major utilities have started using AI on sensor data to transition from reactive to proactive maintenance. In the same way, AI is being applied to system data, which reflects employee work, to proactively identify how to optimize employees work and reduce wasted time. Recognizing the changing patterns given remote work, executives are using AI to give their employees these personal insights and to identify priority productivity impact areas.
This interest goes beyond those who were displaced from the office. They are turning to AI to get insights for engineers who are out in the field, whether for new installations or maintenance and repairs. Historically, field work has been opaque and relied on averages. Inherent in dependence on simple averages is their failure to provide real productivity gains as they’re susceptible to outliers. When the data contains a small number of extreme values, it can drag the average significantly towards those extremes. Averages treat underperformance and over-performance the same way. We know from behavioral psychology and other areas that the world isn’t so simple. When it comes to field work and figuring out if the worker is efficient: Using averages of previous instances and saying “he’s above average” and “he’s below” breaks down. Using AI, it is now possible to know what the optimal time *should* be and use the optimal time, not an average, to determine if a worker is efficient.
Stemming from the application of AI to aid with employee productivity is the provision of personal coaching. Once the AI quantifies how productive an employee is, it then identifies what to focus on to become more productive and makes recommendations on how to achieve it. For example, the mad rush at the end of the day to wrap up work leads to an increase in the volume of work and faster work times. On the surface this appears to be a positive, but at the same time the number of mistakes is disproportionately high, which results in reworks and other employees spending time to correct the mistakes. So, in reality, productivity decreases. The AI highlights this as an area of improvement and makes recommendations to space the work out, be thoughtful about potential errors and to double check the work.
Other operational areas, such as line maintenance, can be evaluated for ways to better utilize resources. By evaluating all available data and applying for an AI assessment, AI can turn mass amounts of data into usable information for management.
In addition, a large number of utility and power jobs are operational in nature – call centers, HR, finance, IT support, help desks, market monitoring and various reporting functions. Now that workers in those areas are remote, utilities need the ability to aggregate workflow data and evaluate ways to ensure consistency of output.
One large utility with more than 11,000 employees is using an AI-based productivity platform. The utility is using AI technology to eliminate tedious non-value-added work through AI analysis so that teams can focus on work that is more impactful, yielding more meaningful business results. The management team is also using AI to coach employees, helping them use their time more wisely, become more productive in less time and stay focused while working remotely.
This utility is also using AI to obtain a “productivity score” on each employee – measuring how much meaningful work that gets completed and uncovering ways to keep that employee motivated, engaged, and focused. Rather than feeling unfairly monitored, the utility employees report that they are happy to use AI as a personal career coach – an ally that offers pragmatic guidelines on how to get more done in a shorter period of time and focus on projects that support career advancement.
Power industry executives exploring AI implementations have reported that they are concerned about allocating capital for maintenance, investment, and staffing during this uncertain time. They are also worried about how their remote workforces could create increased avenues for cybersecurity attacks as many have decentralized system interfaces. Some are looking at ways to fix that issue by partitioning work in new ways. To do that, they want to use AI to identify work groups and work types based on their data and build an assessment tool to monitor them.
At the same time, increasing extremes in weather patterns are posing additional challenges, making reliability issues even more acute. Therefore, power executives are looking at AI to help achieve better outcomes for older resources through greater efficiency, thereby extending their ability to contribute in the future.
In short, AI enables utility organizations to overcome a number of short and long-term challenges – from employee productivity and cost efficiencies to boosting the performance of older systems and improving reliability. AI will likely emerge as a key “superpower” for the power companies that will define the future of the industry.