Fiction has often presented artificial intelligence as an existential threat to humanity, one dedicated to replacing and, often, eradicating humankind. Research indicates that a majority of U.S. citizens have anxiety over AI, ranging from privacy issues to the use of deep fakes and beyond. Couched among these concerns is the long-gestating fear that artificial intelligence will replace workers through automation, an anxiety with a long historical precedent.
Artificial intelligence and machine learning have empowered U.S. grid stewards to better forecast demand through an approach customizable by program and designed to calculate needs quickly and more efficiently. Through artificial intelligence, grid operators can control and coordinate distributed energy resources (DERs) like smart home devices, electric vehicles, or batteries to better meet demand.
So far, the expressions artificial intelligence and machine learning have been used interchangeably as shorthand that drives the question Will machines replace people in the workforce? In specific, however, artificial intelligence and machine learning are not the same: machine learning is a subset of artificial intelligence, just like every square is a rectangle.
Artificial intelligence, broadly, refers to technologies designed to mimic the cognitive functions associated with human intelligence. The phrase itself implies an active intelligence that can make choices independent of input, a simulacrum of a person like Data from Star Trek, the Terminator, or the software bots from The Matrix, which is far from accurate; for the machines we identify as capable of artificial intelligence, engineers have to program and maintenance them, monitoring their decisions to guide optimal outcomes. At the time of this writing, there is no artificial intelligence per the common connotation, although there are software designed to better guide human operators.
A subset of AI, machine learning is a process that allows machines to learn and improve from their experiences. Machine learning employs algorithms to analyze and assess large reams of data, much faster than any living operator could. Because machine learning is designed to improve, the more the process is employed, the better the potential outcomes.
Already, AI and machine learning have been used to offset replacing new and costly employees, which was particularly noticeable throughout the COVID pandemic. This would imply that there are now fewer jobs on the market, presenting fewer opportunities to prospective employees. Still, while the pandemic did shift national employment statistics and labor priorities in the U.S., job growth has returned to pre-pandemic levels. This demonstrates that not only did a pandemic fail to derail the workforce or economy in the long run, but that the jobs market is flexible. To accomplish the work needed during the energy transition, more workers are needed to power a distributed energy resource future.
As mentioned, concerns over losing workers to something like automation/AI have persisted for hundreds of years. The term Lump of Labour Fallacy was coined to address the misconception that there is a limited amount of available work. As such, consider automation — AI, machine learning, or otherwise — as an opportunity to free up resources to pursue other tasks. For example, if a utility has a small program management team to manage a distributed energy resources initiative, introducing automation provides an opportunity not to decrease the workforce, but to redistribute it. After all, AI was designed to imitate human problem-solving to help humanity, which is exactly what it’s been used for in the U.S. electric grid to great success already.
Artificial intelligence, machine learning, and automation are all valuable tools for utilities interested in streamlining their operations. These technologies are already at work in electric utilities around the world, leading to efficient and accurate load management, reductions in workplace risk, and more. These tools are already reshaping the utility sector, shifting workers from monotonous, menial tasks to analytics, systems monitoring, technology management, and more.
Utilities run a host of demand flexibility initiatives ideally suited to machine learning algorithms and artificial intelligence. Programs include, but are not limited to:
– Optimizing energy supply based on real-time and historical data
– Increasing customer engagement through automated communication
– Improving program management efficiency and scalability
– Enhancing device and system monitoring for accurate load management
In each case and more, there are opportunities to automate features, saving utility program managers the time and resources they need to better streamline energy needs in real time.
Experts believe that the AI singularity — a hypothetical, but entirely possible technological shift wherein AI becomes more intelligent than humans — may occur around 2045, meaning that the time of advanced robotic threats is still a thing of fiction. What isn’t fictional is the power that AI affords grid operators to better manage and shift load to meet demand. Like all change, these advancements are already having a net positive effect on operations management, and will create the kind of fresh opportunities needed to modernize the grid; in all likelihood, AI will create more jobs.
Artificial intelligence, machine learning, and automation are not replacing human workers outright, but rather augmenting their capabilities. As utilities embrace these technologies, they can optimize their operations, improve efficiency, and drive the transition to a distributed energy future. By leveraging AI and machine learning tools, utilities will not only better manage their resources but also unlock new opportunities for growth and innovation.
The fear of job loss due to automation is not a new concern, but history has shown that advancements in technology often lead to new job opportunities. As the workforce adapts to the evolving landscape, it is essential to recognize the potential of AI, machine learning, and automation in reshaping the utility sector while also acknowledging the importance of human ingenuity and problem-solving in harnessing the full potential of these technologies.
