The energy jobs that AI will create

This article is part of our Opinions section.

The talk of the town is that AI will be taking all of our jobs – thanks, OpenAI. Who can blame people when we’ve got reports of an AI “apocalypse” potentially taking away almost 8 million jobs in the UK?

Frankly, this mindset is a little uncalled for. Across sectors, it’s far more likely that AI will create just as many jobs as it will take. And we can prove that with a thought experiment!

Take the energy sector. While some jobs will be lost, AI will fundamentally transform that sector, down to its ability to anticipate power demand, divert and save resources, ensure continuity in service, and reduce strain on the national grid. 

Let’s take a deeper look at the energy sector, and what the anticipated outcome of AI will be.  

Jobs are changing, not disappearing

Firstly, it’s important to emphasise that it’s very unlikely that AI will completely replace all workers of an occupation. But even if that’s a possibility, we’re nowhere near a future scenario where operations will have zero human oversight. 

Critical oversight isn’t something that can be fully offloaded to AI. It’s just not compatible with the black box decision-making process of current (and quite possibly) future AI models, and there’s too much at stake anyway to risk delegating that responsibility to AI.

Having said that, AI will be a big deal for the maintenance of critical infrastructure in heavily regulated sectors such as energy. It’s just that we’re more likely to see a smaller number of people working in these areas, with their work supported by AI. 

Consider pipeline and grid inspection as an example: detecting and identifying errors or technical problems can be offloaded to AI, but we’ll still need people on the ground to verify findings and act on them. Not all problems will be software-related. Some will inevitably crop up that require immediate human intervention when dealing with volatile energy resources. 

More broadly, AI will probably help a great deal to reduce human error and contribute to the stability and consistency of grid infrastructure. The challenge is making sure that we find a happy medium between human and AI collaboration as the latter saturates the job market, 

Top use cases of AI in energy

It’s not too hard to imagine new jobs that will need doing in the energy space that cater to the incorporation of AI, but it might first be useful to briefly overview how AI is likely to be used in the sector to begin with. Here are a few examples. 

Predictive demand analysis will be monumental across sectors, but especially within the energy sector. The ability to know when more power is potentially needed, and to be able to save or divert resources accordingly, will be crucial to energy service continuity and reduced strain on the grid. The algorithms that we’re using at the moment will gain additional predictive capabilities, and be able to react to changes quicker.

Hand in hand with sustainability, predictive analysis with AI will be also widely used by forestry and sustainable agriculture companies to assess and report on large areas through satellite imagery through image recognition. Large energy companies have already been making use of smart technologies such as IoT devices, and advancements with AI can be optimised further to enhance operational efficiency, reduce costs and minimise environmental impact. 

We’re also seeing energy-focused AI play a growing role in the buildings we spend time in. More and more buildings are constructed with smart home technologies integrated from the get-go, with end users making use of AI tools to regulate and optimise power and energy consumption based on personal preferences. Predictive analysis and automated distribution and regulation are also supporting electric vehicle companies to increase grid stability, maximising the number of cars able to make use of charging stations

Many energy providers already support smart meters to understand the demand and monitor online use. The introduction of AI will see readily available data connected with data acquired from internet activity, local specifics and information from smart appliances, which will in time provide active feedback for end users. Whether this is advising on behaviour patterns or cost savings, energy providers will be able to improve and tailor customer experience, without having to lift a finger. 

New jobs on the horizon

So, we know AI will have a bunch of use cases in the energy sector. But someone needs to monitor those functions. That is to say, it’s fairly reasonable to expect that we’ll see the creation of jobs entirely charged with the responsibility of overseeing AI operations. 

What kind of jobs are we talking about here? Well, I’m so glad you asked. Here are a few:

  • AI processing technician, who can process large volumes of incoming user and product data collected by AI tools in first-response/crisis management centres.
  • Energy system integration specialists who will integrate AI technologies with existing energy systems. They could ensure that AI solutions work seamlessly with traditional energy systems.
  • Generative AI architects and developers for the energy sector (this role could be applied to other sectors as well, not just energy).
  • AI researchers dedicated to discovering different uses of AI within the energy sector

More generally, GenAI architects and developers will innovate AI solutions tailored for the energy sector, but the biggest takeaway here is that this isn’t just about the energy sector. You could undoubtedly run similar thought experiments in other industries, too. Just as with energy, it’s reasonable to assume we’ll see dedicated AI research teams born out of a need to explore what new applications of AI may look like, in the interest of driving future advancements and efficiencies.

Data will need to be king, though. After all, data is the bread and butter of AI, generated on the producer, distributor and consumer levels. There’s a ton of it, hence why data scientists are at the cross-section of this field and in a privileged role to guide this industry into the AI rainbow.

So, no, AI will not take all of our jobs – least of all in the energy sector. What the “AI apocalypse” will do, though, is transform the future of work, leading some jobs to evolve and creating new ones altogether. Rather than worrying about the state of the job market in two, five or ten years’ time, we should focus our efforts on ensuring that we are prepared (and more importantly, upskilled) for the world of work down the line. 

Phil Thomas
Phil Thomas

Phil Thomas is the VP of Engineering at Zartis. An experienced architect with over 10 years of experience and a strong foundation in full-stack development, Phil is specialised in leading high-performing teams and delivering impactful business solutions.