Artificial intelligence keeps gaining relevance, perhaps now even faster than before when we’ve come to realize that human workforce can’t be always there. The pandemic has brought an increase in companies’ interest in AI technologies, making them adjust or accelerate their adoption strategies (read more in our report “The impact of Covid-19 on IT investments”). 

The energy sector is not falling behind, and there is a plethora of applications of AI aiming to optimize processes, predict issues (or opportunities), and, in general, save money. 

Applications of AI in the energy industry

Business operations

That’s where AI is a true rockstar: in improving business operations. With predictive analytics, businesses can gain more insight into their customers and predict their lifetime value, the risk of churn. With more information about individual customers, salespeople can do their jobs better: they know who they need to reach out to and why they’re doing that in the first place. Such a simple (well, relatively) solution can prevent customers from leaving, boost sales, and translate into more revenue. An additional perk is that customers served better are usually happier customers. If you know more about them, you can address their needs, and increase customer satisfaction. And that always matters, whatever the industry.

CTA churn reduction case study

This is especially important given that the utility industry is rather competitive and pricing plays a significant role in establishing a relationship with customers. The quality of service is also key – it is not a surprise to anyone that an unhappy customer can make a big mess, and it’s not just the loss of revenue from this individual – make them really unhappy, and they’ll go online to complain about your awful services, they’ll share their horror story with all friends who are willing to listen. Yes, I am being quite dramatic now, but quality customer service is not to be underestimated: data from American Express shows that 78% of customers have bailed on an online transaction because of a poor experience, while Gartner found that 48% of customers who had a negative experience told 10 or more others. So make one angry, lose a sale, but also valuable referrals, online reputation, and time trying to make it right when it’s already too late.

On the other hand, research done by Frederick Reichheld of Bain & Company (the inventor of the net promoter score) shows increasing customer retention rates by 5% increases profits by 25% to 95%. You do the math and see if it’s worth it.

Resource management

If you apply AI in resource management, you can cut costs for both the business and the end customer. How? Thanks to predictive mechanisms, energy providers can get accurate forecasts that allow for improved energy scheduling and dispatch of power plants. When they have relevant information in advance, companies can better prepare for demand, predict issues, increase grid stability, and save resources. Proper resource management allows for increasing efficiency and lowering expenses. Optimized resource management will also mean lower utility costs for customers.

Resource management

Predictive maintenance and failure prevention

Predictive maintenance is key in the energy industry as the resource has to be handled carefully, otherwise, it may be dangerous. Faulty equipment or lines pose a huge danger, and not every breakdown can be easily predicted by humans. Artificial intelligence can be used to identify defects such as cracks, corrosion, bad insulation, and prevent failures. 

Additionally, predictive maintenance allows companies to maximize the utilization of equipment or machine components – all that while maintaining all safety standards. In the past, factories were forced to decide whether they chose to try and maximize the utilization while risking machine downtime, or replace potentially good parts early to avoid issues. 

Reducing human error

This point may seem rather vague, but is important, and it will be present in many applications of AI. Artificial intelligence learns in a way that’s inspired by human learning, but is still different. AI will learn from instances of historical data, and get better when trained on large amounts of quality data. A large number of plant power outages or personal injuries result from human error rather than faulty machinery. With the help of AI, many of these could be prevented – take, for example, predictive maintenance (see above) that shows when equipment will need fixes – so no one can overlook issues, forget about maintenance, or, worst-case scenario, get hurt. 

It’s important to remember that errors are just normal – and AI isn’t going to be 100% error-free either, but the margin of error will usually be smaller. People make mistakes because of workload, stress, working conditions, private issues. It just happens. And if they make a mistake, are they to be blamed? This is an additional benefit of allowing AI to take the wheel in some areas: it takes the burden off of people. Naturally, leaving any decision-making to AI raises questions of who’s to blame in case of serious error, but that’s a topic for a whole new discussion.

AI for utilities – what’s next?

Artificial intelligence in energy and utilities can be extremely useful for companies to increase safety and efficiency, cut costs, and increase revenue. All that while taking arduous tasks and unwanted responsibilities away from employees. It seems like AI and smart factories are the future and nothing can change that, but deciding on the best strategy to adopt AI or expand adoption and the best steps to take is tricky. Remember that any technology you adopt must make sense for your business – so you require it to bring tangible value. Before implementing a company-wide AI strategy, analyze your processes and the opportunities there are for the technology.

Predictive analytics for business
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