Media

Media companies have already adopted in many cases machine learning for subscription support and ad management. Also so editors, content programmers and writers know which stories work, which to write and which to highlight.

This is why news companies hire data scientists at more than decent salaries to collect data for customer and readership tracking. This data can then be used to guide them to specific products, as well as provide workers with tools to make it easier for them to find and write stories.

In addition, these companies also employ data analysts to create targeted content that generates more subscriptions and advertising revenue. In short, AI makes it easier for these organizations to deliver the most relevant content to each person.

Energy

Artificial Intelligence can be applied to almost every aspect of the energy sector. From the prediction and identification of faults in production plants to the use of weather forecasts for planning the construction of wind energy projects.

All indications are that energy companies are also going to increase the use of artificial intelligence to reduce the waiting time for their customers to call their customer service. They are already using chatbots to answer basic questions before handing them off to a person who can solve them if they are more complex.

In the not-too-distant future, energy providers are realizing that artificial intelligence can play a significant role in so-called smart grids, allowing supply and demand to be more closely matched. This will be possible thanks to a new generation of smart devices, from meters and electric vehicles to solar panels and heaters capable of improving their energy efficiency. Because of the possibilities it offers, yes, the jobs of supply analysts, surveyors and engineers may be at risk.

Production

Industry veterans know all too well how automation can turn the manufacturing industry upside down. And yes, also eliminate many jobs. But also generate more. Part of what automation brings is greater efficiency.. In addition, machine learning algorithms are already being used with the huge piles of data produced in large factories to advance predictive maintenance tasks and find signals to identify failures before they arrive. This also means that fewer technicians are needed.

On the other hand, generative artificial intelligence is used to design products faster, test them virtually through digital twins, and also to manufacture faster. This, combined with other technologies such as 3D printing, can significantly reduce development costs. In addition, fewer engineers would be needed in fields such as consumer electronics, automotive, and aerospace.

Government

Public administration can also make a lot of use of AI. Managing a city, region or country involves collecting huge amounts of data, both personal and business. All of these can be introduced into artificial intelligence and machine learning systems improve the effectiveness of the implementation of policies and lawsand also offer services.

Everything from waste collection to call centers can be managed more efficiently thanks to AI, improving investment and service priorities thanks to data analysis. Of course, this will not be without controversy, as again the use of AI in public administration would also lead to a lower need for staff.

In addition, there are concerns among authorities about the dangers AI systems can pose in terms of bias and perpetuating stereotypes and discrimination. Moreover, reliance on these systems in the past has sown uncertainty about whether some public priorities will be displaced in favor of others. So although the use of artificial intelligence can improve efficiency in various areas, authorities will need to carefully test and review its effects.

Transport

Workers in the transport sector will suffer greatly from the entry of artificial intelligence into their sector. In the long run, it is most vulnerable to AI job losses. This is indicated by the 2021 PwC report, which already predicted this biggest job losses in the next 20 years will be given in the transport sector.

Despite this, drivers are far from disappearing, although the first autonomous buses, trains and even taxis are already being tested. However, the arrival of fully robotic and autonomous taxis is still far from reality, and unmanned aircraft are still only a distant possibility. Meanwhile, some public transport services are already using artificial intelligence to help manage traffic flow and predict traffic problems.

Financial services

The financial services sector is also exposed to significant job losses due to the impact of artificial intelligence. But it doesn’t have to be a traumatic situation because AI can help fill the gap what is currently between job offers and professionals who can fill them.

Banks and fund managers will therefore need fewer staff to serve new clients as they automate tasks such as checking their records. They will also rely more on AI to detect and flag potential fraud and actions that pose a money laundering risk.

In addition, they will be able to feed new regulations from regulators into machine learning programs to be able to identify potential gaps or flaws in a company’s systems, rather than relying on humans to identify them and perform the first review. . Of course, these systems will still need human supervision. Not only to develop and program the necessary technology, but also to conduct additional tests and solve the most difficult problems.

It will also be necessary for entities to have highly qualified personnel to carry out forensic tasks if they suspect that an error or fraud has occurred. Or provide personalized customer support.

Retail

It is estimated that almost a third of retail jobs could disappear by 2030 compared to 2017 levels. warehouse automation, checkout automation and planning tools based on artificial intelligence.

For customers, the most notable change already today is the growth of self-checkout and self-scanning systems in supermarkets and other large stores over the past five years, accelerated during the pandemic. The number of ATMs could be halved by 2030 as self-service tellers are introduced.

The next step is to open cashier-less stores, such as those launched by Amazon. So in Amazon Go, the system identifies the buyer and what he buys through cameras and sensors on the shelves. Purchases made by customers are automatically identified and logged and recorded in the app on their smartphone. They can thus leave the store and pay even after leaving the store.

However, AI-related technology is not the only thing at the point of payment. Retailers are experimenting with robotic or artificial intelligence systems to identify gaps in the shelves. Others already use detection devices that move around stores and various shelf areas. There are already electronic labels on the shelves, which allow for price changes automatically from the headquarters and have helped streamline tasks in the stores. And already in offices, AI-driven technology to have forecasting and purchase intent information, and more robotics to move and place products in warehouses.