Media
The companies dedicated to the media have already adopted, in many cases, the machine learning to drive subscriptions and manage advertising. Also so that editors, content programmers and writers can know which stories are the ones that work, which ones to write and which ones to highlight.
That’s why news companies are hiring data scientists, at more than respectable salaries, to collect data to track customers and readers. With this data, they can then be guided to specific products, as well as giving workers tools to make it easier for them to find and write stories.
Apart from this, these companies also employ data analysts to create targeted content that generates more subscriptions and ad revenue. In short, Artificial Intelligence makes it easier for these organizations to serve the most appropriate content for each person.
Energy
artificial intelligence can be applied to almost every aspect of the energy sector. From the prediction and identification of failures in production plants to the use of weather forecasts for planning the construction of wind energy projects.
Everything indicates that, in addition, energy companies are going to increase the use of AI to reduce the waiting time of their customers’ calls to their customer service. They already use chatbots to answer basic questions before passing them on to a person who can solve them if they are more complex.
In a future that may not be too distant, energy providers see that Artificial Intelligence can play a prominent role in what are known as smart grids, allowing supply and demand to be more in line. It 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. In view of the possibilities it offers, yes, the jobs of supply analysts, meter readers and engineers can be endangered.
Manufacture
Industry veterans know all too well how automation can turn the manufacturing industry upside down. And yes, also eliminate many jobs. But also generate others. Part of what automation brings is more efficiency.. In addition, machine learning algorithms are already being used with huge piles of data produced in large factories to advance predictive maintenance tasks, and find the signals that make it possible to identify failures before they arrive. This will also mean fewer technicians are needed.
On the other hand, Generative Artificial Intelligence is being used to design products faster, test them virtually through digital twins, and also to manufacture them faster. This, combined with other technologies, such as 3D printing, can significantly lower development costs. In addition, fewer engineers would be needed in areas such as consumer electronics, automotive and aerospace.
Government
Public administrations can also get a lot out of AI. Governing a city, a region or a country involves collecting enormous amounts of data, both personal and business. All of them can be introduced into Artificial Intelligence and machine learning systems to improve the efficiency of the implementation of policies and lawsas well as to offer services.
Everything, from garbage collection to call centers, thanks to data analysis, can be managed more efficiently thanks to AI, and improve investment and service prioritization. Of course, not without controversy, because once again, the use of AI in public administrations would also lead to a lower need for personnel.
In addition, there is concern among the authorities about the danger that Artificial Intelligence systems can imply in terms of bias, and in regard to the perpetuation of stereotypes and discrimination. Furthermore, reliance on these systems has sown uncertainty in the past about whether some public priorities will not be displaced in favor of others. Therefore, although the use of AI can improve efficiency in various areas, the authorities will have to carefully test and review its effects.
Transport
Workers in the transport sector are going to suffer a lot from the entry of Artificial Intelligence into their sector. In the long term, it is the most vulnerable to AI job losses. This is indicated by a 2021 report from PwC, which already predicted then that biggest job losses of the next 20 years they 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. But the advent of fully robotic and autonomous taxis is still far from a reality, and pilotless planes are, for now, only a distant possibility. Meanwhile, some public transport services are already using AI 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 action of Artificial Intelligence. But it may not be a traumatic situation, because what AI can do is help fill the gap what is currently between the offer of jobs and the professionals who can fill them.
Thus, banks and fund managers will need less staff to serve new clients, because they will automate tasks such as checking their records. They will also rely more on AI to detect and label potential fraud and actions that pose a risk of money laundering.
In addition, they will be able to insert new regulations from regulators into machine learning programs, so that they are able to identify possible gaps or deficits in the company’s systems, instead of relying on humans to identify them and make a first review of them. . Of course, these systems will continue to need human supervision. Not only to develop and program the necessary technology, but also to carry out additional tests, and to 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 there has been an error or that fraud has been committed. Or to provide personalized support to customers.
Retail
It is estimated that almost a third of jobs in the retail sector could disappear by 2030 compared to the levels they had in 2017. This situation will occur thanks to the warehouse automationthe checkout automation and the planning tools based on Artificial Intelligence.
For customers, the most notable change, already today, is the increase in self-checkout and self-scanning systems in supermarkets and other large stores in the last five years, accelerated during the pandemic. The number of ATMs could halve by 2030 as self-checkouts are rolled out.
The next step is the opening of checkout-free stores, such as those launched by Amazon. Thus, in Amazon Go, the system identifies the buyer and what they buy through cameras and sensors on the shelves. Purchases made by customers are automatically identified and recorded, and recorded in an app on their smartphone. Thus, they can leave the store and pay even after leaving the store.
But there is not only technology related to AI at the point of payment. Retailers are experimenting with robotic or AI-powered systems to identify shelf gaps. Others already use detection machines that move around the stores and the different shelf areas. There are also already electronic labels on the shelves, which allow price changes automatically from a central office, and have helped streamline tasks in stores. And already in the offices, technology driven by AI to have information regarding forecasts and purchase intention, and more robotics to move and place products in warehouses.
