The Impact of Generative AI on Society and Work

Deskilling is a phenomenon where the need for skilled labor within an industry is eliminated or diminished by the introduction of technologies operated by semi-skilled or unskilled workers.

The modern digital workplace has freed workers to access equipment, information, and co-workers from anywhere, decoupling work and workers from physical space. The point is that as new roles emerge and skills requirements change, the size of the existing pool of skilled workers just isn’t going to be big enough to meet demand.?Today, for example, there is a massive shortage of people skilled in data science and AI.

In order to innovate in an increasingly intelligent automated digital world, it is important for workers to be digitally literate in AI and capable of understanding the adoption of new technologies and learning how to integrate them in their routines at the workplace.

Societal implications of the AI Technological disruption

This problem has wide societal implications and the recent AI technological disruptions are making different social actors to rethink how this change will impact their respective areas and how to adapt.

This can be achieved through retraining and the reskilling of the current workforce that face the challenge and opportunities that arise in the age of automation.

In order for the reskilling process to happen, a massive talent divide must be addressed and a continuous learning approach for both company and workers should go together. This is because this phenomenon has huge societal impact and represents a shift in the way people work and the way we interact with technology.

The AI technology is starting to act as a “copilot”, assisting the human in charge of the input driving the outcomes, in order to generate tangible results with greater time to value. This shift will improve productivity of companies that are successful in adopting this new technology of generative AI with the creation of AI use cases at scale that will have greater impact in generating benefits for society as a whole.

Some examples of generative AI use cases include:

• Logistics and transportation: Generative AI can accurately convert satellite images into map views, enabling the exploration of previously unknown locations.
• Marketing: Generative AI can be used to create personalized marketing campaigns.
• Entertainment: Generative AI can be used for music generation, video editing, voice synthesis, film/music production, fashion and gaming.

Increasingly, the use of technology in organizations worldwide will demand savvy business people that can leverage their results with a better understanding of AI and its applications for the better use of the resources available in order to tackle the challenges and to identify new opportunities of value generation at scale to customers and society.