what impact on civil service professions?

Public jobs around the world should not escape the generative artificial intelligence revolution. 36% of tasks carried out by civil servants are exposed to significant changes and a little more than 7% of jobs could be automated (according to a study by the Roland Berger firm). How do we achieve this result?

To carry out this work, the Roland Berger firm analyzed in detail 450 civil servant professions across the globe. Each job was divided into tasks. The study counts 9, for example, for a senior civil servant. The authors then measured the exposure rate of each task to artificial intelligence.

The results show that among all the tasks carried out, 36% on average are highly exposed to the arrival of generative AI. All those, precisely, which have an important administrative or intellectual dimension, or which require significant expertise.

There are two scenarios according to the study. The first is automation. 7.5% of civil servant jobs around the world could be completely automated and therefore replaced by generative AI tools. The most threatened are office workers, particularly secretaries, postal workers, receptionists and call center workers.

For what ? Because almost all of their tasks are exposed to generative artificial intelligence, well beyond the average, and at the same time their work is very standardized, explains Alain Chagnaud, one of the authors of the study. For example, it is recording, organizing, storing and retrieving information or scheduling appointments, which AI already knows how to do.

The second scenario concerns public service professions with only a few tasks highly exposed to AI. These professions will not disappear, but will be reshaped by artificial intelligence. The latter will allow you to work faster, by taking care of certain tasks. According to the study, lawyers, executive secretaries, customs officers, teachers, or tax service agents will be particularly concerned.

Faced with these scenarios, the study concludes that States must immediately prepare training and professional retraining policies, adapted to the different cases. This involves, for example, avoiding directing younger generations towards civil servant jobs likely to be automated by artificial intelligence.