Changing professional roles

A pattern that was strongly evident across both sessions is the shift from generating output oneself to evaluative and steering work.

Within CMM, this is evident in the selection of concepts, the assessment of creative output, the monitoring of brand consistency and the management of AI-generated variants.

Within TI, the same trend is evident in reviewing generated code, monitoring architecture and overseeing AI processes. Within TI, this is further illustrated by the ‘diamond-shaped’ staffing pyramid: participants expect that certain traditional junior roles may come under pressure, whilst the need for strong mid-level roles may increase.

Both sessions make it clear that AI does not necessarily lead to less complex work. The complexity shifts from execution to assessment, interpretation, monitoring and the assumption of responsibility. This shift in roles therefore requires not only different professional competencies, but also professional dispositions that encourage professionals to actively apply their judgement, take responsibility and ensure quality control as AI plays an increasingly significant role in the work process.

This immediately raises an important educational question: how do you build up that essential mid-level workforce when traditional entry routes come under pressure?