Emerging skills across both domains

When both sessions are considered together, a clear overlap emerges in the competencies that are gaining in importance. Although the context differs, the same underlying professional capabilities recur. The dispositional lens reveals that these competencies cannot be viewed in isolation from the willingness and sensitivity with which professionals apply them in specific AI-integrated situations.

Critically evaluating output

In both domains, expertise is shifting increasingly towards the assessment of generated output: evaluating quality, detecting errors, weighing up relevance and aligning output with context and objectives.

From a dispositional perspective, however, it is not enough for professionals simply to be able to critically assess AI output. The sessions also suggest that they must be sensitive enough to recognise situations in which critical assessment is necessary and must remain willing to question AI output, even when it appears convincing or efficient.

Interpreting context

In both sessions, AI proved to be weaker than human professionals in understanding context. The ability to correctly interpret implicit expectations, understand stakeholders and make sense of situations therefore becomes a key human competence. The sessions also suggest that contextual sensitivity does not rely solely on knowledge or experience, but also on the professional acumen to notice relevant signals and incorporate them into professional judgement.

Ownership and responsibility

Ownership is identified as a core competence in both sessions, explicitly within TI and implicitly within CMM. Professional autonomy means not only taking action, but also assuming responsibility, defending decisions, correcting mistakes and remaining accountable for the consequences.

The findings also suggest that ownership is not merely a competence, but also a professional disposition. Taking responsibility in AI-integrated contexts requires not only that professionals know what constitutes correct action, but also that they remain willing to bear responsibility for decisions and outcomes in which AI has played an active role.

Professional practice with AI

Professional practice within AI-integrated contexts therefore extends beyond mere operational AI proficiency. Both sessions make it clear that being able to use AI fluently is, in itself, insufficient to function professionally and competently within AI-integrated contexts. Professional added value only arises when the use of AI is combined with sufficient domain knowledge, critical judgement, contextual awareness and responsibility for the consequences of one’s actions.

The distinction between being able to use AI and being able to act professionally with AI therefore constitutes a significant underlying tension within both fields. From this perspective, professional practice does not arise solely from the acquisition of AI-related knowledge and skills, but also from the professional willingness to actually apply judgement, responsibility and critical reflection when AI plays an active role in the work process.

Communication and interpersonal skills

It is striking that both sessions emphasise the importance of interpersonal skills rather than downplaying them.

Within CMM, this is explicitly highlighted through client communication, pitching, interpreting briefs and non-verbal communication. Within TI, it is less pronounced but is nonetheless evident in managing expectations, consulting with stakeholders and explaining technical choices to the business side.