In the debate about artificial intelligence and leadership there is one sentence more uncomfortable than most promises of salvation: An average AI today may give a manager more precise feedback on their own communication than an average manager gives their people.
The sentence does not elevate any technology. Its appeal lies elsewhere: it shifts the question. The common question is whether AI replaces managers. The answer is unspectacular — no. The more productive question is: which parts of today's leadership work genuinely require human judgement, and which are repeatable patterns that simply nobody has observed systematically until now?
What AI actually makes visible
Katharina Lange and José Parra Moyano of IMD Lausanne built a language-model-based tool and tested it with 167 managers from various industries and countries. The setup was deliberately plain: managers coached one another through real professional situations while the tool listened. Afterwards they asked for an assessment — which style did I use, which patterns repeat, what do you see that I do not?
That a language model can classify conversational structures is hardly surprising. What stood out was the size of the gap. Around fifty-five per cent of the responses fell into a learning zone where the feedback was both unexpected and useful: it challenged assumptions and exposed blind spots. About ten per cent landed in an irritation zone — usually when the feedback contradicted the self-image.
These figures deserve a sober reading. They come from a single study of limited methodological reach. A model is only as good as its data, it can hallucinate, and it captures cultural nuance or unspoken signals poorly. The authors' conclusion is explicit: AI complements human expertise, it does not replace it.
An old problem, newly measured
Leadership research knew the finding long before language models. Since the work of Leanne Atwater and Francis Yammarino, self-other agreement has been an established indicator of leadership effectiveness. The most robust finding across many studies: Those who systematically overrate themselves are judged less effective — and respond more weakly to developmental feedback.
Leadership was long a field in which self-image, external perception and actual effect were hard to tell apart. Not out of incompetence, but because reliable, frequent, unvarnished feedback is expensive and rare. What language models change is not the insight that the gap exists. It is the price at which it becomes observable.
Leadership thus shifts from a quality attributed to a person to an observable behaviour within a system. The decisive question is no longer: how did I mean it? But: what effect did my behaviour produce?
Why the mirror is accepted — and where it is not
Research knows two opposing patterns. Logg, Minson and Moore described algorithm appreciation: under certain conditions people weight an algorithm's advice more heavily than a human's — partly because a model does not appear to judge and burdens no social relationship. Listening to a machine requires no saving of face.
The pattern is not stable, however. Dietvorst, Simmons and Massey demonstrated algorithm aversion: once people see an algorithm make a mistake, they turn away faster and more decisively than they would from a human. This is most pronounced among experts and in tasks considered value-laden — precisely the terrain of leadership.
An uncomfortable ambivalence follows: the same manager who gratefully takes in feedback in the learning zone may reflexively dismiss it in the irritation zone. And the same apparent objectivity that builds trust can become a new form of diffused responsibility. Phrases such as “the data supports it” sound sober — and yet imperceptibly shift accountability from a person to a model.
Development or control — a design question
The same instrument can produce two very different social logics. A protected space for reflection, in which managers voluntarily analyse their communication patterns, can be extraordinarily valuable. A system that continuously measures, compares and rates managers will hardly produce better leadership. It will mostly produce conformity. People then speak more smoothly, act more cautiously, risk less.
From which it follows: AI governance must not be reduced to data protection, tool approvals and technical security. Those aspects are necessary but fall short. Governance must also settle which social logic emerges — whether the system encourages reflection or control, whether it increases accountability or shifts it. It must also settle who may see the analyses, whether they serve development or assessment, and whether participation is voluntary.
In practice this logic is decided earlier than many assume — in apparently technical choices. A mirror whose analyses only the manager sees produces development. The same mirror whose data flows into performance objectives produces conformity.
What leadership remains
The reason lies in a property of AI that gets lost in efficiency debates: It amplifies what is already there. An organisation with unclear decision paths does not become more decisive through AI. An organisation that covers up mistakes does not become better at learning through better analytics. The prior question is therefore not which processes get faster, but whether an organisation is mature enough to handle the transparency AI creates.
That is precisely why human judgement becomes more important, not less. An AI can analyse a sequence of conversation, but it does not know the history of a conflict. It can structure a decision option, but it does not carry the social consequences of a wrong decision.
Peter Drucker wrote decades ago that the first task of leadership is to lead yourself. AI does not make that task redundant — it makes it verifiable.
Whether AI replaces managers is therefore not the decisive question. What matters more is whether managers are willing to become more visible through AI. AI does not take responsibility away from leadership. It takes away the excuses.
A note on AI use: this article was written with AI support — not exclusively, but particularly for source research, source checking and linguistic quality assurance.
Sources
- Lange, K. / Parra Moyano, J.: Research — How AI Helped Executives Improve Communication — Harvard Business Review, February 2025; study with 167 managers, IMD Lausanne
- John Heron: Six Categories of Intervention — an intervention framework from consulting, coaching and supervision
- Atwater, L. / Yammarino, F. — research on self-other agreement in leadership
- Lee, A. / Carpenter, N. (2018) — meta-analysis on the divergence of self-image and external perception
- Logg, J. / Minson, J. / Moore, D. (2019): Algorithm Appreciation — acceptance of algorithmic judgement
- Dietvorst, B. / Simmons, J. / Massey, C. (2015): Algorithm Aversion — rejection after an observed error
- Leadership interview with José Parra Moyano — manager magazin, July 2026
This article offers a professional assessment and does not replace legal or management advice.
