You have almost certainly sat in the meeting.
A strategy is being reviewed. The numbers are assembled, the slides are clean, the language is confident. Around the table sit fifteen people, most of whom privately suspect the plan stopped describing reality months ago. Nobody says so. Not because they are cowards or lack the analysis, but because the room has already told them what contribution it wants.[…]
A retail chain wanted its apprentices to show up more reliably, so it paid them a bonus for attendance. Absences went up by about half, which came to more than five additional days per person per year, and they stayed up after the bonus ended. Jakob Alfitian, Dirk Sliwka and Timo Vogelsang ran it across 232 stores and 346 apprentices and published it in Management Science as “When Bonuses Backfire”.[…]
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An executive is asked how far along his company is with AI. He answers precisely: two pilots, one policy, forty people trained. Every word is accurate, and it describes a tenth of what is going on there. In the same week, a function that arrived with a March software update is pre-sorting job applications in his HR department. Nobody decided this. Nobody concealed it either. It came with the release notes.[…]
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Early in my forensic training, I found myself in a room I should not have been in alone.
The angle was wrong. The emergency bracelet on my wrist — standard protocol in that setting — would not transmit a signal from where I was sitting. I stayed anyway.[…]
A few weeks ago, I asked my AI agent to shortlist vendors for an event this fall.
It came back in under a minute, having quietly ruled out a firm I have worked with for years. The reasoning came down to one compressed verdict: an average rating and an old article about a project that went sideways.[…]
A few years ago, after a transformation program delivered unusually strong returns, a senior peer leaned over and said the numbers looked too good. Keep a lid on them, he advised, in case other teams got nervous. The technology had worked. The politics had not. That single remark contained the problem most AI programs still refuse to name.[…]
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Interview was conducted by Michaela Ernst / sheconomy.
1) Many leaders treat demographics as background data rather than a strategic force. What does it mean for a company to become truly demographically intelligent?
Avivah Wittenberg-Cox: I call this becoming “longevity literate.” Companies need to become “longevity ready.” Above all, this means putting the topic on leadership agendas, because in many organizations it still is not. […]
Interview was conducted by Michaela Ernst / Der Standard.
Katy George: From the outset teams should define which tasks must remain human – especially in decision-making – and how AI should be responsibly used. On top of that, companies need to invest heavily in empowering their employees with AI skills. The better they can utilize AI, the more it will complement rather than replace human labor. […]
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