PART II:
The missing piece to get AI to work: HI
by Hans Rusinek

ChatGPT has passed an MBA exam at the Wharton School. People were stunned. But what should be stunning about that is that it once again showed how little leadership education focuses on developing the human ability to reflect, question and be empathetic. How little human intelligence is valued and required in management education has been thoroughly researched as has their consequences on the world of work and its productivity^[1]. If we reduce our management training to a multiple-choice logic, we don’t need AI for us to become dominated by machines. And we will not be able to ask the right questions, solve the right problems and become more productive.

For the philosopher Byung-Chul Han, human thinking is a decidedly embodied process^[2]. Before we can conceptualize (begreifen) the world, i.e. develop strategies, formulate declarations, write an email, we must first be gripped by the world (ergriffen). For Han, emotional connection with our environment is essential and primary to our thinking: “Thinking begins with goosebumps”^[3]. Artificial intelligence cannot think in this fundamental sense. What it can do is calculate quickly. But in order to become more productive at work, we need to understand what the real irritations are that employees suffer from, what gives them goosebumps, or what gives them the ick.

Therefore the productive implementation of AI in the workplace depends on our meaningful use of HI, of human intelligence, towards that goal.

Pilotitis, Stealth-AI and deskilling: How organisation’s HI is unprepared for AI

Even the best tool is useless if we have a management problem, a problem with enabling all employees to use their HI to the fullest. Three recent phenomena from organisations trying to make use of AI illustrate this: Pilotitis, Stealth-AI and deskilling.

In many workplaces, cases of pilotitis are rampant: Companies are hesitantly tinkering with peripheral pilot projects on AI that remain detached from any real world implementation: A study by the US Census Bureau^[4] showed that only 5% of American companies state that they use AI to actually produce goods or services – in Europe, we can expect even lower figures. Playing with pilot projects distracts them from implementing and using the technology in real business cases that brings them closer to their goals. In a recent study^[5] conducted in 14 countries, only 8% of CEOs stated that their companies had implemented more than half of their experiments with generative AI. Are these still purposeful attempts or is this fake work to live up to the hype, and to convey to customers, applicants etc. that they are ‘AI-driven’? This cannot be increasing productivity.

Shadow AI

Interestingly, employees themselves are much less hesitant than their organisations. They make use of practices we could call “Shadow AI”. Although only a few companies in the American study were able to report that they are using AI, a third of their employees stated that they are using AI secretly. In some roles, the figure is even higher. 78% of female software developers use AI at least weekly (up from 40% in 2023), as do 75% of HR professionals (up from 35%). And OpenAI says that, significantly, 75% of its revenue comes from consumers, not enterprise subscriptions^[6]. It’s used secretly to streamline tasks like rewriting text or creating reports. Employees might fear that otherwise their superiors will give them more work or take it as a sign that fewer jobs are needed if they admit that they are using AI to get things done faster. Or they will bury any practical use of AI under layers of bureaucracy and regulations.

Deskilling

Such a necessary culture of trust would also include trust in one’s own abilities. Dennis Fischer, a management consultant, reports that he is experiencing more and more cases of AI-driven deskilling: Deskilling happens when people switch off their brains and unlearn skills while relying on AI for that task. In many of his workshops, he finds that participants first use GenAI-tools in brainstorming sessions and then no longer have the motivation to add their own ideas. On a larger scale, deskilling not only leads to a loss of knowledge and skills in a society, but also to a decline in self-efficacy and trust in one’s own intelligence: if we always think ‘AI can do it better anyway’, we might end up with less confidence in our HI and increasingly succumb to automation bias, he reports.

Whether it’s distracting pilotitis, Shadow-AI or deskilling, all of this shows that we need to get our HI, our human intelligence, up to speed before we can make full use of AI. Otherwise, technological hopes will be crushed by all-too-human failures.

About the author:
The economist and philosopher Dr. Hans Rusinek teaches at the University of St. Gallen on the future of work, works as an independent management consultant and is a Fellow of the Club of Rome Germany. Prior to that, he worked at the Boston Consulting Group.

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^[1] e.g. Marylin Sargent, Ego Development and Choice of Major Field in College, Paper, Meeting of the Rocky Mountain Business Law Association 1986.

Robert Frank et al., Does Studying Economics Inhibit Cooperation?, in: The Journal of Economic Perspectives 7 (1993), Nr. 2, S. 159–171.

Robert Williams et al., Managers’ Business School Education and Military Service, in: Human Relations 53 (2000), Nr. 5, S. 691–712.

Donald Mccabe et al., Academic Dishonesty in Graduate Business Programs, in: Academy of Management Learning and Education 5 (2006), Nr. 3, S. 294–305.

Reinhart Selten/Axel Ockenfels., An Experimental Solidarity Game, in: Journal of Economic Behavior and Organization 34 (1998), Nr. 4, S. 517–539.

Björn Frank/Günther Schulze., Does Economics Make Citizens Corrupt?, in: Journal of Economic Behavior and Organization 43 (2000), Nr. 1, S. 101–113.

^[2]Byung-Chul Han, Undinge, p. 31. 2021. Ullstein Verlag, München.

^[3] Ibid.

^[4] Kristina McElheran et al. „AI Adoption in America: Who, What, and When“, Center for Economic Studies at the US Census Bureau, 2023

^[5] Deloitte, „Now decides next: Moving from potential to performance. Deloitte’s State of Generative AI in the Enterprise”, 2024, available online at https://www2.deloitte.com/us/en/pages/consulting/articles/state-of-generative-ai-in-enterprise.html

^[6] https://www.economist.com/business/2024/11/04/why-your-company-is-struggling-to-scale-up-generative-ai