Opinion column: Do we still need to teach decision-making in the era of A.I?

Opinion column by Patrick Martens 

​How well I remember starting my first job many years ago! During my first working day, amongst other things, I discovered that my job carried a grade and that grade was based on a job evaluation system which determined every employee’s salary as well as, rather disturbingly, status and position in a one industry town. This did not come as a complete surprise to me because I had studied HR management at a business school, so I knew that there were several competing pay systems used by companies. What set this one apart though was its use of a single criterion to evaluate and differentiate jobs – decision making. I recall early discussions with my boss during which I questioned this and argued about the wisdom of such a reductionist system (he said it was the company’s prescribed system and that was that). Years later, however, after learning so much more about social cognition, judgment and decision making, I have come to believe that these remain an essential skillset that senior managers and leaders require – even, and arguably more importantly, in the age of A.I.

My own personal academic decision-making odyssey first developed through knowledge of Herbert Simon’s pathfinding work on how and why decisions end up being sub-optimal due to cognitive limitations and other constraints. Later, the highly empirical research conducted by Kahneman and Tversky revealed the cognitive biases behind the everyday decisions and judgments that we make. This groundbreaking work led to decision making theory increasingly entering the academic curriculum in programmes on leadership, organisational behaviour, cross-cultural management, finance, economics and negotiation. The question then arises as to whether this is still relevant given that A.I. is believed by many, including those in major companies, to provide all the support we need to make decisions.

In fact, the major decisions in business have never been about processing information quicker or detecting patterns more efficiently. In a trenchant recent essay, Blair Effron, an investment banker, has argued that the most salient concerns are questions such as what kind of enterprise a firm should aspire to be, what culture it should embrace, what risks it should tolerate, and how its leaders should plan when the future is unclear. These are questions of judgment he argues, and judgment cannot be automated, at least not any time soon. Judgment, the ability to consider several different courses of action and decide on the best one, is a uniquely human skill. Effron goes on to present real cases where A.I. was used as a support tool in major merger and investment decisions but was shown to be inferior to the judgment exercised by effective human decision makers (Effron, 2026). So, A.I. while undoubtedly transformative and useful, is not the whole answer to the challenge of applying effective leadership and management to complex problems. Put differently, A.I. can promote efficiency to a greater extent than it can provide effectiveness.

The above arguments are underscored by the considered views of Yoshua Bengio, known as the godfather of AI. Bengio is certainly very much on the side of the cautionary A.I. safety advocates (as compared to uninhibited growth-oriented positions of Jenson Huang, Sam Altman and Elon Musk amongst others). Bengio argues that we need guardrails in place and the reason is the problem of the subjective perception of consciousness. He is describing the cognitive biases at work in the way we seek information and make decisions based on what we see. The confirmation bias, for example, may click into place whether the information comes from a human source or is machine generated. We see what we want to see, according to our preexisting biases, and then judge and decide accordingly.

Academic institutions, including universities and business schools can benefit from A.I. while at the same time contributing to the structuring of the guardrails that Bengio refers to.  This means well designed curricula and effective policies governing the use of A.I. by students and faculty. As a modern, international business school MSM, for example, is taking this seriously by developing fit for purpose leadership programmes and giving constant attention in faculty meeting agendas to the challenges and opportunities posed by A.I. Our key task is to ensure that the key challenges of making sound business judgments and decisions are addressed. This is our responsibility as educators and leadership developers.

These reflections align closely with MSM’s Artificial Intelligence and Digitalization Framework, which positions AI not as a replacement for human judgment but as a strategic enabler embedded within broader decision-making, governance, and capacity-building processes. By combining phased AI integration with strong leadership and ethical oversight, the framework reinforces the idea that effective decision-making remains a fundamentally human responsibility. In this way, AI becomes a tool to enhance managerial judgment and institutional effectiveness, rather than a substitute for them.

About the author - Patrick Martens
Patrick Martens is lecturer, management trainer and consultant at MSM. He teaches change management, cross-cultural management and HR management in MBA and MM programmes -  both online and in face-to-face settings. He has extensive international experience having worked on a large number of projects and consulting assignments in many countries over 35+ years. Apart from the teaching topics mentioned, his expertise includes international trade, institutional development, negotiations and conflict handling, and project management.

Reference : Effron, B. (01-25-2026). Why A.I. Can’t Make Thoughtful Decisions. The New York Times.https://www.nytimes.com/2026/01/25/opinion/ai-human-judgment.html

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