Author: Shailesh Rastogi
Management education excels at helping students discover answers. It is much less intentionally precise about what action to take should a solid answer prove elusive. This space, between problem-solving and identifying the nature of a problem, is the unaddressed dimension in most management programmes.
The handle on this difference comes from an economist called Frank Knight, who distinguished risk from uncertainty in 1921. With risk, the odds are calculated from experience. A retailer stocking for a holiday season has sales to guide them. With no experience, it is uncertainty. A company reacting to a policy shock, a new technology or a supply shock cannot see the probabilities and may not know what options exist. Predicting, working out decision trees and calculating expected values all work well in the first case but offer little in the second.
Classrooms foster this blindness. We receive cases with the solution clearly laid out. We work with clean data and are rewarded in our assessments for producing that one clear answer. We train students to spit out the answers, but not to ask themselves whether the problem they face can be solved using the data given.
Technology makes this scarier still. Retrieval, modelling and preliminary comments can all be generated in mere moments. Surveys by organisations such as the World Economic Forum indicate precisely where the value is moving: the Future of Jobs Report 2025 lists analytical thinking as the number one “core skill”, followed by resilience, flexibility and agility. Cheap analysis makes judgement scarce, and that shows itself in asking whether you should even bother reading this.
You don’t need a new subject to teach this. What you need to teach is for students to do something differently, and there are three shifts:
One is practising decisions while the facts settle. Simulations and live case debates, where information is incomplete, teams support contrary arguments and the call is made before the picture gels, are more realistic than a well-defined case with a known outcome. Badruka School of Management (BSM) employs both.
The second is evaluating the questions, not just the answer. When AI is writing up a strategy pitch, grading the pitch as though it is the end of the road offers limited value. The tougher challenge is determining how students will stress-test it: what assumptions lie beneath it, what context is absent and what second question might alter the result. That’s what BSM is integrating into the way students work with AI in class.
Third is learning by reading the work of decision-makers faced with uncertainty. We know how decisions are made with scarce data and concrete consequences, and the authors we read have anecdotal stories that defy the structure of a textbook. The block-teaching format of BSM was one way we encountered practitioners.
Underlying all three is a short set of habits: name the kind of uncertainty at hand, whether unknown odds, unknown options or unknown goals; ask second-order questions of any confident output; and prefer small, reversible bets to large, irreversible ones, treating early action as a way of learning.
None of this is a finished solution. The test for any management programme, however, is simple: does a graduate know what to do when the numbers run out? It is worth asking whether curricula and the ways institutions are assessed measure that.
The writer is the Director, BSM Hyderabad; Views presented are personal.