Decision Trees Can Bring Out The Best In New Talent


The idea of decision trees is simple to apply when we are looking at project management. Put the probabilities of failure and success and apply them to financial predictions. Choose the decision that provides the highest expected returns.

So how do we apply this concept in looking at human talent? Here are some tips.

Human Talent

It is not possible to determine the full inner motivations of each newly hired talent. During the initial interview, the talent could have been groomed with a diet of self-help books and YouTube tutorials. Only time can tell about the real value that the talent brings to the plate.

Unfortunately, the importance of constructing a decision tree is magnified by how early it can be used to develop, train and evaluate the talent. The reason is that the talent may not stay long enough to be fully developed.  

Initial Investment

The decision tree always starts with various initial investments. This is expected to signal a possible loss if the wrong decision is made. These is a penalty in bad decisions that are made. Most importantly, all the initial investments highlight the value that the organisation places in each new talent. There is an openness of a two-way communication in how each part can negotiate.

Financial outcome

The most difficult part of this is creating an accurate financial outcome of how this talent will contribute to the organisation. If this employee is a workplace counsellor, is the financial outcome calculated by the increased productivity derived after the sessions? Or in the case of the person washing dishes in a restaurant, can we measure tangible changes in the speed of the washing as compared to investing in a dishwasher?


During my own lessons, I find my students are most sceptical about how probabilities are derived. I start by saying that detailed market research is conducted and, in most cases, there are advanced statistical experiments done to ensure that any error of prediction is minimised to an acceptable rate – which means that there is no way in which we can get a perfect prediction of the future. But here is a thought: if all probabilities have the same margin of error, then when combined, wouldn’t the analysis be much more accurate?

Dynamic environment

Nobody could have predicted that the world would have plunged into a Covid pandemic in 2000 and large unemployment would be seen across the globe. Thus, the typical decision tree has a big disadvantaged of being presented in a static manner. However, with improvement in the gathering and analysis of data, the different components of the decision tree can be made more accurate.

For the purpose of debate, what if there is an interference in the environment which causes the talent to revalue its worth? Take the case of Lionel Messi’s transfer to PSG and his large salary expectations. PSG must sell 5 of its current batch of players to make up for the shortfall. Take the position of one of the PSG players who have to leave the club and whose children have just settled into a school of their liking. This sudden situation is like a bolt of lightning that forces him to relocate and readapt.


It is imperative that HR has an open-door policy of allowing each new talent to communicate his feedback about work in a non-confrontational manner. This will then allow both parties to agree on getting the relevant training programmes to boost the productivity of the talent without taking too much out of his rest period. This will create a high level of buy-in by the talent who may eventually play the crucial role of a mentor, if he chooses to stay on with the company.

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