MANAGEMENT: ARTIFICIAL VERSUS NATURAL INTELLIGENCE

Maurice Thévenet. Photo: Passion RH

Maurice Thévenet, Professor of Management at ESSEC Business School, consultant and Delegate General of the French Foundation for Management Education (FNEGE), shares a two-part article that looks into the dimensions of Artificial Intelligence versus Natural Intelligence.

First published in RH Info 14/3/18 under the title Management et Intelligence Naturelle

By Prof. Maurice Thévenet, ESSEC Business School

Science and art

Management requires intelligence, a capacity to understand, link things up and act. At least the study of finance, strategy, marketing or logistics provides the feeling of controlling reality but this is less the case for human resources or management. These fields touch on the mystery of human behaviours and if is it difficult to maintain good relations in the context of family and friends, it’s certainly no easier in the professional world where it’s a matter of producing results.

On the subject of management, tomorrow’s experts are many. Art is more difficult to tame and it’s for this reason that since the dawn of humanity we have never ceased to question ourselves and write on human behaviour in the field of war, religion, love, and – more recently – working together. We are thus always on the lookout for more intelligence and, as natural intelligence seems not to satisfy this thirst, we now turn with hope towards the artificial.

In a recent article, Davenport [1] observes current projects underway in artificial intelligence on a sample of companies. He identifies three categories of projects. The first, in the majority, consists in accelerating and automating the gathering and linking up of data in various IT systems. The diversity of information systems treating the issue of personnel obviously renders this perspective very interesting – likewise for the need to finally exploit the results of annual performance appraisals.

A second reference to artificial intelligence relates to deep learning or machine learning when we trawl enormous quantities of data for links, regularities and structures of proximity that enable us, for example, to receive targeted ads according to our previous browsing or purchases on the internet. Such possibilities are obviously useful for selection and recruitment, but also in assessing management via the tracing of actions, decisions or managerial behaviours.

The third category of AI projects concerns ‘conversation’ enabling a client or employee to interact with a machine that interprets requests and formulates appropriate replies. Without yet being in the year 2050 where we can imagine machines that can feel [2], these systems used, moreover in the majority, as Davenport says, with employees, provide the answer to all the questions that may concern them.

Better to tackle permanent problems

The article does not let itself go to the extremes of possibilities. Rather, it seeks to identify what is already at work in AI projects. But the perspectives it thus opens cannot leave management indifferent – a management still limited by the frontiers of natural intelligence. In effect, the processing of human issues in organisations is structurally confronted with three major problems. Problems that are permanent and for which we are still vainly looking for a definitive solution.

The first problem is that of measuring. Because managerial decisions exist, we need instruments to describe and compare the options before choosing the best solution. Finance or marketing possess these units of measurement. Moreover, we can always question ourselves, in terms of finance, for example, on the quality of these measurements: do they correctly represent reality? But the great advantage that financial people have is not the intrinsic quality of these measurements but the fact that every stakeholder agrees on the manner in which to measure and describe reality. This is not the case for things human: not only is reality difficult to describe but, even more, nobody agrees on the units of measurement. There is a world of difference in the way in which a line director and an HR director may assess an applicant! We can legitimately expect from AI that it enables other measurements to be applied on decisions, actions and results, for example.

The second permanent problem is that of theory. To tackle the mystery of things human, it is essential to multiply perspectives, change angle, question the limits of our perceptions to which we are far too often tempted to reduce reality with. Through its capacity to aggregate enormous amounts of past data by deep learning structures, regularities and recurrences, AI opens up new perspectives – not the unveiling of mystery but other ways in which to describe the real: it is the case, for example, of managerial behaviour analysis…or traces left on the social networks by job applicants.

There is a third issue, a little less ‘managerially correct’, to which HR specialists are confronted – that of human risk. In every management discipline, from information systems to finance to logistics, the question of managing is as much about money as controlling risk. It is likewise for human issues. Moreover, since a century or so, the preoccupation with reducing human risk has indeed been a constant issue in the development of organisations. Production lines rooted in Taylorism boil down to being independent of operators’ skills; processes and information systems mean supervising human initiative; artificial intelligence and robots is the means to ‘increase’ man, but also that of not taking a risk regarding the limits of his capacities.

We can therefore understand the current appetite for artificial intelligence because, confronted with these structural problems posed by the management of humans in our organisations, it is clear that artificial intelligence cannot suffice. However, it cannot be forgotten that artificial intelligence has other properties, in particular that of nourishing or strengthening illusions to which man is very sensitive – those of control, virtue, and the philosopher’s stone.

[1] Davenport, Harvard Business Review

[2] Alexandre, L. La guerre des intelligences.

Read Part 2 of this article

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