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The arrival of every new generation leaves previous generations puzzled. Generation Z, born between 1997 and 2010 and currently entering the job market, is no exception to this rule. Classically, these young recruits present new expectations and new ways of approaching the employer-employee relationship. According to research conducted by Andrei Adam, a specialist in talent management, three salient elements stand out among members of Generation Z:

- A stronger need for flexibility in organizing one’s own work.

- The search for a workplace that is also a place to socialize: an expectation that can be found among all employees since the Covid crisis, but one that appears to be particularly marked among the young generation.

- The need to develop in one’s professional day-to-day. With Generation Z, there is no longer any question of long-term commitment to one’s employer. Young people see their current position first and foremost as a springboard to the next ones. Faced with this expectation, an employer can only benefit from taking its employees’ plans into account and making itself a fruitful and stimulating step within them. Do you want to attract the talents of Generation Z? Make sure you offer them personalized support to develop their employability: development coach, mentoring program, ongoing training, etc.

Source: 3 ways to retain your Gen Z employee, Andrei Adam, TEDxMcGill, YouTube, August 2023.

It’s an accepted fact: to make a quality decision, it is best to collect as much information as possible and analyze it with care. But is that always true?

Many research studies invite us to nuance this conviction. They show that, in certain contexts, it is beneficial to avoid an in-depth analysis of the situation. In such cases, it is best to content ourselves with deciding on the basis of simple criteria, such as empirical rules founded on past experiences. This can be observed in three situations:

- An uncertain context, saturated in information: when multiple data and analyses are available and the state of the art doesn’t allow a solid decisional basis, adding still more information and analyses only increases the cognitive load, without further clarifying the decision to be made.

- A fluctuating environment: in fast-evolving markets, data is sometimes obsolete before it has even had the time to be collected and processed.

- Difficulties accessing information: sometimes, the necessary cost of collecting information in the needed quantity and quality isn’t justified by the potential benefits associated with a better-informed decision.

In such circumstances, the quality of the decisions taken depends less on the finesse and exhaustiveness of the analyses than on the ability to mobilize our experience or that of our experts. A counter-intuitive discovery in the age of Big Data!

Source:  The Potency of Shortcuts in Decision-Making, Sebastian Kruse, David Bendig, Malte Brettel, MIT Sloan Management Review, September 2023.

Experiments with the four-day week are multiplying. Although it is still too early to draw any definitive conclusions, some feedback seems promising—as at Perpetual Guardian, a property management company in New Zealand, where this set-up allowed employee engagement rates to increase by 40%. However, feedback underlines two essential conditions for the viability of this new rhythm:

– More than imposing one fixed working day less, the main thing is that employees obtain the free time that has the most value to them. At Perpetual Guardian, some people thus opted for an entire day off; others preferred to work five days, but with shortened working hours—particularly some parents, in order to facilitate childcare.

– This new organization must be accompanied by an in-depth reflection on how to improve productivity. An efficient approach consists in helping everyone to think about the least productive moments of their day and in reviewing certain processes accordingly. This could involve, for instance, introducing interruption-free time slots, holding shorter meetings, or making rest areas available.

Source: The Four-Day Workweek: How to Make It Work in Your Organization, Andrew Barnes, MIT Sloan Management Review, June 2023.

New AI tools like ChatGPT can make for good allies in accelerating decisions and improving their quality. While there is no question of delegating the decision-making to them, it can be beneficial to involve them at three stages:

– Ascertaining the context: ChatGPT helps highlight the obstacles and the key success factors taken into account by other companies in similar contexts. Sample request: we are a company in the technology sector, based in the PACA region of France. We’re having difficulties attracting new talents; what might be the reasons for this?

– Defining the possible options: ChatGPT contributes to expanding the range of options and to generating counter-intuitive avenues. Sample request: how have some companies succeeded in limiting their dependency on a given raw material?

– Evaluating the various solutions: for the time being, ChatGPT doesn’t allow you to compare the advantages of each option. But it can help you gain awareness of the biases that are harming the quality of decisions, in certain contexts. Sample request: what are the main risks to keep in mind when trying to recruit within a short time?

To obtain the best possible contribution from AI tools, interaction and questioning are key: we benefit from refining our questions and digging beyond the AI’s first answers.

Source: Using ChatGPT to Make Better Decisions, Thomas Ramge, Viktor Mayer-Schönberger, Harvard Business Review, August 2023.

In 2023, eight out of every ten companies planned to invest in machine learning. This sub-field of artificial intelligence permits the detection of recurring patterns within data to guide decision-making.

Many decisions can thus be delegated to algorithms: selecting among candidates for a recruitment, for a loan…  But how can we educate our algorithm to avoid biases, and in particular discriminatory ones? Experimentations have indeed shown that AI risks amplifying discriminations already in play. This results from the fact that it relies on selection histories to carry out its training—histories that are often biased and lead to certain populations going under-represented.

In rather counter-intuitive fashion, a study on a credit-management algorithm suggests that sharing sensitive personal data with it, rather than masking this data during its training, allows to meaningfully reduce the risk of discrimination. Cherry on top: the profitability of the loans granted by this algorithm also increased by 8%. When it isn’t possible to include this data directly in the algorithm’s training phase, corrective factors can be applied to rebalance the samples it receives, for instance by increasing the share of traditionally under-represented populations.

Source: Removing Demographic Data Can Make AI Discrimination Worse, Stephanie Kelley, Anton Ovchinnikov, Adrienne Heinrich, David R. Hardoon, Harvard Business Review, March 2023.