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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.

The development of artificial intelligences is greatly increasing cybersecurity threats. Their use by hackers could enable deploying a combination of ultra-personalized attacks leveraging the company’s specific information. For instance, imagine a phishing call using an AI-generated voice that can near-perfectly mimic your boss’ tone and conversational style—a science-fiction scenario that is about to become a reality…

What if you used the power of AI to protect yourself from this risk? Some companies are already working on designing software tools, such as ZeroGPT, to identify AI-generated content. AI can also be used to improve cyber-risk detection capabilities. For instance, a customized AI will be able to easily detect suspicious changes in an employee’s online behavior—a sudden increase in the amount of data consulted, a significant change in messaging structure, etc.—and, if need be, trigger an alert. Of course, these new tools won’t go without raising ethical questions regarding the protection of personal data—but they will quickly become unavoidable. A new field to keep a close eye on.

Source: From ChatGPT to HackGPT: Meeting the Cybersecurity Threat of Generative AI, Karen Renaud, Merrill Warkentin, George Westerman, MIT Sloan Management Review, April 2023.