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Crises follow one another and each is different. The ones we are currently undergoing are all the more complex for combining armed conflicts with economic, societal and sanitary disruptions. The time has come to take a closer look at McKinsey’s retrospective analysis of the crisis of 2007.

Approximately 10% of the 1,100 companies studied across 12 business sectors proved to be particularly resilient: they not only withstood the crisis, but also prospered in the process.

What did they do to achieve this feat? Most of them very quickly gave themselves the means to be flexible. At the first signs of the crisis, they strengthened their balance sheets by withdrawing from underperforming activities and reducing their debts. This gave them the means to ride out the crisis while maintaining a sustainable financial situation, then to seize the opportunities offered by the market rebound. They also focused on the reduction of their production costs, while maintaining their expenses relating to sales and administrative support. Operational flexibility played an important role, with the renegotiation of more flexible contracts and the expansion of supply sources.

In a context of crisis, investing in flexibility seems to always be a good first move to facilitate the resilience of one's organization.

Source: Bubbles pop, downturns stop, Martin Hirt, Kevin Laczkowski, Mihir Mysore, McKinsey Quarterly, May 2019.

Putting an end to projects or cutting loose from historical activities is always difficult. And yet it is a practice that distinguishes companies that are lastingly over-performing. The logic is rather intuitive: perpetuating dead-end projects only disperses resources and undermines the company's ability to invest in more solid avenues. Nonetheless, in practice, many biases make this process difficult. Consulting firm PwC has identified four practices that can help make the divestment exercise more natural and more effective:

- Implementing standardized and regular portfolio evaluations. The cadence is important to making this a habit.

- Carrying out an in-depth review of each project in the portfolio, including the analyses of historical financial data, but also of extra-financial information and of the current and future competitive environment. Many companies stop with the first of these analyses.

- Involving the board of directors in these assessments. The study shows a significant positive impact of this involvement on the likelihood of companies considering divestments. The involvement of the board also speeds up the implementation process.

- Working on reinvestment plans in parallel. Identifying other avenues to be pursued once existing projects are closed helps to counter the status quo bias, by making the opportunity cost visible.

Source:  The power of portfolio renewal and the value in divestitures, PwC US, March 2023.

Currently, most companies are pondering how they can best take advantage of AI at their own scale. Applications and experiments are thus flourishing, with varying degrees of success. Very often, the frustrations are commensurate with the hopes. And with good reason: AI, however "intelligent" it may be, can ultimately only do one thing—work from the data we provide it with. To capitalize on it, we therefore need centralized data of sufficient quality and quantity, and derived from a wide range of sources. In many organizations, however, this data is scattered among various business functions, each of which has its own systems.

So, before considering sophisticated generative AI set-ups, it is useful to carry out a quick diagnosis of your organization. Is tacit knowledge sufficiently formalized? Is it centralized? Are data collection and processing methods sufficiently standardized? Given the nature and quantity of the data collected, is there a risk of triggering biased responses from your AI system? Would you benefit from access to additional sources? This upstream work is essential to ensuring the quality of the AI's responses and maximizing its potential to help decision-making.

Source:  Harnessing AI to accelerate digital transformation, The Choice by ESCP, July 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.

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.