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The problems that arise when AI scales up

There is one thing that most AI implementations that fail in business have in common. They don't fail because they don't work, or even because they don't add value, but when they cease to be tolerable within the existing framework. Scaling up acts less as an accelerator than as a revealer. What was acceptable as long as the use remained low-profile can become fragile when it becomes more exposed and changes scale. What was acceptable as long as it remained reversible and concerned only a small scope with controlled costs and therefore...

The HiPPO principle: when opinion replaces decision-making

We thought we had replaced the power of the boss with highly rational approaches, particularly those based on data. A century of management science,...

Augmented governance: AI as a lever for collective lucidity

As the years go by, corporate governance finds itself caught between an increasingly complex and rapidly changing environment and the structural limitations of its operating model. On...

Does AI spell the end of collective intelligence?

The massive arrival of AI in businesses, with the underlying promise of replacing individuals, is reviving discussions on collective intelligence. After all, what is the...

How can you avoid becoming a bottleneck in the workplace?

In the world before bottlenecks were machines that could not keep up with the pace of other machines and completely blocked the production process. The result:...