On 5 July 2018, Émile Servan-Schreiber reshared GS1 France’s invitation to its thirteenth summer university, planned for 31 August in Paris. The organiser presented shared knowledge as a source of competitiveness and asked whether value creation could rest on a common good.
GS1’s invitation put Servan-Schreiber alongside Jean-Luc Abelin, Martin Duval, Gilles Cavalli and Ollivier Dyens. It asked practical questions about why organisations share knowledge, how such projects work and what benefits they might bring. Servan-Schreiber added no separate commentary to the share.
From the invitation to the talk
A later account by Visionary Marketing, published on 16 October 2018, reports that the event took place on 31 August and describes Servan-Schreiber’s presentation. The publisher also provides an audio recording labelled as his GS1 conference talk. This recovers a connection with forecasting that the invitation alone did not explain.
The recap attributes four organising ingredients to him: different perspectives, independence of thought, information gathered on the ground, and objective aggregation. Its account stresses that connecting more people is only part of the task; the collective also needs an organisation that makes its contributions useful.
It then links these ingredients with forecasting and innovation in companies. Sales and new products appear as examples, with particular attention to situations where historical data are scarce. The argument is that dispersed human knowledge can inform a judgement before a structured dataset is available.
Shared knowledge needs a decision method
The recap presents forecasting as a way to use observations dispersed across an organisation. Historical datasets describe what has already been recorded; employees and partners may also notice changes that are difficult to capture in a table. The reported talk connected this practical knowledge with the need for independent contributions and objective aggregation.
For forecasting, the useful distinction is between making information available and turning it into an assessment. A shared repository can preserve what people know; a forecasting process still needs a question, independent contributions and a way to combine them. Sharing observations creates the material for a forecast; the organising rules determine how those observations become a collective answer.
Our earlier account of prediction markets in the foresight toolbox develops the aggregation side. The GS1 appearance brings that question into a broader discussion of knowledge sharing: how an organisation can convert its many perspectives into a judgement it can use.
