On May 15, 2025, during the Bouygues Innovation Morning: How is AI impacting Robots?, we shared our vision and presented Humanoids for Industry, our program dedicated to supporting companies in their first use cases for humanoids starting in 2025. Thank you to the Bouygues group for the invitation and for making this event a success.
Feel free to download the presentation and watch the video included.
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Le 15 mai 2025, lors de la Matinée de l’Innovation Bouygues: How is AI impacting Robots?, nous avons partagé notre vision et présenté Humanoids for Industry, notre programme dédié à l’accompagnement des entreprises dans leurs premiers cas d’usage des humanoïdes dès 2025.Merci au groupe Bouygues pour l’invitation et la réussite de cet événement.
N'hésitez pas à télécharger la présentation et regarder la vidéo incluse.
The success of adopting generative AI in business lies at least as much in training employees on a large scale as in solving the technical challenges.
This white paper is the result of a major survey that we carried out with key AI decision-makers from French institutions and companies, mainly including large groups, in order to draw up a precise and realistic picture of the adoption of generative AI within organizations, delivering concrete examples.
AI is projected to enhance HR productivity by 30 to 40%, catalyzing its strategic evolution within the company's framework.
If deep learning will definitely play a crucial role in automation and improvement of the daily insurer operations involving image, text, and audio processing, it is not expected in the mid-term to deeply impact the core insurer business related to risk management.
The aim of this short paper is to share a point of view on the use of deep learning technologies in the insurance sector.
An ever-increasing digitalization of industrial activities, ever more voluminous data generated, a potential of business value still under the radar of most decision-makers, …
Nothing new about AI, you’d think.
Well, think again!
Update on the challenges and opportunities of an industrial world renewed by AI
It is time to acknowledge the challenges to delivering significant impact from the use of AI in manufacturing industries.
One of the main causes derives from a prevalent mistake: AI & data tools have not been effectively conceived, neither marketed, as services for operators in factories, resulting in poor adoption and in worst cases in mistrust behaviors. In factories, humans will be playing a core role for a while, and their “customer” needs and “customer” experience have to be carefully addressed and marketed in the plant when designing AI & data tools. This is a key success factor to massive adoption of AI solutions in the coming years.This short paper is based on Emerton Data research and analysis, and on numerous interviews with industry experts and solutions providers.
It provides an overview of promising AI use cases and solutions for manufacturing, and detailed hints to unlock the value potential.
An increasing reliance on Artificial Intelligence for decision making is driving financial institutions, regulators, and supervisors towards a clarification of sources and control of risks. These risks were either already present (but marginal) or even non-existent in the usual model risk management framework. In a context where the use of machine learning is becoming massive and industrialized across banks and insurance companies, problematics such as interpretability and dynamic monitoring, robustness, ethics, bias and fairness require a specific attention.
Although all these topics are becoming active academic research topics and business innovation fields, their rigorous analysis from the model risk point of view remains at its early stage. A close collaboration between academics, regulator experts and private sector professionals can accelerate finding pragmatic answers to multiple important questions, e.g. how to interpret outputs of black-box models? How to monitor machine learning models in time? When and why do they deviate? How to control the discrimination incurred by the algorithms? How to prevent the effects on decisions of input data changes or data falsification?
This short paper is based on Emerton Data research and analysis and provides an introduction to the newly raised problematics of machine learning risks and ethics, with a focus on insurance and more generally on financial services, probably the most mature sectors, even if these problematics will soon affect all industries.
This paper introduces X-SHAP, a model-agnostic method that assesses multiplicative contributions of variables for both local and global predictions.
This methodtheoretically and operationally extends the so-called additive SHAP approach. Itproves useful underlying multiplicative interactions of factors, typically arisingin sectors where Generalized Linear Models are traditionally used, such as ininsurance or biology.
We test the method on various datasets and propose a set oftechniques based on individual X-SHAP contributions to build aggregated multiplicative contributions and to capture multiplicative feature importance, that wecompare to traditional techniques.
It is commonly acknowledged that healthcare data has tremendous potential as a tool to improve overall public health and unlock significant innovation in both the public and private healthcare sectors. Use cases based on the sharing and processing of healthcare data (e.g., machine learning, AI, etc.) are numerous.
