The Artefact Research Center's mission is to foster an innovation ecosystem around AI and data by encouraging collaboration between university professors and representatives of international partner companies. The goal: cutting-edge research driven by data and the industrial applications of its partner companies.
Discover how Agentic AI is driving practical, strategic advantages for digital and data marketers. From autonomous campaign optimization to personalized customer experiences, this technology empowers your team to focus on high-impact innovation.
Vincent Luciani, co-founder and Executive Chairman of Artefact, emphasizes that artificial intelligence (AI) must be concretely integrated into a company's collective processes, if it is to deliver real productivity gains. The entrepreneur will be taking part in the MKIA, an event organized by Maddyness and dedicated to AI, at the Salle Gaveau in Paris, on Tuesday April 29.
No strategy is effective without measurement! Choosing projects, evaluating the performance of AI agents or the hallucinations of the machine: detailed explanations of the governance you need to put in place to stand out from the crowd thanks to artificial intelligence.
In July 2024, Robin was promoted to CTO of Artefact France for his deep technology expertise and management skills. He now leads a large team of data scientists, software engineers and frontend developers, ensuring that his team is at the forefront of technology, with training and curiosity at the heart of his approach.
AI evoluciona rápidamente hacia un futuro en el que los usuarios ya no piensen en modelos, modos o herramientas. En su lugar, los sistemas se están volviendo dinámicamente adaptativos: ajustan automáticamente la profundidad del razonamiento, seleccionan las herramientas adecuadas y gestionan los recursos en tiempo real en función de la complejidad de la tarea.
As AI agents increasingly handle tasks like email drafting or resolving customer service issues, one challenge becomes clear: how can these agents communicate effectively - not only with humans but also with the software systems they depend on?
El auge de los agentes de AI en las empresas se desarrolla a dos niveles: la mejora de la productividad individual mediante agentes de tareas y la redefinición de los flujos de trabajo colectivos a través de agentes de flujos de trabajo. Aunque estas innovaciones prometen ganancias de eficiencia, también introducen retos estructurales. Sin una estrategia bien orquestada, las organizaciones corren el riesgo de una proliferación incontrolada de agentes y dependencias operativas críticas.
A medida que los agentes de AI se hacen cargo de tareas como la redacción de correos electrónicos o la resolución de problemas de atención al cliente, nos enfrentamos a un reto fundamental: ¿cómo se comunican estos agentes de forma eficaz, no solo con los humanos, sino también con los ecosistemas de software de los que dependen?
