De nombreux responsables marketing sont confrontés au même paradoxe : malgré des investissements considérables dans l'IA générative, les retombées attendues en termes de coûts, de rapidité et de performances ne se concrétisent pas. Pourquoi ? Parce que la plupart des IA se contentent de “ réfléchir ”, sans agir. L'IA agentique change la donne.
For the last 18 months, 'Generative AI' has dominated every C-level strategy session. But what if that's already the old conversation? In the energy and industry sectors, the discussion is rapidly shifting from GenAI to Agentic AI. This isn't just an incremental update; it's a new paradigm, one that moves from simply augmenting tasks to completely reinventing core industrial processes.
Lorsque OpenAI a annoncé Atlas, son nouveau navigateur basé sur ChatGPT, l'entreprise n'a pas simplement lancé un nouveau produit. Elle a ouvert une porte qui mène à la prochaine ère de l'interaction homme-machine et, inévitablement, à de nouveaux horizons pour la publicité.
Most companies are not ready to replace a dashboard era data stack with an AI stack. Salesforce's latest State of Data & Analytics indicates that 84% of data and analytics leaders say their strategies require a complete overhaul before AI ambitions can succeed. Leaders estimate that 26% of their data is untrustworthy, only 43% report formal data governance frameworks, and around 50% are not confident in their ability to generate and deliver timely insights. At the same time 70% believe the most valuable insights are locked in unstructured data. The conclusion is straightforward: the obstacle is not enthusiasm but foundation, and that foundation must change before agentic systems can scale.
Les commerçants évoluent dans un environnement concurrentiel et dynamique, confrontés à des défis allant de la sensibilité aux prix à la complexité logistique. Pour prospérer, ils doivent adopter des solutions innovantes qui rationalisent leurs opérations et améliorent la satisfaction client. Cet article examine comment les agents AI — des systèmes autonomes capables d'observer, de raisonner et d'agir — redéfinissent le commerce de détail et préparent le secteur à franchir une nouvelle étape en matière de compétitivité.
For two decades, Master Data Management (MDM) gave organizations a single source of truth for customers, products, and suppliers so analytics and operations could run on clean, governed data.
When a leading global commodity trading company sought to accelerate its AI transformation, it turned to Artefact to turn potential into measurable performance. The goal was to make generative AI a daily productivity tool across departments while ensuring robust governance, compliance, and lasting cultural change.
Every decade or so, the way companies organize themselves quietly changes – not through grand announcements, but through a series of accumulated decisions that suddenly reveal a new pattern. We saw it with digital transformation in the 2000s, when data and connectivity became the fabric of the modern enterprise. Then came the agile revolution, which redefined how teams collaborate, iterate, and deliver value. Each shift didn’t just alter tools or processes—it changed what we believed possible about work itself.
At Artefact, we have designed a proven methodology to help corporations move from exploration to sustainable impact. This article explains the journey, the challenges, and the pragmatic steps to success.
