Viele Marketingverantwortliche stehen vor demselben Paradoxon: Trotz erheblicher Investitionen in generative KI stellen sich die erwarteten Auswirkungen auf Kosten, Geschwindigkeit und Leistung nicht ein. Warum? Weil die meisten KI-Systeme nur “denken”, aber nicht handeln. Agente-basierte KI ändert das.
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.
Als OpenAI mit „Atlas“ seinen neuen, auf ChatGPT basierenden Browser vorstellte, brachte das Unternehmen nicht einfach nur ein weiteres Produkt auf den Markt. Es öffnete eine Tür, die in die nächste Ära der Mensch-Computer-Interaktion und damit zwangsläufig auch in eine neue Dimension der Werbung führt.
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.
Einzelhändler agieren in einem wettbewerbsintensiven und dynamischen Umfeld und stehen vor Herausforderungen, die von Preissensibilität bis hin zu komplexer Logistik reichen. Um erfolgreich zu sein, müssen sie innovative Lösungen einsetzen, die ihre Abläufe optimieren und die Kundenzufriedenheit steigern. Dieser Artikel untersucht, wie AI-Agenten – autonome Systeme, die beobachten, Schlussfolgerungen ziehen und handeln – den Einzelhandel neu definieren und die Branche auf die nächste Stufe der Wettbewerbsfähigkeit bringen.
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.
