Veel marketingleiders hebben te maken met dezelfde paradox: ondanks grote investeringen in Generative AI blijft de verwachte impact op kosten, snelheid en prestaties uit. Waarom? Omdat de meeste AI alleen “denkt”, maar niet handelt. Agentic AI verandert dat.
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.
Toen OpenAI Atlas aankondigde, zijn nieuwe ChatGPT-browser, lanceerde het niet zomaar een product. Het opende een deur die leidt naar het volgende tijdperk van interactie tussen mens en computer en, onvermijdelijk, naar een nieuwe grens voor reclame.
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.
Detailhandelaren opereren in een concurrerende en dynamische omgeving en worden geconfronteerd met uitdagingen die variëren van prijsgevoeligheid tot complexe logistiek. Om te kunnen gedijen, moeten ze innovatieve oplossingen aannemen die hun activiteiten stroomlijnen en de klanttevredenheid verhogen. Dit artikel onderzoekt hoe AI agenten - autonome systemen die observeren, redeneren en handelen - de detailhandel herdefiniëren en de sector positioneren voor het volgende concurrentieniveau.
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.
