Many marketing leaders face the same paradox: despite major investments in Generative AI, the expected impact on costs, speed, and performance isn’t materializing. Why? Because most AI only “thinks”, it doesn’t act. Agentic AI changes that.
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.
When OpenAI announced Atlas, its new ChatGPT-powered browser, it didn’t just launch another product. It opened a door, one that leads into the next era of human-computer interaction, and, inevitably, into a new frontier for advertising.
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.
Retailers operate in a competitive and dynamic environment, facing challenges from price sensitivity to complex logistics. To thrive, they must adopt innovative solutions that streamline operations and enhance customer satisfaction. This article explores how AI agents — autonomous systems that observe, reason, and act — are redefining retail and positioning the industry for its next level of competitiveness.
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.
