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Will the Future of Agentic AI rely on Knowledge Graphs?

As enterprises rush to operationalize AI, most discover that their data infrastructure was never designed for autonomous reasoning. Today, up to 80% of AI implementation time is spent on data wrangling and schema alignment, a symptom of infrastructures built for storage, not understanding. Without a foundation that captures relationships and meaning, agents will remain powerful, but blind. With AI agents becoming active participants in enterprise workflows, the nature and scale of data querying are evolving.

Agentic AI Will Transform Your Marketing Department’s Capabilities

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.

Shaping AI strategy in Energy & Industry

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.

AI-Controlled Ads: The Future of Advertising

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.

Data Platforms for the Agentic Era

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.

Revolutionizing Retail with AI Agents – Is the Industry Ready for It?

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.

Master Context Management: The Evolution Beyond Master Data Management

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.

Case Study: Accelerating GenAI adoption in commodity trading

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.

Hybrid Agentic Organizations: The Next Operating System for the Enterprise

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.