Enterprise Data Management

Boardrooms wanted measurable AI impact yesterday, yet risk disclosures kept piling up as exposure widened from datasets and pipelines to model behavior and semi-autonomous agents that act without clear oversight or context. That friction showed up in the numbers: public AI-risk disclosures jumped

The moment agents stopped asking for dashboards and started filing tickets, shipping code, and adjusting prices, the quiet plumbing of data platforms became the frontline that decided whether automation saved money or broke production. Enterprises that once tolerated stale extracts and fragmented

Richard Lavaile sits down with Chloe Maraina, a business intelligence leader who turns big data into crisp narratives that executives can act on. With a front-row view of AI’s accelerating impact on leadership, operating models, and market structure, Chloe dissects the week’s pivotal moves—from

Market signals have grown so dense and fast-moving that any delay, mismatch, or silent error in data can ripple through models, distort risk views, and chip away at returns long before performance attribution catches the miss. That is the context in which OLZ AG, a quantitative asset and wealth

Vessel operators have spent years wrestling with technical manuals that look complete on paper yet stall the very processes they are supposed to enable, because unstructured PDFs and mismatched codes choke the path from documentation to maintenance and procurement. That bottleneck has defined the

Boardrooms did not debate whether agentic AI would arrive so much as how fast it could move from lab demos to dependable systems that run the business, and this event answered with a blueprint that fused research, infrastructure, and enterprise guardrails into one production posture. The headline