Boardrooms buzzed about generative breakthroughs, yet a colder reality surfaced as a new survey found that the majority of enterprises still cannot move data freely enough to feed the very models they hope will transform the business. That tension between ambition and access set the stakes: growth
For the first time at true hyperscale, a social platform’s AI backbone is being rebuilt around commodity-efficient Arm CPUs to orchestrate billions of agentic interactions while GPUs and custom accelerators focus on raw parallel compute. Meta’s agreement to deploy tens of millions of AWS Graviton5
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
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
Chloe Maraina sits at the intersection of logic and visualization, possessing a rare ability to transform cold, raw data into narratives that drive high-level business strategy. As a Business Intelligence expert with a deep background in data science, she has spent her career watching the evolution
The ability to harness data scattered across a tapestry of edge devices, private data centers, and public clouds has become the definitive marker of success for the modern enterprise. As organizations grapple with the explosion of unstructured file data and the demands of AI-driven workloads, the
