Enterprise AI is moving fast — from simple chatbots and proof-of-concept demos to autonomous agents, RAG applications, and production-grade inference workflows. But as AI gets more capable, it is also putting an entirely new kind of pressure on the data layer.
For years, the promise of cloud migration has centered on agility, scalability, and cost efficiency. Enterprises were told they could move faster, modernize infrastructure, and unlock new capabilities simply by moving workloads out of the data center and into the public cloud.
This week in Copenhagen, Silk had the opportunity to join SimCorp Global Summit — an event that brings together some of the most forward-thinking leaders in investment management technology.
At ViVE, one theme was everywhere: AI.
AI is no longer confined to innovation labs or experimental chatbots. It’s showing up everywhere: in financial applications, healthcare systems, supply chains, retail platforms, customer service workflows, and the core operational systems that run modern enterprises. And as AI becomes embedded into everyday business processes, one shift is becoming impossible to ignore:
Cloud storage has come a long way. What began as a simple replacement for on-premises disks has evolved into a sprawling ecosystem of tiers, performance classes, snapshots, APIs, and data services. Yet despite all this progress, enterprises still face a familiar set of challenges: inconsistent performance, rising costs, operational complexity, and growing data sprawl.
In the fast-evolving healthcare landscape, innovation is not just an option – it’s a necessity. Sentara Health, a leading healthcare provider with 12 hospitals and over 300 care sites, is at the forefront of this innovation by integrating artificial intelligence (AI) into its care delivery processes. Guided by a commitment to patient-centric care, Sentara is revolutionizing how data is used to empower clinicians, enhance operations, and provide better outcomes for patients. At the core of this transformation is Sentara’s partnership with Silk, a Virtual Storage Area Network (Virtual SAN) that is purpose-built for the cloud.
Microsoft Ignite made one thing unmistakably clear: we’re entering the era of the AI-first enterprise. Every product, every workload, and every piece of infrastructure is being reshaped around AI acceleration. Whether you’re building customer-facing applications, running mission-critical databases, or storing decades of historical data, the expectations around speed, flexibility, and scale have completely changed.
Multi-cloud has moved far beyond buzzword status. Today, enterprises are intentionally spreading workloads across multiple cloud providers to improve resilience, increase flexibility, avoid vendor lock-in, and tap into best-of-breed services.
For many database teams, the cloud promised limitless scalability and performance on demand. But in practice, it often delivers something far more frustrating: bottlenecks, inconsistent throughput, and mounting costs just to maintain acceptable SLAs.