Victoria Gayton
Latest from Victoria Gayton
The platform that ate the pipeline: Vast Data’s rethink of AI infrastructure
The enterprise data stack wasn’t designed for continuous, autonomous agentic AI. For years, the challenge was storing and organizing information. Now the challenge is delivering that data — consistently, globally and in real time — to systems that reason and act without pause. Most infrastructure was built for batch analytics and discrete workflows, not always-on ...
What to expect during the AI Trust & Cyber Resiliency Summit: Join theCUBE March 10
AI trust increasingly determines whether enterprise AI scales. As organizations move beyond pilots and into operational systems, the question is no longer whether models perform well in isolation, but whether the infrastructure beneath them can withstand cyber risk, data integrity failures and real-world disruption. AI adoption continues to outpace data, identity and security readiness. That ...
What to expect during Vast Forward: Join theCUBE Feb. 25
The AI arms race has centered on compute: Who has the most graphics processing units, the fastest chips and the biggest clusters? But a different pressure point is emerging as enterprises move AI from pilot programs into continuous, production-grade operations. Vast Data Inc. has built its strategy around that gap, offering a data infrastructure platform ...
Escaping the pilot trap: How composable AI data platforms move enterprise AI to production
Enterprise artificial intelligence has a completion problem. Even with strong models, ambition and executive support, most initiatives fail when implemented in real operations. The persistent gap between a promising proof of concept and a production system that delivers repeatable business value has earned its own shorthand: The pilot trap. Avoiding it requires an AI data ...
Dell’s approach to the data architecture problem standing between AI pilots and production
Generative artificial intelligence is no longer an experiment tucked away in innovation labs. Enterprises now push toward production-scale systems that can reason, retrieve and act across vast data estates. The shift exposes the fact that existing data architectures must evolve to meet the demands of today’s AI imperative. Gen AI fabrics are emerging as the ...
Enterprise AI adoption, demystified: What enterprises learned building with Google Cloud
Enterprise AI didn’t slow because the technology wasn’t ready. It slowed because people weren’t sure what to trust, what to learn or where to begin. Over the past year, conversations with Google Cloud leaders and industry experts revealed a consistent pattern: Enterprise AI adoption advances when confidence replaces complexity. That pattern became clear over the ...
How Google Cloud is shaping the enterprise AI inference moment
Enterprise technology investment continues to accelerate, but the friction point has shifted. The hard part is no longer training models or selecting architectures. It’s getting those models into production, keeping them responsive under real-world conditions and proving they deliver value once they’re live. AI inference is where initiatives either prove their value or grind to ...
AI agents face a widening trust gap, theCUBE Research finds
AI agents are fast becoming the defining force behind the enterprise shift from simple automation to true decision intelligence. If the first satisfactory phase of enterprise artificial intelligence was about automation, the next is clearly about augmentation: enhancing human intelligence in knowledge work. TheCUBE Research’s “Agentic AI Futures Index” shows that shift accelerating. Sixty-two percent ...
Three insights you may have missed from theCUBE’s coverage of AWS re:Invent
Something that’s been building for several years has finally come into clearer view as enterprises shift their attention from model performance to the systems that bring intelligence into production. The conversation now centers on how agentic AI fits into real workloads, reshaping expectations for automation, performance and the work developers can hand off to software. ...
Union.ai on the rise of experimental AI development infrastructure in the enterprise
Building software has long followed a reliable playbook: Write code, test, deploy and iterate. But when deterministic algorithms give way to artificial intelligence models that learn and adapt, the old playbook falls apart. AI development infrastructure has become essential for enterprises trying to move projects from prototype to production. The shift stems from a fundamental ...