Kelly Knight
Latest from Kelly Knight
Everpure and Nvidia target the AI-ready data gap
Enterprises have poured billions into AI infrastructure — graphics processing units, cloud capacity and model tooling — yet most deployments remain mired in experimentation rather than generating measurable business value. The bottleneck is not compute. It is AI-ready data. The gap between owning data and having AI-ready data is proving to be the defining obstacle ...
Data primacy becomes central to enterprise AI: theCUBE’s Pure Accelerate 2026 keynote analysis
As AI transforms the enterprise, the old model of managing data in application-controlled silos is breaking down. The companies that will win in the AI era are those that embrace data primacy by treating data, rather than the applications sitting on top of it, as the primary asset. That shift was on full display at ...
Kion ties automated governance to FinOps’ next phase as AI spend spreads
Artificial intelligence is creating a new class of cloud cost challenge — one that no longer belongs exclusively to engineering teams. As AI tools put spend capabilities into the hands of sales, finance and executive teams, the need for automated governance has become as urgent as the innovation it is meant to protect. The FinOps ...
FinOps adapts to AI spend as token economics reshape enterprise budgets
As generative AI accelerates from a product experiment into a core enterprise operating cost, FinOps is evolving rapidly to manage AI spend, introducing a layer of complexity that traditional cloud budgets have never fully prepared practitioners to handle. Token economics are forcing organizations to rethink not just how they measure spend, but what costs even count ...
Microsoft positions the intelligence layer as the control plane for enterprise AI
As enterprises move beyond AI experimentation into full-scale production, the central challenge has shifted from accessing models to managing the organizational context they need to act reliably. The pressure to govern costs, secure data and maintain accountability is now redefining how companies architect their entire AI intelligence layer. That convergence of AI adoption and infrastructure ...
Tokenomics emerges as the new discipline for managing AI’s runaway cost frontier
AI spending is outpacing every budgeting model enterprise finance teams have built, and the gap between what tokens cost on paper and what organizations actually owe is becoming an operational crisis. As FinOps evolves into a boardroom strategy, the discipline is now being forced to reckon with a fundamentally new unit of technology referred to ...
Walmart turns to FOCUS to simplify multi-cloud and AI cost accountability
As artificial intelligence drives a new wave of token-based spend across cloud and on-premises environments, enterprises are discovering that understanding what they actually owe — and to whom — is harder than ever. The FinOps FOCUS specification is emerging as the open standard that provides practitioners with a shared data language to cut through multi-provider ...
Vibe coding moves from weekend hobby to enterprise change management mandate
AI is rewriting how software gets built inside large enterprises — not just at the tooling level, but at the levels of culture, governance and organizational change. The rise of vibe coding, where developers describe intent in natural language and let AI generate working code, is collapsing development cycle times and forcing leadership to rethink ...
Exposure management evolves from vulnerability scanning to full-stack AI defense
AI has fundamentally broken the economics of cybersecurity and exposure management, compressing exploit windows from days to minutes and forcing organizations to rethink how they inventory, prioritize and remediate risk across an attack surface that now includes cloud infrastructure, identities and AI workloads themselves. The traditional approach — periodic scanning followed by manual patching cycles ...
Open data architecture powers DoorDash’s real-time logistics and agentic AI ambitions
As enterprises race to support machine learning, agentic workflows and analytics on the same infrastructure, open data architecture has emerged as the dividing line between platforms that scale and those that stall. At petabyte scale, logistics platforms can’t afford a monolithic data stack. DoorDash Inc. — one of the world’s largest real-time logistics companies — ...