AI & Machine Learning

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The CEO-CIO Compact for Agentic AI: Who Owns Risk, Spend, and Business Outcomes?

TL;DR Agentic AI creates an accountability problem before it creates a technology problem. An AI agent can interpret goals, retrieve data, select tools, spend money, initiate workflows, and change business or technical systems. That authority cannot be assigned to an innovation committee, hidden inside a platform team, or treated as a normal software feature. The […]

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Choosing an LLM for Enterprise RAG: Retrieval Fit Beats Model Hype

TL;DR The best LLM for enterprise RAG is not automatically the largest or newest model. The right model is the one that works with your retrieval design, citation expectations, latency target, cost profile, data controls, and evaluation requirements. Model selection should happen after source quality, access control, retrieval behavior, and test questions are understood. Why

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On-Prem Private AI Series: Dell AI Factory with NVIDIA and Red Hat OpenShift AI as the AI Factory Build Pattern

TL;DR Dell AI Factory with NVIDIA and Red Hat OpenShift AI is the private AI option for organizations that want a validated infrastructure and platform stack instead of building every AI layer themselves. Compared with the VMware approach in the first article, Dell’s center of gravity is less about extending an existing private cloud operating

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Why Platform Engineering Is Becoming a CEO-Level Productivity Strategy

TL;DR Platform engineering is becoming a CEO-level productivity strategy because software delivery is now a direct constraint on revenue, customer experience, operational change, regulatory response, and AI adoption. A well-designed internal developer platform reduces repeated engineering work, shortens delivery queues, embeds security and reliability controls, and gives product teams a supported path from idea to

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