Why AI Chooses Your Brand: Demystifying How AI Discovery and Digital Buyer Journeys Work
AI discovery and AEO are reshaping how B2B buyers find and trust brands — here’s how to ensure yours shows up in AI search. As generative AI assistants like ChatGPT, Claude, Gemini, Grok, and Perplexity replace traditional search, brand visibility now depends on how large language models (LLMs) learn, rank, and recommend. Together, they unpack a 19-attribute framework across four categories that explain how LLMs discover, learn, and select brands to include in AI-generated answers.
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Dell AI Data Platform: Building the Foundation for Agentic AI
Dell Technologies’ enhanced AI Data Platform establishes a unified foundation for the era of agentic AI and AI factories. Combining PowerScale and ObjectScale storage with Elastic for vector search and Starburst for federated analytics, Dell delivers a composable, hybrid data architecture that bridges cloud, core, and edge environments. New integrations like MetadataIQ and the PowerScale RAG Connector make unstructured data actionable for retrieval-augmented generation and generative AI workloads. Built on open standards such as Apache Iceberg and designed for interoperability with NVIDIA’s AI Factory ecosystem, Dell’s platform enables enterprises to operationalize AI faster, reduce data silos, and extract measurable business value—turning data infrastructure into the backbone of intelligent, hybrid enterprise systems.
Turning AI Into Performance With DevOps Discipline
The 2025 DORA report reveals how pairing AI with DevOps discipline drives software performance. Learn how small-batch delivery, version control, and adaptive governance transform AI from hype into measurable engineering outcomes.
Accelerated Computing and the Evolution of Service as Software
Accelerated computing, platform engineering, and DevSecOps are converging to reshape how software is built, secured, and delivered.
296 | Breaking Analysis | AI Factories – Data Centers of the Future
The data center as we know it is being re-imagined as an “AI factory” – a power- and data-optimized plant that turns energy and information into intelligence at industrial scale. The tech stack is flipping from general purpose CPU-centric systems to GPU-centric accelerated compute, optimized for parallel operations; and purpose built for AI. Network fabrics are most critical to this transition with supporting elements such as disaggregated storage, governed data planes, and a shift from app-centric operations to an agentic control plane that orchestrates models, tools, and process workflows. Simply put, investments in general-purpose computing are rapidly shifting to extreme parallelism, scale-up/scale-out/scale-across networking, and automated governance built for AI throughput
Special Breaking Analysis: The Hidden Fault Domain in AWS — Understanding Control Planes and Availability Zones
The AWS outage on October 20–21, 2025 in the early morning ET hours exposed a systemic vulnerability whereby even pristine multi-AZ designs don’t protect you from a shared control plane and DNS path failure. Our understanding is that the incident is a function of an architectural coupling to US-EAST-1 for identity, service discovery, and API orchestration. Specifically, while availability zones do exactly what they were designed to do – i.e. absorb intra-region physical failures – they don’t defend against logical, cross-AZ control-plane dependencies. Enterprises should not equate multi-AZ with business resilience as and must reframe their thinking around fault-domain isolation across regions (and, where justified, clouds), with clear strategies for DNS, identity, and service-discovery dependence.
Eliminating Setup Debt and Governing AI Workflows
Developer setup debt is slowing innovation and increasing risk. Discover how governed, ephemeral environments—and platforms like Coder—are transforming AI development through reproducibility, compliance, and speed at scale.
Special Breaking Analysis: IBM’s Technology-Arbitrage Play – Turning Agentic AI and Durable Platforms into Enterprise Outcomes
We believe IBM is attempting one of the more underappreciated pivots in enterprise AI. Specifically, we’re talking about a shift from traditional labor-arbitrage services to technology arbitrage, where value is created by packaging agents, durable platforms, and domain IP into repeatable outcomes. The story isn’t that “IBM has AI.” Everyone has AI. IBM’s promise is to deliver outcomes at the workflow level across regulated, complex environments. In particular, situations where time-to-value, policy, and sovereign constraints dominate buying decisions. Our assessment based on recent conversations with the company and several of its customers indicates this messaging aligns with enterprise priorities within demanding industries. IBM is focusing on mission-critical workflows, blending deterministic systems of record with probabilistic systems of intelligence.
Dreamforce 2025 Developer Impact and the Rise of the Agentic Enterprise
Explore how Dreamforce 2025 redefines developer roles with AI, low-code, and the Agentic Enterprise vision.