As AI moves from hype to measurable results, one truth is becoming clear: Enterprise AI needs business context to be fully trusted.
Most enterprise data lives outside databases. Here's why that's holding AI back — and how connecting context can change it.
The shift to service as software will bring learning curve advantages, software-like marginal economics, and winner-take-most dynamics to all companies across every industry, not just tech vendors. We ...
For enterprises, proprietary data is a source of competitive advantage. Take these four steps to ready it for AI-powered applications and agents. When Miqdad Jaffer, product lead at OpenAI, challenged ...
Salesforce Inc. is expanding its artificial intelligence platform with new data management and governance capabilities, aiming to address what the company says is a crisis in enterprise AI adoption ...
NEW YORK, NY, August 12, 2025 (EZ Newswire) -- CHEQ, opens new tab today announced the launch of Traffic Intelligence in CHEQ Manage, the industry's first tag manager-native solution to give ...
HPE will support new NVIDIA AI Data Platform, advancing business insights through AI and data HPE Alletra Storage MP X10000 speeds data pipelines with built-in intelligence HPE Alletra Storage MP ...
Today's enterprises need unified semantic layers that seamlessly connect traditional BI, GenBI applications, and emerging AI workloads. As organizations face challenges in making enterprise data truly ...
Modern vision-language models allow documents to be transformed into structured, computable representations rather than lossy text blobs.
Modernization, in many ways, is often synonymous with centralization. Creating a unified framework from which all enterprise systems derive ensures frictionless operations through a sense of ...
When data is productized and semantics encoded, you unlock agentic AI at scale. Instead of assistants, you get executors: ...
A practical overview of security architectures, threat models, and controls for protecting proprietary enterprise data in retrieval-augmented generation (RAG) systems.
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