AI Success Depends on These Data Governance Metrics

Anthropic's rapid revenue growth and profitability highlight the transformative potential and competitive dynamics in the enterprise AI sector. The post Anthropic expects 130% revenue surge to $10.9B in June quarter appeared first on Crypto Briefing.

Day two of TechEx North America has been more of a deeper, critical examination of AI in the enterprise, but with a optimistic bent. The AI and Big Data programme opened with reference to what was termed the “AI graveyard” – that is, AI projects that seem to perform well in pilot, but don’t seem […] The post Enterprise AI roadblocks and roadmaps, security and physical AI: Day two at TechEx appeared first on AI News.

A December 2025 paper from Silicon Valley venture capital firm Foundation Capital, titled “AI’s trillion-dollar opportunity,” has generated significant excitement in the enterprise AI industry. The reason? It introduces the new concept of a “context graph,” a knowledge graph designed to capture a new AI paradigm known as “decision traces.” The context graph is emerging as a potentially powerful idea. The context graph approach could capture the full context, reasoning, and causal relationships behind critical business decisions, making it a highly practical concept. As the paper notes, “Agents don’t simply need rules; they need access to the decision traces that show how rules were applied in the past, where exceptions were granted, how conflicts were resolved, who approved what, and which precedents actually govern reality.” This point is echoed by some of the commentary on the prediction, which points out that the most important knowledge comes from the data about the decisions that

A December 2025 paper from Silicon Valley venture capital firm Foundation Capital, titled “AI’s trillion-dollar opportunity,” has generated significant excitement in the enterprise AI industry. The reason? It introduces the new concept of a “context graph,” a knowledge graph designed to capture a new AI paradigm known as “decision traces.” The context graph is emerging as a potentially powerful idea. The context graph approach could capture the full context, reasoning, and causal relationships behind critical business decisions, making it a highly practical concept. As the paper notes, “Agents don’t simply need rules; they need access to the decision traces that show how rules were applied in the past, where exceptions were granted, how conflicts were resolved, who approved what, and which precedents actually govern reality.” This point is echoed by some of the commentary on the prediction, which points out that the most important knowledge comes from the data about the decisions that

Redis' Iris could accelerate enterprise AI adoption by bridging data retrieval gaps, enhancing efficiency, and reducing infrastructure costs. The post Redis launches Iris, a context and memory platform for AI agents appeared first on Crypto Briefing.

95% of enterprise AI pilots fail to launch. Why? The post Why Your AI Demo Will Die in Production appeared first on Towards Data Science.

SAN FRANCISCO, May 14, 2026 — Graphon AI emerged from stealth today with $8.3 million in seed funding to build a new class of AI infrastructure: a pre-model intelligence layer that […] The post Graphon AI Emerges from Stealth with $8.3M to Build ‘Pre-Model’ Intelligence Layer for Enterprise AI appeared first on AIwire.

Enterprise AI systems are entering a phase where inference design matters as much as model capability itself. The post The Next AI Bottleneck Isn’t the Model: It’s the Inference System appeared first on Towards Data Science.