Initializing SOI
Expert insights on RAG implementation, knowledge graphs, and AI productization for regulated industries
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Еmbeddings aren't memory, RAG isn't learning, and a massive context window is not organizational intelligence. This is the silent crisis of modern AI deployment.
Discover why most enterprises fail to scale AI and how leaders can bridge the gap between inspiration and implementation with a proven framework for AI maturity.
AWS provides the databases (like Amazon Neptune for graphs) and the compute for RAG. But it does not provide a turn-key memory system that learns from organizational interactions.
VC and PE firms have a once-in-a-generation opportunity to skip the decades of “digital transformation” that burden other industries and go directly to AI-native operations.
Most post-acquisition playbooks focus on financial integration and cost synergy. They miss the deeper opportunity: building what we call Knowledge Velocity - the speed at which insight moves to the point of decision.
Most GPs believe they have operational excellence figured out. They've hired smart people. They use the latest AI tools. They have processes documented somewhere. They're doing everything that worked five years ago. And it's no longer enough.
When Salesforce announced their new ITSM platform at Dreamforce 2025, one phrase dominated the pitch: "zero learning curve." It sounded audacious, even impossible. But here's what nobody in that packed auditorium wanted to admit: the learning curve has always been the wrong model for enterprise software adoption.
Why do 87% of AI tools fail? Static personas can't match dynamic human contexts. Learn how adaptive AI interfaces guarantee adoption and drive real ROI.
Dreamforce 2025 promises seamless Agentforce AI, but 70% of implementations fail due to integration gaps. Uncover cross-system workflow challenges and orchestration fixes for real enterprise success.
