Check out Datavid's in-depth articles on ai and genai.
See why enterprise AI decision-making can remain slow despite faster AI, and how context, governance, and human oversight can increase decision velocity.
by
Philippe Delorme
What is AI-ready data? Learn why FAIR data principles, provenance and semantic grounding create the foundation for trusted enterprise AI.
Most AI pilots never ship. See why operationalizing AI is a delivery-model problem and how forward deployed engineering gets pilots into production.
What a knowledge graph for AI agents costs to run: ownership, maintenance, lakehouse fit, and where graphs earn their cost. A production operating guide.
Six agentic AI use cases in regulated industries, scored by autonomy, evidence, and data prerequisites, plus how to sequence your first deployment.
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AI agents need more than data. Discover how semantic context, knowledge graphs, and governed knowledge build a trusted foundation for enterprise AI.
by
Clive Smith
Delaying FAIR data principles in life sciences can slow AI features, data reuse, and product delivery. See the evidence and when waiting is defensible.
Data readiness for AI requires more than data quality and governance. Learn why semantic readiness is the missing layer for production-grade enterprise AI.
Discover why trusted enterprise AI depends on governance, semantic context, explainability, and connected knowledge, not model capability alone.
by
Avinash Dixit
