AI Readiness

An AI agent can only tell its data isn’t fit for purpose if the context layer records what analysts know. Here are the six fitness checks it needs.

Five takeaways from Money20/20 on how credit unions are approaching AI, member experience, and trusted data.

AI needs more than access to enterprise data. It needs to understand what that data means and how business concepts connect. Learn how data ontology provides this context, why it matters for AI, and how DataOS turns ontology into an operational layer built on governed data products.

At Cypher 2026, we explored how DataOS brings business context and governance to enterprise data so AI can deliver answers businesses can trust.

Embedding data governance turns control into enablement by building guardrails, ownership, trust, and accountability directly into everyday data workflows.

A good metric reflects purpose, drives action, and has an owner. Here is how good and bad metrics differ, and what Wells Fargo’s cross-sell target teaches.

Key takeaways from our Mumbai roundtable with 11 enterprise leaders on AI context, cost, data readiness, and moving from experiments to business outcomes.

Government agencies face the same AI and data trends as the private sector, often in a sharper form. A public sector read of the findings from The Modern Data Company's 2026 interim report.

New survey findings show 57.3% of organizations are piloting or running AI agents, while only 8.4% say their data is production-ready.

Named AI cost overruns at Uber and Microsoft show why spending caps alone cannot govern agent costs. Runtime attribution is what actually works.

AI agents thrive on context, not just data. Explore how DataOS delivers governed, semantically rich data products that improve AI accuracy while reducing cost and complexity.

Banks are sitting on a powerful data stack-but can't activate it yet. See how adding the missing layer changes what's possible.

Context is the new currency for AI readiness. Learn how a context layer helps AI agents understand enterprise data reliably and at scale.