From this article. As we shift to Agentic AI (systems that act, not just respond), the bottleneck is no longer code, it's trust. This article highlights a hard truth: without "guardrails" and data readiness, AI simply cannot scale. Governance must become the engine, not just the brakes. Let’s Discuss: 1. The ROI Gap: Only 22% of orgs see actual AI results. Is "bad data" the silent killer for companies? 2. Agentic Readiness: Are current frameworks mature enough to supervise autonomous agents?

One thing I’m seeing repeatedly with AI governance: Most governance frameworks fail because they live outside where decisions actually happen. Top learnings from recent work: - AI risk is rarely a model issue — it’s a context + data + ownership issue - Policies defined upfront don’t survive runtime without enforcement hooks - “Human in the loop” breaks down without clear decision rights and escalation paths - Agents amplify governance gaps faster than dashboards ever did Key challenge ahead: Governance must move from review-time controls to runtime guardrails — embedded in data access, memory, orchestration, and action execution. Curious how others here are handling governance inside live AI workflows, not just around them.

From this article : For China to lead in AI, it must first master its data foundation China generates an unmatched volume of data, but this article highlights a critical paradox: massive data does not equal meaningful intelligence. While the country has the raw "fuel," it is currently choked by unstructured formats, isolated legacy systems, and quality issues that act as barriers rather than catalysts. The strategic takeaway is clear: the next phase of the AI race won't be won by who has the most data, but by who has the most governed, clean, and interoperable data foundation. China is now pivoting to treat data quality not as an IT fix, but as a national strategic imperative—a move that defines whether they will lead or just lag in reliable AI deployment.

The AI Data Readiness Assessment helps you evaluate whether your data foundations are strong enough to support reliable, scalable, and responsible AI.It breaks down the five critical pillars of AI readiness — quality, governance, metadata, access, and architecture — and gives you a clear maturity score from Level 1 (ad-hoc) to Level 5 (optimized). Use this template to: ✅ Identify where your data limits your AI potential ✅ Spot gaps in governance, lineage, quality, and ownership ✅ Prioritize improvements across teams ✅ Build a realistic roadmap before deploying or scaling AI It’s a practical, no-fluff tool to ensure your AI initiatives rest on solid, trustworthy, and well-governed data foundations.

From this article. The G20 is moving fast to define AI as a global public good, while Washington remains on the sidelines. It’s a dangerous gamble. International norms are "sticky", once set, they are hard to change. By isolating itself, the US is allowing the rest of the world to write the playbook, effectively forcing American tech giants to eventually comply with rules they didn't write.