This week on our Data Radio Show Podcast, we take a look at the AI Inflection Point. Exploring the significant shift in the Artificial Intelligence landscape, moving away from basic model selection towards a focus on operational reliability and data infrastructure. You can find the Episode on all Podcast platforms now - just search for The Data Radio Show.

We’ve been seeing a common pattern across AI projects lately:the issue usually isn’t the model — it’s the quality and structure of the data underneath it. If the data lacks consistency, lineage, context, or trust, AI tends to amplify those problems rather than solve them. We recently put together a piece exploring why data quality is becoming a core AI readiness issue in modern lakehouse environments, including where approaches like Data Vault can help create a more reliable foundation for AI. Interested to hear if this aligns with what others are seeing. https://ignition-data.com/resources/data-intelligence-series/your-ai-is-only-as-trustworthy-as-your-data-a-practical-framework-for-data-quality-in-the-modern-lakehouse

Over the last 18 months we’ve watched Eastern open weights ecosystems evolve differently to Western “premium platform” models. The result is a messy reality most teams are not instrumented for: > Model portfolios, > Caching discounts, and > RAG pipelines spreading across the estate with almost no audit trail. If you had to fix one thing this quarter, would it be a model gateway, AI observability, or retrieval corpus ownership? Check out this weeks video edition of www.datapro.news

This paradox is not a communications problem. It is a data problem, and it lands squarely on us. Three years on from the ChatGPT moment, the backlash is no longer background noise. This week we investigate what the socio-political reckoning around AI actually means for data professionals, from model collapse and Shadow AI to the governance discipline that will separate the teams who are trusted from those who are not. We also ask three questions that every data professional should be sitting with right now. They are uncomfortable. That is the point. This week's edition is in your inbox. 👇

This week’s edition of www.datapro.news is a practical guide to running model work like any other production workload, with sessions, artefacts, quality gates, and cost controls. We break down what Anthropic actually shipped at Code with Claude 2026, why Claude Managed Agents resembles a job runner, how Outcomes functions like a rubric-driven test loop, and where multi-agent orchestration helps. You will also get a simple reference architecture you can use to pick a safe pilot, plus the governance questions you should answer before “agent memory” becomes a silent failure mode. Check out the video edition below ⬇️