This article outlines the importance of having governed and discoverable data for successful AI projects. It highlights common pitfalls in AI implementation and presents a structured approach to ensure data quality and compliance. A roadmap is provided for creating a reliable data stack that supports effective AI systems.

This article covers recent advancements in technology, including new AI capabilities from IBM and Cisco, as well as updates on cloud revenue driven by generative AI. It also highlights trends in data governance and unified communications.

This article discusses the evolution of data governance from a rigid, compliance-focused approach to a more dynamic, context-driven model. It argues that as AI systems become more autonomous, organizations need to shift from controlling data to ensuring accountability and intentionality in how data is used. The author emphasizes the importance of negotiating meaning and maintaining oversight in increasingly complex socio-technical environments.

This article discusses how Google integrates AI agents into its cybersecurity operations. It outlines key lessons learned in building these agents, focusing on trust, real problem-solving, performance measurement, and the importance of foundational practices.

The article outlines the challenges enterprises will face in scaling AI systems by 2026. It emphasizes the need for robust data governance, vendor independence, and updated infrastructure to handle the demands of AI workloads. Companies not adapting to these changes risk falling behind.

Google Cloud introduces new AI-powered features in Cloud Storage, including auto annotate and object contexts, to help organizations analyze and derive insights from their unstructured data. These tools automate the generation of metadata and allow users to attach custom tags, facilitating data discovery, curation for AI, and governance at scale. This shift transforms unstructured data from a passive resource into an active asset driving innovation.