Updated March 27, 2026
AI vs. BI: Differences, What Changed, and Why Semantics Became CriticalEnterprise analytics was never just about data. It was about trust and confidence, getting the right people the right information so they could make intelligent decisions and keep things moving forward. The BI stack was made for that purpose, and…
Updated March 26, 2026
AI Agents Are Transforming Compliance: Opportunities and Risks for Enterprise DataAutonomous AI agents, which are systems designed to accomplish tasks and goals independently without human oversight, have become integral across a wide range of use cases, spanning from customer service to supply chain management. It's no surprise that, as regulatory…
Updated March 16, 2026
Using AI Agents for Data Analysis: How Autonomous Agents Are Changing AnalyticsFor a long time, enterprise analytics centered around building dashboards, running scheduled reports, and relying on the right person to ask the right question at the right time. That model is changing. Autonomous AI agents can now look through data…
Updated March 26, 2026
Power BI & Excel on Snowflake Without Data Movement | Semantic Layer for AIMost enterprises have already standardized where their data lives. What they haven’t standardized is how that data is interpreted. Power BI and Excel users still rely on a workaround: moving data out of Snowflake into extracts, cubes, or Microsoft Fabric…
Updated March 17, 2026
Why Moving Data for BI and AI Creates Architecture ProblemsIn modern data architectures, analytics tools often replicate data from the warehouse into secondary systems to improve performance. While this can speed up dashboards, it introduces new problems: duplicated datasets, inconsistent metrics, fragmented governance, and rising cloud costs. Modern analytics…
Updated March 12, 2026
Hits and Misses from Gartner Data & Analytics Summit 2026Gartner opened the Data & Analytics Summit 2026 with a memorable metaphor. The keynote framed the current moment as a raft ride through a roaring, treacherous river. The river represented AI. Fast-moving. Powerful. Difficult to control. The message was clear.…
Updated March 11, 2026
AI Can’t Run on Untrusted Data: Why Agentic Analytics Requires Live Semantic ConnectivityA logistics company deploys an AI agent to monitor inventory and automatically trigger reorders. The model is solid. The prompts are well-engineered. The agent connects to their analytics environment and starts running. Three weeks in, it fires a reorder for…
Updated March 5, 2026
Autonomous AI Without a Semantic Layer Is Just Expensive GuessingI've been in the BI and data space long enough to recognize when something fundamental shifts. Denny Lee and I first worked together when he was at Microsoft on SQL Server Analysis Services (SSAS), and I was a customer building…
Updated February 25, 2026
Headless AI Agents Are Already Here and They’re Where the ROI IsMost enterprise AI conversations still center on prompts, whether through chat interfaces, copilots, or question-answer workflows. But the highest-value AI agents don’t wait for someone to ask a question. They monitor signals, detect thresholds, and trigger actions. These are headless…
Updated February 24, 2026
The Missing Pillar in Agentic AI: Why I Joined AtScaleRecently, while debating my next career chapter with a close friend, they highlighted something I had almost begun to take for granted: the most important work our civilization is doing right now is tied to the advancement of AI. It…
