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…
Updated February 20, 2026
Why Agentic AI Fails Without a Semantic LayerMany teams assume they’re doing agentic analytics because they support text-to-SQL or chat-based BI. And while conversational BI does change how questions are asked, it doesn’t go so far as to change how decisions are made. It’s like transitioning from…
Updated February 17, 2026
The Hidden Costs of Letting People “Talk to Data” with AIConversational BI pilots succeed because they operate in constrained environments. A dozen users, curated datasets, predictable query patterns. When AI agents enter the picture, everything breaks. Gartner forecasts that more than 40% of agentic AI projects will be abandoned by…
Updated February 10, 2026
AI Governance Isn’t Theoretical Anymore: What Enterprise Leaders Are Actually AskingAI governance discussions used to center on frameworks and theoretical risk scenarios. This month’s TDWI webinar with Cal Al-Dhubaib, head of AI and data science at Further, revealed the many ways in which theory has moved to the practical. As…
Updated February 3, 2026
Why Most Enterprise AI Projects Fail (and How to Make Them Work at Scale)Most enterprise AI projects follow a familiar pattern. Successful demos lead to promising pilots. Executives commit budgets to scale the initiative across departments. Then the wheels come off. Despite $30-40B in enterprise investment in GenAI, MIT’s Project NANDA found roughly…
Updated January 29, 2026
Why Context Became the Most Critical Layer in Enterprise AII recently sat down with Juan Sequeda, Principal Researcher at ServiceNow and co-author of Designing and Building Enterprise Knowledge Graphs, to discuss what's happening in enterprise data and AI. Listen to the full podcast episode here. Juan brings a unique…
Updated January 21, 2026
The Hidden Cost of Semantic Drift in Enterprise AIThree teams ask the same question: "What was our gross margin last quarter?" Finance's dashboard shows it’s 30%. Sales’ margin reports show 32%. The AI copilot reports 31%. Which one's correct? Technically, all of them. Each system defines "gross margin"…
