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"…
Updated January 6, 2026
Data & Analytics Predictions for 2026As 2025 comes to a close, one thing is clear: the experimentation phase of GenAI in data and analytics is ending. In its place, enterprises are entering a more pragmatic era focused on trust, governance, economics, and tangible business outcomes.…
Updated January 6, 2026
Looking Back at 2025: Rating My Predictions on AI, Semantics, and Data DemocratizationLast year, I made a set of predictions for data and analytics trends for 2025: "The Future of Data & Analytics: Trends to Look For." Now that the year has wrapped, it’s time to put those forecasts to the test…
Updated December 18, 2025
Snowflake Venture’s Strategic Equity Investment in AtScale: The Strategic Play for Semantics in the EnterpriseThis isn’t about funding. It’s about control. After 15 years helping enterprises modernize analytics, I’ve learned to ignore the size of the check. What matters is why it was written. Snowflake Ventures led AtScale’s largest equity financing to date. But…
Updated December 18, 2025
Why Snowflake’s Strategic Equity Investment in AtScale Validates the Open Semantic FutureI've spent more than two decades watching the data industry cycle through architectural shifts. Each wave promised to solve this fundamental challenge: delivering consistent, governed analytics that actually scale with the business. Most failed because they optimized for a single…
