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 12, 2026
Welcoming Luis Maldonado as Chief Product OfficerWe’re excited to share that Luis Maldonado has joined AtScale as our new Chief Product Officer. Luis will lead AtScale’s global product strategy, roadmap, and execution as we scale our AI-ready semantic layer platform. As enterprises move from AI experimentation…
Updated February 12, 2026
Welcoming Amy Miller as Vice President of Customer SuccessWe’re excited to share that Amy Miller has joined AtScale as our new Vice President of Customer Success. Amy will lead AtScale’s global customer success organization, with a focus on driving customer adoption, operational excellence, and measurable business outcomes across…
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 27, 2026
Why AtScale Is Helping Shape the Open Semantic Interchange (OSI) Initiative2026 is shaping up to be the year enterprises finally accept that semantic layers are no longer optional. As organizations scale AI initiatives and rationalize their analytics stacks, a familiar problem keeps resurfacing. The same business question produces different answers…
Updated January 15, 2026
What Actually Changed in 2025 and Why It Redefined the Semantic Layer2025 wasn't just another year of AI hype. It was the year enterprise data shortcomings became visible at scale. Multiple industry studies show that most AI initiatives stall before production or fail to scale. MIT’s Project NANDA found that roughly…
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 22, 2025
The Rise of AI Analysts and the Infrastructure They RequireI've been building analytics software for more than two decades. I've watched waves of innovation promise to democratize analytics, including self-service BI, cloud warehouses, and natural language query systems. Each wave delivered value, but also exposed new challenges. We're now…
