Updated March 30, 2026
Why AI Agent Governance is the Latest Priority for EnterprisesOver the past year alone, AI tools that once surfaced insights and handed them to a human for action have begun carrying out those actions themselves. Sixty-two percent of organizations are now experimenting with AI agents, according to McKinsey's latest…
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 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 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 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…
