Many AI-analytics demos promise magic: impressive visuals, slick dashboards, and smooth interactions. But in real business settings, those polished demos often break down. This post pulls back the curtain and explains what it really takes to make AI analytics work at enterprise scale. You'll learn about: Building agentic architecture that supports complex queries and multi-step insights Ensuring reliability with intent parsing, root cause detection, and strong validation layers Managing data quality, security, and governance so insights can be trusted Scaling models, visualizations, and workflows to support thousands of users and billions of rows of data If you're evaluating AI analytics tools or planning your own build, this is your guide to separating real capabilities from toy demos and focusing on what drives measurable performance.

Data-driven organizations should not be content with the same old approaches to data consumption. In the modern data stack, each layer plays a key role in your organization’s goals to get better insights from vast amounts of data and to proactively uncover new opportunities for growth.

AI-powered Insights & Analytics is the missing layer of the Modern Data Analytics Stack that enables analysts to control a lot more of the data value chain and increase their impact on organizations.

Learn why modern business and analytics teams need a new stack to realize the full potential of their data with AI-driven augmented analytics.