Starburst Unveils Enterprise Intelligence Platform at AI & Datanova, Giving Enterprises a Faster Path to Trusted AI

Starburst

Starburst announced the Starburst Enterprise Intelligence Platform, enabling enterprises to run AI directly on governed data across distributed environments. At the center of the platform is AIDA, now generally available, which brings AI-powered intelligence directly into the workflows, applications, and agents where business users work. Starburst also introduced new AI-Ready Data Products that provide consistent business context for queries, models, and AI agents, along with additional capabilities including Icehouse Ingest and Icehouse LakeOps for Apache Iceberg operations, and Bring Your Own Cloud (BYOC) deployments for customer-managed infrastructure.

According to Mavvrik’s 2025 research, 84% of companies report AI costs are reducing gross margins by more than 6% — not because AI doesn’t work, but because the data beneath it doesn’t. Data remains fragmented across clouds, lakes, SaaS apps, and operational systems, forcing expensive data movement, creating governance blind spots, and eroding confidence in AI outputs. The result is slower, lower-confidence decisions, higher costs, and AI initiatives that stall before they scale.

Starburst’s answer is to bring AI to the data, not the other way around. The Starburst Enterprise Intelligence Platform gives enterprises a single platform to run AI directly on distributed data, in place, without moving or replatforming, while providing consistent business context to queries, models, and agents regardless of where data is stored or processed — across clouds, catalogs, and enterprise systems.

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“At Vizient, we’re focused on improving how teams across the organization access, connect and use trusted data in a complex healthcare environment. As part of that effort, we’ve been building the foundations for a governed internal data marketplace and reusable data products that support cross-domain analytics and AI enablement. Our approach incorporates a range of technologies, including Starburst, to help teams work more effectively across distributed datasets while reducing unnecessary data duplication,” said Ram Radhakrishnan, Engineering Leader – Data & AI Platforms, Vizient.

“Enterprises are realizing that scaling AI depends less on the models and more on access to trusted, governed data across complex hybrid environments,” said Brad Shimmin, VP & Practice Lead, Data Intelligence, Analytics, and Infrastructure at The Futurum Group. “The industry is shifting toward architectures that reduce data movement while preserving governance and business context. Starburst’s latest announcement reflects that trend with a focus on distributed data access, embedded AI, and enterprise-scale analytics performance.”

Intelligence Where Work Happens: AIDA

Starburst’s AIDA is now generally available. Built for executives and business users who cannot afford to wait on dashboards or data teams, AIDA operates directly inside the workflows, applications, and agents where they work. Unlike AI tools that rely on centralized or duplicated data, AIDA works across distributed enterprise data in place, using Starburst’s AI-Ready Data Products to provide consistent business definitions and governed context at runtime, regardless of where data resides.

AIDA helps users move from question to action by generating visualizations, triggering workflows, opening tickets, updating records, and initiating processes across connected systems — without leaving the applications they use. Through support for the Model Context Protocol (MCP), AIDA can also connect to external tools, unstructured content, and third-party systems to provide richer context across enterprise workflows.

“AI has outpaced data architecture,” said Justin Borgman, co-founder and CEO of Starburst. “Most enterprises are trying to layer AI on top of fragmented, ungoverned data, and it’s not working. At AI & Datanova, we’re showing a different path. With the Starburst Enterprise Intelligence Platform and AIDA, organizations can finally operationalize AI — in weeks, not months — on top of the data they already have, without moving it or rebuilding their stack.”

SOURCE: Businesswire