Starburst Data Inc. today introduced an artificial intelligence capability designed to let business users query and analyze enterprise data using natural language, positioning the offering as a shift away from traditional business intelligence dashboards toward more interactive, real-time decision-making.

AI Data Assistant is intended to address what the company describes as a growing disconnect between the speed of business decision-making and the slower processes required to build dashboards and reports.

“AI is the new BI, and I think this is going to be the future of our industry around analytics, that increasingly you will be able to answer your questions directly through AI,” Starburst Chief Executive Justin Borgman said on an announcement webcast.

Traditional analytics workflows, which can require lengthy extracting transformation procedures, are no longer keeping pace with business needs, Borgman said. “Businesses are moving into real-time. Questions change faster than dashboards can be built, and leaders need answers, not ticket queues,” he said.

The launch is part of a broader industry trend toward applying generative AI interfaces to structured enterprise data. Borgman asserted that structured data will be the next major frontier for enterprise AI, citing its role as a system of record for business operations.

“AI is only as effective as the data it can access and understand,” he said. “Most organizations don’t have an AI problem; they have a data problem.”

Data in place

Aida operates across distributed environments without requiring data to be moved and centralized, a key architectural distinction from many existing analytics and AI approaches. Starburst’s platform, which is built on the open-source Apache Trino engine, connects to data in place across cloud storage, data lakes, warehouses and operational systems, applying governance and metadata consistently across sources.

The assistant uses a reasoning framework that the company said goes beyond simple text-to-query translation. Instead of generating a single query, it evaluates available data, constructs queries and iterates through intermediate steps to produce what the company describes as a contextually grounded answer.

“It’s much more than a chat,” said co-founder and Vice President of AI Matt Fuller. “It reasons through what a question may mean. It will identify the data sets that could answer the question and then ultimately deliver a response.”

Starburst demonstrated Aida analyzing historical Formula One racing data, generating queries, visualizations and narrative explanations based on a natural-language prompt. In another example using financial data, the assistant produced summaries, charts and executive-level summary information from general ledger data.

Fuller said one of the primary limitations of existing BI tools is user friction. He cited internal data suggesting that 41% of customers spend over four months building dashboards and 72% of users regularly bypass dashboards by exporting to Excel, adding that “67% of companies have low confidence in their in-app analytics offering.”

Aida attempts to address those issues through what Starburst calls a “context layer,” which maps business terminology to underlying data structures. That allows users to ask questions in business language while the system interprets the technical schema.

LLM choice

The platform also supports multiple large language models, allowing customers to select models based on cost, performance or compliance requirements. “With Starburst able to plug into a model of your choice, we can fit into your ecosystem,” Fuller said.

Cost control is another focus. Fuller said 84% of companies report AI costs are eroding gross margins by more than 6%, highlighting the need for flexible deployment options, including on-premises and hybrid environments.

Starburst positions Aida as complementary to existing BI tools rather than a full replacement, although executives acknowledged that the long-term trajectory could reduce reliance on dashboards.

“We’re not suggesting that dashboards disappear entirely, but rather that they become a more specialized, curated set of dashboards,” Borgman said. “For all those extra questions, you can now have this conversational interface.”

The company outlined a roadmap that includes integrations with enterprise applications such as Slack, Jira and GitHub, as well as governance features to control AI outputs and enforce data policies.

“Reports are now becoming conversations, and static BI is giving way to interactive data,” Borgman said.

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