Podcast: Don’t Panic! It’s Just Data
Guest: Adrian Estala, VP, Field Chief Data & AI Officer, Starburst
Host: Shubhangi Dua, Podcast Producer, Host and B2B Tech Journalist, EM360Tech
"AI is replacing BI,” stated Adrian Estala, VP and Field Chief Data & AI Officer at Starburst.
When Shubhangi Dua, host of Don’t Panic, It’s Just Data, put the statement back to Estala, the tension was intentional. In enterprise tech, few systems are as ingrained as business intelligence (BI) dashboards. For two decades, they have been the common language of decision-making – static reports, polished charts, and visuals that meet compliance standards.
However, Estala insists that the change isn't about removing dashboards. It's about staying relevant. “BI isn’t going away,” he explains. “It’s evolving.”
How AI is replacing BI?
A transformation to AI begins with something deceptively simple – a business semantic layer. Instead of forcing executives to understand data through IT-designed schemas, enterprises are creating context-rich data products using business language. A CFO sees finance terms, not table joins. A loans team sees portfolios, not pipelines.
Once this foundation is established, teams can plug the same governed, reusable data product into their business intelligene (BI) tools. This leads to improved performance and consistency rises too.
However, the growth doesn’t stop here; businesses typically ask for more. When a conversational agent is added next to a legacy dashboard, using the same trusted data product, the behaviour changes quickly. Leaders start asking questions in natural language, exploring trends they have never charted before. They make forecasts in seconds and adjust their thinking while on the go.
What was once a static reporting experience transforms into an interactive analytical dialogue. In one major bank, Estala recalls, a CEO challenged himself to avoid opening a dashboard for two weeks. He didn’t need to; the agent managed everything for him.
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Why AI Works Only When Data Gets Smaller
Enterprise AI initiatives often stall for one reason: governance paralysis. Estala has experienced it firsthand as a former Chief Data Officer. He describes environments filled with thousands of applications – lakes and warehouses, and endless pipelines that duplicate and transform the same datasets in slightly different ways. The outcome is usually inconsistent answers, audit issues, and elevated tool costs.
AI doesn’t resolve that chaos; instead, it brings it to the surface. “If your metadata isn’t right, your AI won’t be right,” Estala tells Dua. The breakthrough, he claims, isn’t about adding more tools; it’s about having fewer, well-defined data products.
Most enterprise AI programs start with architecture diagrams. Estala’s AI programs begin in workshops that exclude IT. During a 60 to 90-day "Pathfinder," business teams actively help build their own data product. They identify the right source systems, challenge duplicates, and define the metadata in their own terms. They see queries populate live.
By the time the conversational agent is added, there is no panic—only ownership, he explained to Dua. “When they leave,” Estala notes, “they feel empowered.”
For CIOs and CDOs listening, the lesson isn’t to wait for perfect architecture. It’s to start small, visibly, and let the business take charge. “If you’re waiting for a two-year AI platform build,” Estala warns, “you’re already behind.”
Organisations that are making progress aren’t necessarily the ones with the most complex models. They’re the ones with the clearest data products and the willingness to experiment openly.
When Data Becomes Strategy
See how treating data as an always-on decision engine, not exhaust, reshapes leadership, culture, and competitive advantage in the AI era.
Takeaways
- AI is replacing BI, but it's more about evolution than replacement.
- Organisations are moving towards data products for better analytics.
- Engaging business teams early is crucial for successful AI implementation.
- Conversational agents are transforming how teams interact with data.
- Data quality and governance are essential in the transition to AI.
- Business semantic layers help bridge the gap between IT and business needs.
- Organisations can achieve significant impact with AI in a short time.
- Don't wait for perfect architecture; start with a Pathfinder approach.
- Business teams can drive innovation when they understand their data.
- The future of data engagement lies in combining AI with traditional BI tools.
To learn more about how data products and AI agents are changing enterprise analytics, follow:
Starburst LinkedIn: @Starburst
Starburst X: @starburstdata
Starburst YouTube: @StarburstData
EM360Tech YouTube: @enterprisemanagement360
EM360Tech LinkedIn: @EM360Tech
EM360Tech X: @EM360Tech
Follow: @EM360Tech on YouTube, LinkedIn and X
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