For many years, the data industry has operated under the drumbeat of a single model. Centralize all your data. Build pipelines. Deliver insight through Business Intelligence (BI) tools and dashboards.
That model defined an era. But expectations are changing. Business users no longer want static summaries of what happened last quarter. They want dynamic answers, in real time, grounded in trusted data.
AI is reshaping how we interact with information, bringing both profound disruption and powerful new opportunities.
Why AI is replacing BI
We are now in a new era. As enterprises move from reporting toward AI-driven decision-making, the constraints of traditional BI become impossible to ignore.
Dashboards were built for a static consumption model
Dashboards were designed to provide a structured view of historical performance. They assume predefined questions, fixed schemas, and a curated set of data sources. In that context, they work well.
But that model is inherently static.
Building and maintaining dashboards requires coordination across teams, agreement on definitions, and time to formalize logic. The result is clarity, but also constraint. They are optimized for reporting on what has happened, not for continuously exploring what might be happening now.
As the demands on data evolve, the limitations of that model become more visible.
AI looks towards a more continuous consumption model
In contrast, AI systems are designed for continuous, human interaction rather than periodic reporting. In this, they are more conversational in nature. They retrieve context across domains, combine signals from multiple environments, and respond dynamically as new information arrives.
That difference matters.
Dashboards are optimized for predefined questions and structured summaries. They assume the scope of analysis is known in advance. AI systems assume the opposite. They explore, refine, and expand the question itself.
How data foundations impact insights
There is a persistent belief in the market that if AI is not delivering enterprise value, the answer is a more advanced model. In reality, the constraint is rarely the model. It is the data foundation beneath it.
As enterprises shift toward AI-driven decision-making, the demands placed on the data layer fundamentally change. It must support real-time, governed access across distributed systems. Specifically, for the data layer to function properly, it needs to carry business context, not just raw records. Further, it needs to enforce policy consistently at scale. And it must do all of this without forcing consolidation into a single platform.
Dashboards are not disappearing tomorrow. They will remain part of the enterprise landscape for years. But like every interface built for a previous era, their role will narrow. The center of gravity is shifting toward systems that allow people to interact with data more fluidly, more directly, and more naturally.
The question is no longer whether that shift will occur. The question is what foundation will support it.
How Starburst is operationalizing the shift from BI to AI
Starburst was built for this moment.
From the beginning, we operated on a simple premise. Enterprise data is distributed, and it will remain distributed. While much of the market optimized for consolidation into a single platform, we built a federated foundation that enables secure, governed access across multiple data sources. What was once seen as architectural flexibility is now the prerequisite for enterprise AI.
In this model, AI does not reduce fragmentation. Rather, it exposes it. It demands consistent access, shared context, and enforceable policy across systems. That is not a new problem for us. Instead, it is the problem we were always designed to solve, applied in a new area.
The shift to AI does not require us to reinvent our position in the stack. We are already there.
The foundation enterprises now need is the one we have been building all along.
How Starburst’s AI Data Assistant (AIDA) makes getting answers easier than ever
If our approach to data enables the shift, the Starburst AI Data Agent (AIDA) brings this to life.
AIDA represents a fundamental shift in how organizations interact with data. AIDA is not simply a conversational interface layered on top of enterprise data. It is the operational expression of the foundation we have built, the interface between business intent and distributed, governed data.
Because it runs on top of Starburst’s federated architecture, AIDA inherits what the enterprise now requires:
- Real-time, cross-platform access
- Consistent enforcement of policies
- Shared business context embedded alongside the raw data
Every interaction, whether initiated by a business leader or an AI agent, is executed against the same governed layer.
The two approaches work in tandem. Federation provides the reach. AIDA provides the interaction.
Overall, AIDA offers a way to interact with data that is:
Adaptive
It moves at the speed of the business, responding to changes without requiring a new dashboard build.
Interactive
It allows for a conversational flow in which one answer naturally leads to the next, deeper question.
Distributed
It empowers individual teams to work with their own data while remaining aligned with enterprise-wide governance and definitions.
This is not just a new interface. It is a new interaction model. And it is powered by three foundational layers.
1) Access that allows you to reach all your data
AI is only as powerful as the data it can access. Enterprise data is inherently distributed across cloud platforms, data sources of all types, and SaaS systems. That distribution is not temporary. Rather, it is structural.
Starburst’s federated architecture enables governed, high-performance access across those environments without forcing consolidation. Policies are enforced consistently. Performance scales with enterprise demands. Security remains intact.
Data access is not an enhancement to AI. It is the starting condition. Without it, AI operates in fragments. With it, AI operates across the enterprise.
2) Context that understands your business
Access alone is not enough. It produces answers, but without context, those answers will not be the right ones.
In traditional BI environments, governance often lived inside dashboards. Definitions were embedded in reports. Access was controlled through curated views. That model worked because interaction with the underlying data was limited.
AI changes the interaction model.
AI must understand how the business defines its metrics, which datasets are authoritative, and how policies apply across domains. Governance can no longer live solely in dashboards. It must be embedded in the assets AI consumes.
Data products make this possible. By organizing data around business domains, with embedded definitions, ownership, and policy, they create durable units of trust. AI operates against curated, governed assets rather than ad-hoc extracts or reverse-engineered reports.
This is what allows AI to replace BI as the primary consumption layer. Governance moves from the dashboard to the foundation. Access expands without losing control.
3) Extensibility that integrates AI into the enterprise
Enterprise data is distributed by default. Because of this, it will never standardize around a single agent, model, or application. Instead, organizations will run multiple agents across teams, use cases, and vendors. That heterogeneity is not a temporary phase. Rather, it is the standard operating model.
This is really the old story of optionality, retold for the AI age, and any data foundation must support that optionality.
Again, Starburst is built for this moment.
AIDA is designed to integrate Starburst’s governed, federated foundation into any AI ecosystem. Customers can use AIDA directly, embed it within their applications, or bring their own agents entirely. Through open extension points and support for customer-managed MCP servers, agents can call into and operate against the same consistent layer of access, policy, and business context.
Customers choose the agents. Customers control where they run. Starburst ensures that wherever they operate, they operate on governed, consistent data.
The future of AI is powered by a Starburst data foundation
AI’s replacement of BI is about more than technology. At a foundational level, it’s about opening up data to a new form of conversational, human interaction.
For years, dashboards acted as a gatekeeper. They structured access, but they also constrained it. AI changes that pattern. When governance, interoperability, and context are embedded in the data foundation itself, more people can interact directly with trusted data. Exploration becomes continuous rather than episodic. Insight becomes operational rather than static.
That shift is ultimately about empowering human beings.
Not by removing control, but by strengthening the architecture underneath it. When the foundation is coherent and governed, organizations can widen access without widening risk. Business users are no longer limited to what was pre-built. They can ask new questions, refine them in real time, and act with confidence.
This translation is already underway.
Starburst leads on disrupting BI workloads
At Starburst, we are building the foundation that makes it possible.
It starts with federated data access — a control layer that unifies access across sources without forcing centralization.
It extends through context — governance embedded directly in data products, where definitions, ownership, and policy travel with the asset itself.
It scales through extensibility — interoperability by design across cloud platforms, data sources, and agents, preserving optionality in a multi-platform, multi-agent world.
And it comes to life through AIDA — the operational interface that connects business intent directly to distributed enterprise data.
The disruption of BI is not the end of dashboards. It is the beginning of a new way of working with data. It is no longer just about the questions you are asking. It is about the questions you did not know to ask.
And that is the future we are building toward.
