We’re seeing more users choosing AI over traditional BI dashboards. Who can blame them? After all, why settle for a static view of your data when you can have a conversation with it instead?
This is part of a larger trend. The entire model of conversational analytics is perfect for business users looking to understand their enterprise in a deeper way. At any time, for any reason, they can ask ad hoc questions of their data and get an answer, not in days, but in minutes, when they really need it. It’s a kind of superpower.
This all falls apart, though, if those answers can’t be trusted.
AI technology is still as prone as ever to gaps, bias, and hallucinations. Succeeding with AI data analysis requires the proper data architecture, one optimized for access to context above all else. Notably, this is a different architecture than that required by BI, but built on the same foundations. In this article, we’ll dig into this shift from BI to AI, why it’s compelling, and how to lay the proper foundation to make the shift.
Why AI is quickly replacing BI
Every day, in different ways, users are outgrowing BI. The transition itself is massive. Over the years, companies have poured millions into making BI dashboards. That effort, sadly, is too often never fully realized. A survey from Luzmo found that 41% of companies spent up to four months making dashboards. Despite this, 72% of their users said they just dumped the raw data into Excel anyway.
In this context, users are clearly starved for an alternative, and AI is giving it to them. Let’s look at how that happens, and the form that it’s taking.
AI can move as your business moves
Suppose a user has a great idea at 6 a.m. They get to the office and start looking through BI dashboards, hoping to find data to affirm or refute it. Except the data isn’t there.
What happens next? Typically, they ask the data engineering team to produce a new dashboard report. This adds yet another to-do item to an already overloaded team’s workload. They might not be able to get to it for days, or even weeks.
Let’s say they do get to it in a timely fashion, but then, suddenly, your business shifts. Maybe a huge world event happened. Maybe the business shifted its strategic focus or marketing message.
A BI dashboard is static. It will always be static. It can’t change on a dime to match this changing reality.
AI can change, and it is built on the concept and promise of changing quickly. A user can change their question or add a new data set to their query, and the answers the system returns will reflect that shift instantly.
AI delivers meaning, not just metrics
Traditional BI gives you numbers, but it can’t explain them, or put them in context.
For example, a report might tell you that sales rose 10%. Is that good? Well, not if acquisition costs also rose 20%.
This is the kind of analysis AI excels at providing. Users can dig in and ask exploratory questions from the initial data they get back using what Perplexity CEO Aravind Srinivas called “relentless questioning.” AI queries will often typically return related context that users didn’t know they should ask about. For example, AI systems might factor in customer acquisition cost figures automatically when a user asks about sales numbers.
Overall, AI isn’t just faster than traditional BI. It’s also deeper and more powerful. It acts as a business user’s personal analytics assistant.
AI draws on the full data estate
AI has this advantage over BI because it’s not limited to a specific data set in the same way as a dashboard.
In traditional BI, data engineers combine multiple data sources into structured tables inside a data warehouse. A report then queries this data and presents the result to stakeholders. This model was built to work within a disconnected world riddled with data silos.
By contrast, in a well-engineered implementation, AI draws on the full context available to it across a company’s data estate. It employs a federated data model that pulls in structured, unstructured, and semi-structured data from across the enterprise.
Businesses that enable this wide access to context are the ones that can most easily replace BI with AI.
AI delivers continuous decision support
Continuity is another differentiator. BI operates on cycles, for example, weekly, monthly, or quarterly views. Depending on the complexity, producing a new dashboard can take anywhere from days to weeks.
This gap is so wide that, by the time the dashboard’s ready, users may not even need it anymore, which explains, frankly, why so many dashboards go unused.
AI isn’t like this. Users can create ad hoc queries driven by business need and get answers back in minutes, not weeks. If something in the underlying data changes, the LLM can often adjust automatically, eliminating the need to file a ticket with the data engineering team when a report mysteriously breaks.
This assumes you have a solid data architecture for AI in place, with continuous data ingestion. Businesses that build this foundation can transition from the era of slow, periodic reporting to the era of rapid, continuous decision support.
AI delivers true self-service
BI dashboards promised to open up a new world of data self-service to business users. Unfortunately, they didn’t deliver.
Many data engineering teams that monitor dashboard usage find that many go unused. The Luzmo report seems to bear this out.
By contrast, AI delivers true self-service. It does so in the form users most need, that is, by offering immediate responses to their most urgent data-driven questions.
What’s required to make the leap from BI to AI
There’s a compelling business case for making the shift from BI to AI. It’s not enough, though, to just point an LLM at your data.
LLMs have general language parsing capabilities and generalized knowledge. They know little to nothing about how your business works.
The good news is that you can feed them this knowledge. This requires providing AI platforms with two things.
Access to all your data sources. Data federation breaks down data silos, giving AI access to the full scope of structured, unstructured, and semi-structured data within your enterprise.
Supporting data federation requires building:
- A materialization strategy for consistent performance and cost predictability
- A performance-first architecture to prevent bottlenecks caused by large, federated queries
- A federated approach to governance that includes built-in access control capabilities, filter and column masking, and policy harmonizing across disparate data sources
Access to all your context. Context is any data that provides critical information on your business or AI use cases. Data federation is the necessary prerequisite to enable context, as it ensures you can find the high-quality data you need today, not after a long and expensive data centralization effort.
Building on what you already have
The good news is you don’t need to build this from scratch. You just need a good context and governance layer within your existing architecture.
That technology is the data lakehouse. A data lakehouse is the beating heart of a scalable AI data architecture. It combines federated data access with high-performance materialized data via Apache Iceberg.
Starburst is an enterprise intelligence platform that’s built for the AI era. It allows you to connect data from any source to the AI model of your choice, enabling data engineering teams and business users to quickly access, ingest, collaborate on, and govern your AI data.
You still need a way to make this data available to your AI applications. AIDA provides that, by connecting your federated, curated data to your LLMs and custom agents. AIDA converts natural language queries into SQL, giving users access to the answers they need today with speed and context.
To see how Starburst can help you transition from BI to AI. Watch this recent webinar.
