BI Is Back, and Better Than Ever: How AI Reinvigorates Business Intelligence

Earlier this week, I came across an interesting post from Dmitry Pavlov suggesting that "BI is Dead" and challenging readers to "Change My Mind." I felt compelled to respond!  Having been deeply involved in analytics and Business Intelligence for many years, I naturally felt the need to investigate further. Dmitry's post raises some valid points: traditional BI can indeed feel cumbersome, particularly when constrained by legacy tools and manual data management, and AI changes the game. However, I don’t believe that the magic of AI powered analytics means that BI is dead. In fact, I contend that we should be asserting: "BI is Back, and better than ever!"

To suggest that BI is obsolete merely because AI can generate visualizations and answer queries is akin to implying that customer support systems or sales management systems are no longer necessary. While the form factor of the application may change, or be more AI-enabled, the key objectives are the same. The core objective of BI—to derive actionable insights from data to understand the business—remains unchanged. This very cool example from Dmitry’s post is a good example - while the content is AI-generated, the output is still “business intelligence."

What has evolved is the method by which we obtain these insights. And, thanks to AI, we are now achieving this with greater speed, efficiency, and depth than previously possible.

It’s important to note that BI platforms do more than simply create quick visualizations and dashboards. While AI can automate aspects of dashboarding and Q&A, this represents only a small part of the puzzle. It is easy to be captivated by the "wow" factor of AI generating reports instantaneously, but we must not overlook the fundamental mechanisms that underpin effective BI.

The Heart of BI: A Semantic Layer That's More Critical Than Ever

If there is an under-appreciated component in the evolution of BI, it is the semantic layer. This refers to a structured approach to defining and managing the meaning of data, ensuring that all stakeholders share a common understanding. This is an area where AI truly excels, and where robust BI platforms are essential. While AI can certainly accelerate the delivery of insights, it requires a clear and consistent framework within which to operate. Dmitry’s example of how DWAINE uses their internal documentation to infer the semantic model is a brilliant example of both: a) how AI changes the game and b) why a semantic model remains critical!

A well-defined semantic layer ensures that everyone is aligned on the definitions of terms such as "revenue," "active users," or "churn rate." (Snowflake’s Cortex Analyst experience notably starts off with a semantic model… see below)

Without this, AI tools are working with ambiguous and inconsistent data, which can lead to confusion. Far from rendering BI irrelevant, AI actually underscores the importance of data modeling, governance, and semantic consistency—areas that have always been central to traditional BI. Semantic models won’t be a thing of the past, but they will be auto-generated, evolving, and richer than the BI models of the past.

The Power (and Necessity) of Data Platforms

It is crucial to acknowledge the engine that drives AI-powered BI: the robust data platform. This includes cloud data warehouses, data lakes, and other similar technologies. BI is not merely about creating visually appealing charts; it is about ensuring that the data behind those charts is accurate, governed, and consistently interpretable. And, more than ever, these platforms must provide a level of performance that enables rapid interaction with and query refinement within this data in search of insights. 

Effective BI platforms enable the creation of reusable dashboards, facilitate the seamless sharing of insights, and provide version control for metrics. These capabilities remain essential, even with the advent of AI for faster analysis. The foundational principles of good BI—reliability, consistency, and governance—are still critical for scaling data-driven decision-making.

AI Supercharges BI—It Doesn't Kill It

AI has undoubtedly transformed BI workflows, and in a positive way. Tasks that previously required hours of manual effort can now be accomplished in minutes with AI-driven tools. This enhanced efficiency does not diminish the importance of BI; rather, it amplifies it. It allows us to focus on interpreting insights, asking more probing questions, and translating data into strategic action. Our recently announced Snowflake Intelligence feature is a great example of how AI unlocks this "BI, but even better" experience.

It is important to remember that while AI excels at pattern detection, anomaly identification, and natural language queries, it requires well-modeled data to function effectively. In other words, the tool is only as effective as its foundation—and that foundation is what BI has been building for years.

In Summary: BI is Back—And Why That's Exciting

It is true that AI can generate a chart on demand, and answer questions in natural language… even find patterns in the data that might have previously gone unnoticed.  However, true “business intelligence” still requires that we:

1. Discover meaningful metrics and anomalies.
2. Publish and Share those insights in context.
3. Collaborate and Refine to arrive at meaningful conclusions.
4. Persist valuable queries as dashboards or reports.
5. Manage Semantics to ensure organizational alignment.

AI accelerates these processes, but it does not replace them. BI is not obsolete—it is more vibrant than ever, invigorated by AI. The resulting productivity gains are finally unlocking the true potential of BI: data-driven insights woven into the fabric of everyday operations, rather than being relegated to isolated projects.

While "BI Is Dead" may be a compelling headline, it does not reflect reality. BI is indeed evolving significantly. However, at its core, it remains focused on understanding the business through data. AI simply enhances this mission, providing us with tools that were previously unavailable. If anything, it makes a strong BI foundation—characterized by a robust semantic layer, a powerful data platform, access to all data, governance, and scalability—even more critical.

Therefore, whether we call it a renaissance, a resurgence, or something else entirely, BI is back, and it is more effective than ever. This is a development that I find genuinely exciting.