GenBI - Generative Business Intelligence vs Traditional BI
Business intelligence (BI) is undergoing a revolution. Until recently, BI meant dashboards, rigid SQL queries, and weeks-long turnaround times for new insights. Now generative BI is changing the game, putting the power of analytics directly into the hands of every business user through natural language.
Why does this matter now? Well, enterprises today face relentless pressure to move faster. Decisions need to be made in hours, not quarters. Traditional BI, while robust, creates bottlenecks, relying on analysts to build custom reports or dashboards. Generative BI (GenBI) democratizes analytics, allowing users across sales, finance, operations, and beyond to ask questions in plain language and get contextual, actionable answers instantly.
The stakes are high: Organizations that enable self-service analytics, contextual insight generation, and multi-turn interaction can accelerate decision-making and broaden data literacy across their workforces. Generative BI makes this possible, and this article will unpack how it works, what makes it fundamentally different from traditional BI approaches, and why it’s emerging as a critical advantage for modern enterprises.
Summary of key GenBI concepts
What has traditional business intelligence been good at?
Traditionally, BI has focused on descriptive analytics: understanding what happened through reports and dashboards. BI dashboards visualize past sales data or measure business performance against predefined KPIs. If the CFO needs to investigate why a KPI has been missed, more context is required, which might come from asking the BI team to add more data to the dashboard or a conversation with a particular business unit manager. Performing this diagnostic analytics to identify why something happened through root-cause analysis is time-consuming. The traditional dashboard and reporting approach is incompatible with the conversational, context-driven, and creative nature of predictive and prescriptive analytics: the ability to anticipate what is likely to happen and determine the best course of action.
Large language models (LLMs) provide conversational interfaces and encourage users to ask questions. GenBI still provides dashboard and reporting capabilities, but the primary user interface is conversational, a shift that enables self-service analytics for everyone. In our previous example, the CFO can ask directly why a business unit manager's performance dropped month-on-month.
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Core application layer capabilities of GenBI
To achieve the level of self-service described above, GenBI applications need some core capabilities.
Natural language query and interaction
A conversational interface is fundamental to supporting self-service analytics and encouraging different types of data analysis. GenBI uses LLMs to interpret the user’s intent, extracting key elements like the desired metric, time period, and visualization type.
Suppose a user asks a question in plain language, such as “how many bookings were made each year?” The system deconstructs this prompt into a plan like this:
- Identify the table: hotel_booking_xhtn.
- Determine the relevant column for grouping by year: arrival_date_year.
- Count the total number of bookings for each year using COUNT(*).
- Group results by arrival_date_year.
- Order results by arrival_date_year ascending for clarity.
The success of generative AI depends on this semantic mapping. Traditional semantic layers used in business intelligence tools have evolved into context layers that capture an organization’s semantics, metadata, schema relationships, historical queries, and business logic to improve data access by providing more context to the LLM.
After the relevant data sources have been identified, the system generates the following SQL query.
SELECT
arrival_date_year,
COUNT(*) AS total_bookings
FROM
hotel_booking_xhtn
GROUP BY
arrival_date_year
ORDER BY
arrival_date_year ASC
Finally, the system generates a visualization that answers the user's question.
To encourage interaction and deeper analysis, the system also follows up with other useful questions to ask.
Generative visualizations and self-service
A key component of GenBI is providing self-service analytics. If a user can describe what they want to achieve, the system should be able to action this. GenBI still needs to provide the reporting and dashboard generation capabilities of traditional BI tools, but these insights are generated through a conversational interface that:
- Automatically generates an appropriate visual based on the user’s question without requiring manual chart building
- Produces natural language summaries and executive briefs that explain key findings, trends, and outliers
- Creates entire dashboards from a single prompt
The natural language capabilities of GenBI also extend to updating visualizations. In this example, the “Reservation Cancellation Rate by Month” graph would benefit from a second Y-axis for “Cancellation Rate.”
This prompt updates the chart to the following. Notice that the system has automatically labelled the Y-axis and used a combination of bars and lines to display the data more clearly.
Support different types of analytics
GenBI needs to provide the foundational capabilities of traditional BI tools to help users perform exploratory data analysis and answer “what happened” questions. GenBI systems provide suggested questions to inspire users and tools to explore new datasets.
Shown below is an example from a hotel booking dataset where the GenBI system provides new users with an “explore this data” option. Clicking this automatically inputs this prompt: “Help me understand the dataset. Then give me some ideas for questions to get started.”
The “some ideas for questions to help you get started” list shows how GenBI supports deeper data analysis. Question 9 is a more complicated one to answer. GenBI data analysis systems are able to address this type of data analysis, as shown below.
To help build user trust, the system also flags that this question was answered with limited context. This trust aspect will be explored later in the article.
Proactive agents
Proactive agents provide a virtual data analyst capability and are made possible by LLMs and the context layer. These agents are configured using natural language and can monitor key metrics or detect anomalies. They work in the background, analyzing historical data to distinguish between normal fluctuations and true anomalies. They can deliver insights via email or Slack or interact with an API. For the hotel bookings example above, a “Bookings” proactive agent can be fined via natural language like,
“Track the monthly reservation cancellations and alert me if the cancellation rate exceeds 30%.”.
The GenBI system provides a visualization (below) showing the relevant data. The alert threshold is shown, which may indicate that the threshold needs to be refined.
A proactive agent could also be used to follow up with an email to capture why a customer canceled their reservation.
Architecture and building blocks of GenBI
A genBI system's core app layer capabilities are built on a modular, layered architecture. As illustrated in the diagram below, the Data Sources, Governance, and Security layer forms the foundation, ensuring connectivity to trusted enterprise data. Above it, the Context Layer provides the data with semantic understanding, schema knowledge, historical queries, and user feedback, creating a shared “enterprise memory.” Then, the Agentic Layer interprets user intent, generates code or queries, and orchestrates data agents that proactively surface insights. At the top, the Insights Layer transforms this intelligence into valuable insights for the user through conversational interfaces, dashboards, analytics, and natural language summaries.
Building trust in GenBI
How do you get team members and stakeholders to trust insights when they are coming from automated AI systems instead of human analysts? Here’s what’s required.
Data governance
Enforce least-privilege access so that only authorized users and systems can view or analyze sensitive business data. Enable comprehensive audit logging to track every query, data access, and AI-generated report. Maintain strict compliance with relevant regulations (GDPR, HIPAA, SOC2, etc.) to assure stakeholders that AI insights are as well-governed as those created by humans.
Explainability
Trust grows when users can understand how and why an answer was generated. Make data lineage visible by showing where the numbers came from, which tables or documents were referenced, and what logic and queries were applied.
User feedback
Encouraging user feedback builds trust in the AI system. In the example below, WisdomAI asks for user feedback and indicates that the question was answered with limited context.
The system explains the missing context and provides definitions that the user could provide to improve the answer.
Use cases for GenBI
How does all of this play out in the real-world? In the following subsections, we explore three real-world examples that demonstrate how GenBI solutions are transforming decision-making across various industries.
Energy
A Houston-based global energy leader managing drilling across 14 countries empowered over 2,000 engineers with agentic analytics by deploying WisdomAI. Previously, frontline teams faced bottlenecks, relying on analysts or SQL for operational questions about well status or job performance, slowing decisions company-wide. With WisdomAI’s data and planning agents, engineers now query WellView and Snowflake data directly in natural language, with insight agents instantly synthesizing results and referencing thousands of pages of drilling manuals. This led to a 50% accuracy improvement over other AI tools, eliminated manual report issues, and enabled field teams to access critical insights in seconds.
Manufacturing and supply chain
Rehrig Pacific, a fourth‑generation family-owned manufacturer, boosted analyst productivity 5-10× using a GenBI solution. Confronted with exploding IoT and computer vision data and dashboards that took weeks to build, they adopted scenario analysis: natural-language queries, step-by-step guidance, and embedded dashboards. Analysts can now explore “what-if” configurations, drill into warehouse error rates, and model operational changes in minutes instead of days.
Healthcare
BaptistCare, a major Australian nonprofit supporting over 24,000 people through aged care, in-home services, and senior housing, adopted a modern GenBI platform to streamline operations and boost frontline impact. Using the AI assistant for summarizing reports and finding information across systems let BaptistCare staff save two to eight hours per week.
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Last thoughts
Generative BI fundamentally changes how companies find and use business insights. Instead of analytics being siloed with technical experts, generative BI uses LLMs, agentic orchestration, and integration with enterprise data to put real analytical power in the hands of everyone. Teams get answers and context right when they need them, speeding up decisions, breaking down bottlenecks, and creating a culture where data-driven thinking becomes the default mode.
Of course, this transformation is as much about trust and enablement as it is about technology. Organizations that invest in strong data governance, transparency, and user empowerment will not only accelerate adoption but also build lasting confidence in AI-driven insights. In a nutshell, every question can become an insight, and every user an analyst.
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