Organizations are tired. They're tired of sifting, sorting, and interpreting data to get no real tangible insights. That's the point of data, right? To understand why and what is happening. But it's evident that after years of being in the industry, it's not as easy as it should be. However, it doesn't need to be like that.
Generative Business Intelligence (GenBI) is the next exciting innovation, saving organizations from getting lost in a sea of data.
Let me explain.
What is generative BI?
Generative BI is an AI-powered evolution of traditional Business Intelligence (BI) that enables users to interact with data via a chat-like function using natural language. Unlike conventional BI, which relies on predefined reports and dashboards, generative BI allows users to ask direct questions and receive instant insights, visualizations, and recommendations.
Due to its ease of use, generative BI eliminates the frustrations associated with traditional BI. For example, how often are you asking that person 'in charge' of the data and BI in your business, "can you get me the numbers for the board meeting, please?" or "can you tell me where these MQLs came from?".
GenBI takes this barrier away and allows users at any level to query your data directly and ask, for example:
- "What were our top-performing products last quarter?"
- "How did marketing campaigns impact sales growth by region?"
- "What product is selling the best?"
Generative BI interprets these queries, pulls relevant data, and generates visual explanations in seconds. GenBI is putting the power of data back into the hands of everyone.
It's as easy as that.
Generative BI vs. generative AI
Generative BI and generative AI are not separate types of AI models but very similar. If you look at it in simple terms, Generative BI is a use case for generative AI.
Both generative BI and generative AI leverage AI, but they serve distinct purposes.
| Feature | Generative AI | Generative BI |
|---|---|---|
| Purpose | Creates new content (text, images, code, etc.) | Analyzes and visualizes data to generate insights |
| Function | Uses AI to generate text, images, and more | Uses AI to extract business insights and trends |
| Interaction | User inputs prompts to generate content | User queries data to receive insights |
| Output | Human-like text, images, or videos | Data visualizations, reports, and actionable insights |
| Example use case | Writing an article, generating an image | Identifying sales trends, forecasting revenue |
Think of it this way: generative AI helps you write a report; generative BI tells you what the report should say based on your data.
How does generative BI work?
Generative BI works similarly to other generative AI tools, such as ChatGPT or DeepSeek. Users simply use a natural language prompt, and the tool generates the answer using their data. The answer to the question may be a chart, table, or paragraph that most appropriately illustrates the prompt.
GenBI includes several key components:
- Natural Language Processing (NLP): Enables users to ask questions in plain language instead of complex query building.
- Real-time insights: Instantly generates responses, ensuring data remains relevant.
- Source traceability: Users can verify the origins of data, increasing trust.
- Simplified data structures: AI automatically interprets complex naming conventions and relationships.
- Secure access: Role-based permissions ensure data is only accessible to authorized users.
- Seamless integration: Works with existing BI tools, databases, and infrastructure.
What are the benefits of generative BI?
1. Data democratization - remove the barriers to data insight
The beauty of GenBI is its ability to remove the barrier to data access. Its easy-to-use functionality allows users at all levels to extract meaningful insights and even question their data. There is no longer the need to wait on internal specialists or data teams to get your insights.
2. Faster decision-making
As GenBI is designed to be used by all and generate insights in real-time, it removes the time spent on time-intensive analysis, a feature that traditional BI struggles to keep up with. In fact, in traditional BI, the barriers inherently set up by BI cause low adoption rates. According to a survey by BARC the percentage of employees actively using BI/analytics tools is currently 25% on average.
GenBI allows business leaders to make informed decisions quickly, adapting to market changes when needed.
3. Enhanced collaboration
Inherently, GenBI, being very user-friendly, removes the entry barrier and allows users at all technical levels to benefit from the software. Putting data back into the hands of everyone and working from the same data source, without data silos, naturally facilitates collaboration across teams and departments- everyone has access and can contribute.
The key benefits of enhanced collaboration with GenBI include:
- Consistency and unfragmented insights.
- Improved understanding of data.
- Cross-functional and departmental insights.
- Workflow optimization for enhanced productivity across teams.
Scalability & cost efficiency
A huge advantage of GenBI is its ability to scale while maintaining cost efficiency. Unlike traditional BI systems that can require costly infrastructure overhauls, GenBI flexes to your existing ecosystems. The focus is more on the insights and less on the infrastructure.
Some other benefits of GenBI regarding scalability and costs are as follows:
- Optimized resources: Automates query processing, reducing IT workload for higher-value tasks.
- Handles data growth: AI-driven optimization ensures scalability without performance issues.
- Flexible deployment: Incremental rollout minimizes upfront costs while enhancing AI capabilities.
- Lower operational costs: Reduces reliance on specialized BI staff and complex infrastructure, improving efficiency and accuracy.
Use cases of generative BI
The benefits of Generative BI are not restricted to specific users or industries, for example:
For business leaders and executives
- Smarter decision-making – Provides real-time insights into company performance.
- Strategic planning – Uses predictive analytics to forecast trends and mitigate risks.
- Cross-departmental collaboration – Ensures all teams operate from a single source of truth.
For product and operational teams
- Accelerates roadmap delivery – Integrates AI-driven insights into product development.
- Enhances efficiency – Automates manual reporting processes, freeing up resources for innovation.
- Improves customer experience – Enables teams to tailor services based on real-time data.
For data analysts and BI teams
- Reduces data wrangling – Automates tasks, allowing focus on deeper analysis.
- Ensures data integrity – Flags inconsistencies and improves data validation.
- Provides a unified data source – Eliminates discrepancies in insights across teams.
For end users and employees
- Empowers self-service analytics – Employees can access insights without technical expertise.
- Improves productivity – Automates data retrieval and visualization.
- Supports skill development – Helps employees build data literacy and make informed decisions.
Security in generative BI
As with all forms of AI, there is always a security concern associated with it, and generative BI is no different. However, these security concerns will be at the forefront of most (if not all) generative BI providers' minds when developing the functionality.
The security foundations of generative BI are built on:
Trust and transparency
- GenBI uses source traceability to show where data is derived from.
- Builds confidence with explainable AI, allowing users to verify insights.
Data control and compliance
- Role-based access ensures that only authorized personnel access sensitive information.
- Supports data residency and sovereignty by keeping data within regulatory frameworks.
Human oversight with AI assistance
- GenBI complements human expertise rather than replacing it.
- Users can monitor AI-generated insights and refine them as needed.
Advanced security protocols
- Encryption and activity logging track data access and prevent unauthorized usage.
- AI-assisted monitoring identifies anomalies and potential security threats.
Data for all: Why this matters now
In my experience, Business Intelligence, for years, has just been too complex. It isn't working. And when something does work, it needs to change.
The future of Business Intelligence is not just about analyzing data but about making it universally accessible and giving you, your C-suite, your admin team, everyone the insights they need.
As organizations collect more data than ever, the ability to extract insights without technical barriers is critical. With generative BI, every employee can harness the power of data, leading to more agile, competitive, and intelligent organizations.
Final thoughts
Generative Business Intelligence is the future. Bridging the gap between complex data and everyday users ensures that organizations focus on strategic decision-making rather than data wrangling.
Now is the time to embrace the change and leverage AI-driven analytics to stay ahead.
