Generative BI: Or How Generative AI Changes Business Intelligence

GenAI democratizes tech and access to information. New open-source models, like DeepSeek R1, could democratize GenAI itself. What does this mean for companies? Their data science teams will finally run models locally. Even small firms will be able to afford it since there’s no longer a need to spend gazillions on GPUs. The impact on analytical functions – BI in particular – could be huge.

This article covers GenBI: the new, rapidly growing practice of using generative AI tools in business intelligence. We’ll also look at where AI tech is heading, the biggest trends to watch, and how it all plays into changing the work of analytics experts across industries.

Let’s start with the sector overview.

The Promise of Generative Artificial Intelligence

The U.S.-China LLM drama has once again spiked global interest in generative AI. But it’s not like people were forgetting about it. Way before DeepSeek flooded the headlines, GenAI’s economic potential was pegged at 2.6 to 4.4 trillion annually. That’s a staggering 15-40% boost to AI and analytics value. Once companies operationalize the algorithms into their workflows, they’re expected to see 60-70% productivity gains.

GenAI was and remains the key trend in the AI space. However, there are also quite a few emergent developments within the subsector that might become more or less mainstream in 2025. Here are some of them:

Agentic AI, which we’ve covered in detail, keeps rising. People are on board with agents’ utility, and big names continue to throw their hats into the ring. Interest, advertising, and adoption continue to grow.

Inference-time compute models are also gaining traction and showing promise. What are they? Inference-time compute just means giving the trained AI more time and energy to think before responding. It’s a shift in how models are made smarter.

While the bulk of compute resources were traditionally allocated to the training phase, algorithms can now also be enhanced during inference – the stage when an LLM actually interacts with people. The logic is simple: giving the model the possibility to mull over problems and try different outputs before replying will make its predictions more accurate. Combined with traditional methods, this new approach could lead to significantly smarter models.

Another logical trend: even bigger LLMs. This is a safe bet, given the history of transformer evolution. After the Stargate announcement, we’re waiting to see even crazier sums thrown into AI model training and expansion. If current frontier algorithms like GPT-4 have about 1-2 trillion parameters, we expect the next generation to have upwards of 50 or even 100 trillion – especially since the precedent has already been set.

On the flip side, smaller, distilled algorithms are also likely to receive recognition. As seen with R1, they can quite consistently rival the big dogs in specialized tasks.

The takeaway: generative AI models are getting better, and adoption will rise. For use cases like BI, this means even small companies that had little to no analytical capabilities will now have the chance to utilize data to the max and unlock AI-enabled, data-driven insights at a fraction of the cost and without substantial effort.

Here’s where we come to something called GenBI.

What is Generative Business Intelligence?

While traditional BI focuses on analyzing existing data to understand the past and predict the future, GenBI uses LLMs’ power to create new content, insights, and even solutions. It goes beyond reporting and forecasting to actively produce things like:

• Synthetic data. GenBI can create realistic artificial data objects and datasets that can be used for testing, training, and exploring “what-if” scenarios – without compromising sensitive real-world information.

• Automated narratives and explanations. Instead of just presenting charts and graphs, GenBI can generate natural language explanations of the data, summarizing key findings and trends in an easy-to-understand way.

• Personalized dashboards and reports. GenBI can tailor how information is presented to individual LOB users, emphasizing parts most relevant to their specific roles and needs.

• Code generation for data analysis. While this use case still requires caution and careful supervision, GenBI can already help automate parts of the data analysis process by generating code (e.g., SQL queries, Python scripts) to perform specific tasks.

GenBI Types

Now, let’s discuss how, in general, these capabilities help elevate the descriptive, predictive, and prescriptive business intelligence processes.

Descriptive BI

This BI summarizes past and present data to show what happened and what’s currently happening. Reports, dashboards, and visualizations that track KPIs, identify trends, and compare performance. It answers “What happened?” and informs immediate actions. Here, generative AI can automatically create narratives and explanations of the data. So, instead of just seeing a chart showing, say, a sales dip, the LOB user gets a written summary explaining the details and likely reasons for the dip, all based on data.

Predictive BI

This is where statistical and ML models are used to forecast future outcomes. By analyzing patterns in historical data, the algorithms can predict potential events like customer churn or sales. They try to answer “What might happen?” and enable proactive decisions. In this case, GenBI can help by creating synthetic data (when the existing datasets aren’t sufficient to train an effective predictive model) and generating code (for preprocessing, training, or evaluations, etc.), significantly expediting the entire ML development lifecycle. 

Prescriptive BI

This is the most advanced type of BI, where tools are used to determine optimal actions to achieve desired outcomes. Using various types of simulations, models analyze scenarios and suggest the best course of action. They try to answer the question, “What should we do?” and GenBI can help by generating suggestions. For example, after a predictive model forecasts a sales drop, GenBI could produce several potential solutions, such as launching limited-time discounts or bundling several products together, along with explanations of their likely impact. It could even generate marketing copy for a new promotion designed to counteract the predicted sales decline.

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Most Common GenBI Use Cases

Let’s now look at how GenBI is used across BI and analytics. We won’t try to cover every possible application – that would take an entire book – but we’ll talk about the main ones that can make the biggest impact.

Data analysis and mining

Data analysis and mining are definitely among the top use cases. According to research from Slalom, 52% of GenBI-using organizations implement these capabilities. Analysts can now use casual language to create queries, explore datasets, automate pattern detection, and generate insights. Then, in the same tone, they can ask follow-up questions based on initial findings until they get to the exact specific solutions they’re after.

Suppose you’re running a retail company, and your BI experts use a model to understand why online sales of a product dropped last month. Instead of writing complex SQL queries for hours, they can simply ask the GenAI system, “Why were the sales of the ‘Red Funky Sweater’ so low last month?” The AI then scans sales data, traffic, social media sentiment, and competitor pricing and replies, “Sales dropped due to a competitor’s discount, negative reviews on sizing, and less website traffic from social media campaigns.” 

Forecasting

With GenBI, complex predictive models can be turned into plain language. This democratizes predictive analytics and allows LOB users to get insights without technical expertise.

Imagine a restaurant that wants to predict demand for a new menu item. They turn to GenAI, which looks at historical sales, weather forecasts, and local events, then gives a plain-language forecast: “Demand for the ‘Supa-Dupa Spicy Sandwich’ could be very high this football weekend, especially Saturday afternoon. Afterward, it recommends increasing the typical stock by 20% and scheduling more staff.

Automated reporting and visualization

LOB users can now generate full dashboards with natural language prompts. And once the generative model gets to know them, it can even automatically recommend relevant reports based on their role.

For instance, a sales manager needs a weekly report on regional performance. They type, “Generate a sales report for the Northeast region for the past week, showing sales by category and top-performing reps.” GenBI quickly creates a professional, slick-looking dashboard with charts, graphs, and key metrics.

So, all in all, research suggests that generative AI is transforming BI through:

• Accelerated insight generation
• Increased accessibility for non-technical users
• Automation of routine analytical tasks
• Enhanced depth of analysis
• Better integration of diverse data sources

How Exactly Does Generative BI Work?

Now that we understand the uses, at least on a high level, let’s talk about the inner workings of these tools and go through the entire process. It all starts with user input. Let’s say you ask a question or ask for a data visualization of some sort. No need to write SQL queries, of course, everything is in plain language. So, you go, “Show me a bar chart showing ROI for each of our recent campaigns, segmented by region.” 

Then, the GenBI system activates its NLP capabilities to derive your intent from this sentence. It breaks the request down to identify its key components, such as entities and conditions. To be more precise, it applies something known as tokenization and entity recognition – tech speak for splitting the text into individual words and identifying named entities. And this enables the AI to understand your request in the context of available data.

Then, the tool translates your prompt into a structured search or database query, depending on where the needed data is stored and its format. For structured environments, it might create an SQL query. If the data is unstructured, it may use vector search or NoSQL techniques. The tool can also pull and combine data from multiple sources, including custom internal reporting tools and enterprise platforms like SAP or Salesforce if that’s what the task requires.

When that’s done, an AI-powered analytics engine has to process the retrieved data. This might include various calculations and statistical operations such as pattern recognition, predictive modeling, and causal inference. In the end, if the model is advanced enough, it can extract highly valuable insights from the dataset, including trends, anomalies, and even factor correlations that could significantly enhance a company’s business decisions.

Finally, the data is visualized. Given the initial request, you’ll get a slick-looking bar chart showing ROI for each campaign, segmented by region. Besides that, some additional visualizations could be added, like a map showing performance by region, as well as natural language summaries: “ROI increased by 15% in Q3 compared to Q2.”

But that’s not all. Beyond reporting, GenBI offers recommendations and decision support based on the visualized data. For instance, if the chart reveals low ROI in a specific region, it could suggest targeted marketing campaigns to enhance performance or even point out how to fix potential issues with local distribution. Furthermore, it enables scenario planning, allowing you to explore questions like, “How would ROI be affected if we doubled the marketing budget in region Z?

So, in broad terms, a GenBI system could be comprised of large language models that do the NLP, knowledge graphs that enable business context understanding, and AutoML that facilitates the fast creation of forecasting and anomaly detection algorithms. In addition, there could be elements like semantic search and vector databases, which enhance retrieval from unstructured data. Finally, cloud technologies can be incorporated, too, as they simplify the integration with enterprise systems.

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The Challenges of Generative BI Implementation

The adoption of the technology is nascent. So, despite all the promise, there are challenges as well. Data quality is one of the biggest. 

As we’ve stressed before, AI models are only as good as the data they’ve been fed, and GenBI is no exception. Fragmented, inconsistent, or incomplete datasets can lead the tool to inaccurate, vague, and sometimes plain wrong conclusions. However, proper data governance is hard and requires specific skills, which many companies still lack.

Another hurdle is integrating GenBI with existing systems. Many organizations still rely on a mix of legacy databases, cloud platforms, and third-party applications, which could severely complicate data extraction and synchronization for a generative BI tool. To fix this, extensive customization, API development, and ongoing infrastructure maintenance may be required.

Security and compliance are also major concerns. If a GenBI system is entrusted with large amounts of sensitive information, the company must ensure strict access controls, encryption mechanisms, and audit trails are in place. Furthermore, output verification procedures need to be implemented so that potential misinformation – GenAI is notoriously prone to hallucinating – doesn’t lead to costly fines or damage to trust.

Last but not least, scalability and performance bottlenecks could render GenBI tools ineffective if the infrastructure isn’t optimized. This is particularly true for enterprises handling massive datasets. Processing complex queries across distributed systems requires significant computational power. And, even if a company has the resources, the response times could be too long for the system to be useful. To be valuable, GenBI must evolve and be able to handle growing volumes of data without sacrificing speed.

Final words

GenBI tools open many possibilities but should be approached with caution, especially by organizations operating in heavily regulated industries. To fully realize their benefits – empowering every link of the analytics chain – organizations must establish robust data infrastructures, caching mechanisms, effective indexing strategies, and more.

The preparation is thorough, but it is essential for both security and smooth performance as usage scales. At Symphony Solutions, we cover every facet of the AI/ML development lifecycle and have extensive experience building GenAI-based tools. If you’re looking to enhance your business intelligence with a more efficient, data-driven approach, contact us now – we’ll help you get there fast.