The Spark Blog

The Shift in AI for Business Intelligence

For years, enterprises have relied on traditional AI and Machine Learning (ML) approaches to enhance Business Intelligence (BI) projects. These projects demanded significant investment in data preparation, cleansing, and structuring before AI could generate insights. However, with the rise of Generative AI (GenAI) and Large Language Models (LLMs), a new paradigm is emerging—one that transforms how enterprises interact with their data.

Let’s compare the traditional AI-driven approach to BI with the new GenAI-based method and examine the pros, cons, and long-term impact.

Option 1: Traditional AI & ML for BI

How It Works?

1. Enterprises hire multiple data scientists who conduct in-depth research on various internal data sources.
2. These experts determine which data to ingest into a central data repository, often a meticulously curated data lake.
3. The data lake is built with a strong emphasis on cleanliness, structure, and precision, ensuring that AI models have the best possible dataset to analyze.
   
4. BI applications generate reports and insights based on the structured data.

Pros of Traditional AI for BI

• High Data Quality: With rigorous cleansing and structuring, the data used is reliable and well-organized.
• Domain-Specific Expertise: Data scientists tailor the dataset to business needs, ensuring relevant and valuable insights.
• Predictable Performance: Since the data is predefined and structured, AI models operate with a controlled and predictable level of accuracy.

Cons of Traditional AI for BI

• Time-Consuming & Expensive: Building and maintaining a data lake requires extensive human resources and technical expertise.
• Data Silos: Not all enterprise data is included in the data lake, which can limit insights and prevent real-time decision-making.
• Slow Adaptation: If new data sources emerge, integrating them into the BI system requires additional effort, slowing down responsiveness.

Option 2: GenAI-Powered BI

How It Works

1. Instead of manually curating a data lake, enterprises select a powerful GenAI foundation model.
2. The BI project manager connects multiple internal and external data sources to the LLM using Retrieval-Augmented Generation (RAG) or other real-time data integration techniques.
3. Data remains decentralized, and the GenAI system dynamically retrieves and processes relevant information on demand.
4. Users simply query the system and receive immediate, context-aware insights from a much broader range of data.

Pros of GenAI for BI

• Instant Data Accessibility: No need for extensive data lake preparation; all relevant data sources can be tapped into on demand.
• Scalability & Flexibility: The system can easily integrate new data sources without requiring a major overhaul.
• Faster Insights: Business users get answers in real-time, without waiting for pre-defined data structures or cleaning processes.
• Lower Costs: Reduces the need for extensive data scientist involvement, making BI projects more cost-effective.

Cons of GenAI for BI

• Data Quality & Hallucinations: Since the model relies on multiple, sometimes inconsistent sources, responses may not always be 100% accurate.
• Security & Compliance Risks: Ensuring sensitive enterprise data remains secure while using GenAI requires robust access control and governance.
• Less Predictability: Unlike structured AI models trained on fixed datasets, GenAI systems generate responses dynamically, making quality control more challenging.

Which Approach Will Win in the Long Run?

While both approaches have merits, the vast majority of BI projects will likely gravitate toward GenAI-powered BI in the long run. The ability to integrate multiple data sources dynamically, reduce reliance on pre-built data lakes, and provide real-time insights offers a compelling advantage over the traditional approach.

That said, enterprises adopting GenAI for BI will need to invest in:

• Data validation techniques to mitigate hallucinations and errors.
• Security frameworks to protect sensitive business data.

Traditional data science often involves reducing data to maintain structure and accuracy. However, this process can lead to the loss of vital and even critical data points necessary for generating deep insights and comprehensive knowledge. In contrast,



GenAI thrives on vast, interconnected datasets, ensuring that no valuable information is overlooked in the pursuit of intelligence and decision-making. This enables the generation of insights and knowledge that would be impossible to achieve using traditional methods.

Ultimately, GenAI is not just a more efficient tool for BI—it represents a fundamental shift in how enterprises interact with their data. By embracing this new paradigm, businesses can unlock unprecedented levels of insight, agility, and competitiveness.