Uses of Generative AI in BI

5 Main Uses of Generative AI in Business Intelligence & Data Analytics

Generative AI is rapidly transforming the landscape of business intelligence (BI) and data analytics, marking a pivotal shift in how companies operate and drive profitability. With an expected increase in data generation—from 1% a few years ago to an estimated 10% by 2025 according to Gartner—this technology offers vast potential for automation, personalization, and efficiency. This article will explore five key applications of generative AI in BI and data analytics, showcasing real-world implementations.

Read More: Emerging UI/UX Patterns in Generative AI

5 Main Uses of Generative AI

1. Automate Analytics

  • Challenges in Traditional Analytical Processes: Traditional analytics often involve repetitive, time-consuming tasks such as data collection, cleansing, and initial analysis which require substantial manual effort. These processes can be prone to human error and are typically resource-intensive, demanding significant coding expertise for script creation and maintenance. Additionally, any changes in analytical strategies necessitate script revisions, further adding to the workload and potentially delaying insights.
  • Role of Generative AI in Automating Data Extraction and Analysis: Generative AI revolutionizes this paradigm by enabling the automation of these tedious tasks. It allows for the dynamic generation of scripts and can adapt to new analytical requirements without manual intervention. This technology uses natural language processing (NLP) to understand user queries and machine learning (ML) to automate complex data operations, making analytics accessible to users without technical backgrounds.
  •’s Database Query Automation: exemplifies the application of generative AI in automating database queries. It enables users to interact with databases using conversational language, which the system then translates into executable SQL code. This process not only simplifies data querying but also enhances the efficiency of extracting insights by minimizing the time spent on query formulation and execution. Such automation extends the capabilities of data teams, allowing them to focus on higher-value analytical work.

2. Simplifying Data Preparation

  • The Challenge of Complex Data Preparation Tasks: Data preparation is arguably one of the most cumbersome aspects of the analytics process, involving multiple stages like data collection, cleansing, and structuring. Traditionally, this has required skilled data scientists to manually handle the data, which is not only slow and expensive but also susceptible to inconsistencies in data quality and relevance.
  • Streamlining with Generative AI: Generative AI tackles these challenges head-on by automating the data preparation phases. It can perform tasks such as data tagging, segmentation, classification, and even data cleansing and enrichment automatically. This not only speeds up the process significantly but also improves the quality and consistency of the data prepared for analysis.
  • Snorkel’s Implementation of Vertex AI for Data Extraction: Snorkel has innovated in the field by using Vertex AI to enhance data extraction processes. Their approach involves using generative AI to programmatically label data, particularly from unstructured formats like PDFs, HTML, and text documents. This method reduces the need for manual data labeling, which is often error-prone and inefficient. By automating data preparation, Snorkel helps organizations streamline the ingestion and processing of large datasets, facilitating quicker and more reliable analytics.

3. Enhancing Predictive Analysis

  • Understanding Predictive Analysis Challenges: Predictive analysis involves using historical data to forecast future events, which is inherently challenging due to the complexity of selecting appropriate models, the vast amount of data required, and the need for high accuracy in outputs. Traditional methods often fall short in terms of scalability and adaptability to new data, leading to less reliable predictions.
  • Improving Accuracy with Generative AI: Generative AI improves the predictive analytics process by enabling more accurate and dynamic model training. It can handle large datasets with ease and adjust models in real-time based on incoming data. Additionally, generative AI can generate synthetic data to test predictions under various scenarios, enhancing the robustness of predictive models.
  • Google’s AI Predicting Cardiovascular Risks: A notable example of generative AI in predictive analysis is Google’s use of AI to assess cardiovascular risks through retinal scans. This AI model successfully predicts cardiovascular issues by analyzing images of the retina, demonstrating a non-invasive, highly efficient alternative to traditional blood tests. The model’s ability to predict accurately in 70% of cases, compared to more conventional methods, showcases the potential of AI in transforming predictive analytics.

4. Effective Risk Management

  • Recognizing the Importance of Risk Evaluation: Effective risk management is crucial for any organization aiming to navigate uncertainties and mitigate potential threats that could impact operational efficiency and profitability. Traditional risk management involves identifying, assessing, and prioritizing risks using standardized methods, which can be time-consuming and often lacks the flexibility to adapt to new threats quickly.
  • Generative AI’s Contribution to Risk Identification and Mitigation: Generative AI transforms risk management by automating the detection and analysis of potential risks, thus speeding up the decision-making process. It can simulate various scenarios to predict outcomes and recommend mitigation strategies, making it possible to proactively address risks before they materialize. This capability is particularly valuable in dynamic environments where risk factors can change rapidly.
  • Stripe’s Utilization of GPT-4 for Fraud Detection: Stripe has leveraged GPT-4, a sophisticated AI model, to enhance its fraud detection capabilities. By integrating this AI into their systems, Stripe can analyze vast amounts of transaction data in real-time to identify patterns that may indicate fraudulent activity. This proactive approach not only helps in mitigating immediate financial risks but also in refining their fraud detection algorithms continually, thereby improving their overall security infrastructure.

5. Revolutionizing Visual Data Generation

  • Addressing the Limitations of Conventional Data Visualization Tools: Traditional data visualization tools often restrict users to predefined formats, which can limit the understanding and interpretation of complex data sets. These tools might lack flexibility in handling large-scale or rapidly changing data, making it difficult for users to achieve timely insights.
  • Advantages of Generative AI for Personalized Visualizations: Generative AI elevates data visualization by allowing users to create customized, interactive, and highly adaptive visual representations of data. This technology supports real-time data processing and visualization, making it easier to digest complex information and uncover hidden insights. Additionally, generative AI enables the generation of visual content directly from natural language queries, greatly enhancing accessibility for non-technical users.
  • Telus Leveraging for Customer Analytics: Telus has employed to transform their approach to customer analytics through advanced data visualization techniques. By using’s interactive visualization capabilities, Telus can better understand and utilize vast amounts of geo-temporal data to optimize service delivery and create effective marketing strategies. This not only improves customer satisfaction but also drives strategic business decisions based on deep, actionable insights extracted from their data landscapes.


Generative AI is set to redefine the scope and efficacy of business intelligence and data analytics across various domains. From automating mundane tasks and refining data preparation to enhancing predictive analytics, managing risks, and revolutionizing data visualization, this technology promises to streamline processes and boost operational efficiency.

Businesses like Stripe and Telus showcase the practical benefits of integrating generative AI into their operations, demonstrating how these tools not only support current needs but also innovate for future challenges. As the landscape of data-driven decision-making evolves, generative AI stands as a cornerstone technology that enables businesses to not only keep pace with changes but also to set the pace, making smarter decisions faster and with greater confidence.

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