Last updated on February 25, 2025
6 Use Cases for Generative AI in Data Analytics + Best Practices
By Kevin Lobo
In this blog, we break down six practical ways your peers are using Generative AI to get more value from their data. We also cover key considerations, best practices, and technology options for integrating Generative AI effectively.
If you know Generative AI has potential, but you are struggling to figure out real-world application in your work, here are six practical use cases to consider — plus key factors to keep in mind before you get started.
In this blog, we’ll cover:
Use Case #1 — Code Generation: How Generative AI Accelerates Development
A core Generative AI use case in data analytics is using large language models (LLM) to generate initial code, which accelerates the overall development lifecycle. Generative AI doesn’t replace well-structured, thoughtfully written code; but when used correctly, it helps teams work faster by generating template code or maintaining reusable repositories for common use cases.
Another practical application is converting legacy code to modern platforms, making migrations more efficient. For example, when moving from Qlik Sense reporting to Power BI, Generative AI can refactor proprietary Qlik syntax into DAX, automating the conversion of basic expressions and reducing the manual effort typically required.
Use Case #2 — Chatbots and Virtual Agents: How to Enhance Interactions with AI
If you’re considering adding a chatbot to your site to expand your customer service options, broad-based LLMs make the implementation and roll-out of chatbots far more accessible than in the past. But chatbots aren’t just for answering customer inquiries — a chatbot can be an internal tool that helps business users understand and explore their data more effectively.
Integrated into analytics platforms, these AI-powered chatbots can summarize dashboards, explain key metrics, and answer follow-up questions about the data. Unlike static reports, they allow users to query data conversationally, making it easier to extract insights without manually navigating dashboards or writing queries.
Cloud-based platforms like Databricks and Snowflake are rapidly building “data-in” features to deploy cognitive search services and off-the-shelf LLMs against your own dataset, so the barrier to entry in deploying an LLM-based Chatbot is becoming increasingly lower. You can integrate these chat bots into workflows via API endpoints or as native applications, depending on your cloud provider. If you prefer an open-source approach, frameworks such as LangChain offer another way to build AI-powered chatbots.
Use Case #3 — Data Governance: Using AI to Automate Documentation and Improve Trust
Major platforms like Databricks now integrate Generative AI into their governance tooling, automating metadata generation, improving data documentation, and tracking lineage more intelligently. These capabilities streamline traditionally time-consuming data governance tasks, helping you maintain robust data practices without sacrificing agility.
Beyond basic documentation, Generative AI helps document processes and improve quality assurance. It analyzes existing workflows, generates comprehensive documentation, and identifies areas for improvements. This is especially valuable when you’re building or updating data governance frameworks, ensuring consistency and completeness across your data ecosystem.
AI can also improve user trust — when someone questions a metric or analysis, Generative AI can quickly reference your documented data governance framework to provide clear, contextual explanations of data lineage, calculations, and business rules.
Use Case #4 — AI-Generated Visualizations: Creating Dashboards and Reports Faster
Modern BI platforms like Databricks AI/BI and Power BI have built in Gen AI capabilities that let you create and enhance data visualizations through natural language interactions. With simple conversational prompts, you can generate sophisticated visualizations and entire dashboard layouts in seconds. This goes beyond basic chart creation, letting you quickly iterate on designs and optimize layouts for different audiences and purposes.
Agentic AI tool Zenlytic take this further by integrating its AI analyst (Zöe), which not only create visualizations but also help interpret the data and suggest relevant insights. Meanwhile, Power BI’s Copilot changes how you interact with their data, offering AI-driven features to generate visualizations, create DAX expressions, and produce narrative summaries — all through natural language commands.
This generative AI-driven approach to UI creation saves time and makes self-services analytics more accessible, especially for those without deep SQL or visualization expertise. For example, you could ask, “show me monthly sales trends with year-over-year comparison,” and instantly receive professionally designed visualizations. This accessibility helps build a data-driven culture where insights are available to everyone, not just technical analysts.
Use Case #5 — Automating Workflows: How Generative AI Streamlines Data Processes
With workflow automation tools like Zapier, Power Apps, and Power Automate, you can now embed Generative AI directly into your existing business applications and workflows without complex development efforts. These integrations automate analytical requests, from simple data summaries to complex report generation, while maintaining your organization’s security and governance standards. Low-code platforms and API integrations make insights more accessible to business users.
The real power of these integrations comes from their ability to connect different systems and data sources seamlessly. Whether you’re generating weekly performance reports, creating data-driven email responses, or building interactive analytical applications, these workflows reduce manual effort while keeping insights consistent. You can automate workflows that monitor business metrics, generate analytical summaries with natural language explanations, and distribute insights through existing communication channels like email or Teams — ensuring stakeholders get the right information at the right time.
Use Case #6 — AI Agents: Handling Complex Analytical Tasks
AI agents go beyond workflow automation by handling complex analytical tasks that require reasoning and adaptation. While workflow automation focuses on structured processes, AI agents adapt dynamically to different analytical requests and refine their approach based on new data.
Agent frameworks like Mosaic, LangGraph, AutoGen, and CrewAI let you build specialized components that work together — just like human analysts solving complex problems. When properly implemented, AI agents break tasks into logical steps and execute them systematically. (This process should not rest entirely in the hands of AI – your oversight is essential to ensure accuracy and consistency.)
You can apply these frameworks within analytics platforms to handle routine analytical workflows. For example, when you’re investigating a business metric, an analytics agent can follow a structured approach: identifying relevant data sources, performing statistical analysis, and generating preliminary insights. You can enhance this workflow by deploying multiple specialized agents — one for data preparation, another for statistical analysis, and a third for visualization. Proper coordination is key to getting accurate results.
While AI agents adjust their approach based on initial findings, they should enhance — not replace — your analysis. The multi-agent approach streamlines routine analytical tasks and highlights key insights, but it works best when you set clear boundaries and use cases. If you’re implementing agent-based analytics, maintain oversight and validation processes to ensure the accuracy and reliability of automated analysis.
Generative AI Caveats: Common Challenges and Risks to Watch For
Generative AI offers significant potential, but you need to consider certain risks before integrating it into your data strategy:
- Basis of Evidence: Generative AI relies on LLMs and neural networks, which generate results through infinite permutations. This makes it difficult to explain why a specific code, design choice, or recommendation was made in each process.
- Security, IP, and PII Risks to Data: The ease of use in Generative AI is one of the biggest advantages — but also a risk. Without proper safeguards, sensitive, proprietary, or personal identifiable information can end up in a training dataset, creating compliance and security concerns.
- Accuracy: Public LLMs like ChatGPT pull from open-source data. In a private setting, their accuracy depends entirely on the quality of your training data and metadata. Poor data leads to poor results, so you need strong data governance to ensure reliable outputs.
- Cost: The barrier to entry has never been lower — but cost overruns have never been higher. Cognitive search with an LLM is resource-intensive, and if you’re not careful, deployment and scaling can drive up costs quickly. Monitor usage closely before rolling out AI in production.
- Rapid Evolution: The Generative AI landscape is constantly changing, with frequent updates to models, tools, and frameworks. This evolution can break workflows and require ongoing maintenance to keep your AI implementation secure and effective.
- Response Consistency: Even when you use the same inputs and data, foundation models can generate different outputs. This inconsistency is especially challenging for production use cases where reliable, repeatable results are essential.
Generative AI Tools and Platforms: Choosing the Right Technology
Most mainstream analytics tools offer Generative AI capabilities in different forms. The right platform depends on your organization’s needs — whether you’re looking for built-in AI features within your existing analytics stack or open-source frameworks for customization. Here’s a breakdown of the available options:
Generative AI Frameworks for Implementation: A Structure to Get Started
These frameworks help you build, deploy, and manage Generative AI applications by providing structure, automation, and integration capabilities. Whether you’re developing AI-powered chatbots, multi-agent systems, or analytics automation, choosing the right framework depends on your use case and technical requirements.
Tips for Success with Generative AI
Analytics8 Gen AI Offerings
No matter where you are on your generative AI journey, there is a strategy package that fits your needs.
