Generative AI is transforming data analytics—not as a replacement for analysts, but as a powerful tool to speed up processes, improve data accessibility, and automate time-consuming tasks. While many organizations recognize the potential, figuring out where to apply it for real business impact is another challenge.

At Model8, we take a practical, results-driven approach to AI adoption. Instead of chasing hype, we focus on where AI can drive efficiency, improve decision-making, and automate processes. Here are six ways businesses are already using Generative AI in data analytics, plus best practices to help you get the most out of AI.

1. Code Generation: Speeding Up Development

One of the most immediate wins for Generative AI is in automating code generation. While it won’t replace well-structured, thoughtful development, AI accelerates the process by:

✔ Generating initial code templates for queries, scripts, and automation tasks.
✔ Refactoring legacy code—for example, converting Qlik Sense reports to Power BI by translating Qlik syntax into DAX.
✔ Creating reusable code snippets for common analytics tasks, reducing manual effort.

This speeds up development, helps teams standardize best practices, and allows engineers to focus on solving complex problems instead of writing boilerplate code.

2. Chatbots & Virtual Agents: Making Data More Accessible

Chatbots are no longer just for customer support. In analytics, they serve as interactive assistants that allow business users to:

✔ Query data conversationally, rather than navigating dashboards or writing SQL.
✔ Summarize reports and explain key metrics in natural language.
✔ Explore insights dynamically, adjusting queries in real time.

Cloud platforms like Databricks and Snowflake make it easy to integrate chatbots into analytics environments, while open-source frameworks like LangChain allow companies to build custom AI-powered assistants that fit their specific needs.

3. Data Governance: Automating Documentation & Compliance

Data governance is a major pain point for organizations, often requiring manual documentation and ongoing oversight. Generative AI helps automate and improve governance processes by:

✔ Automatically generating metadata and data lineage documentation.
✔ Detecting anomalies and inconsistencies in data models.
✔ Providing contextual explanations for metrics and calculations.

For organizations navigating strict compliance frameworks, AI-driven governance improves accuracy, transparency, and trust in data.

4. AI-Generated Visualizations: Faster, Smarter Reporting

Modern BI platforms like Power BI and Databricks AI/BI now allow users to generate visualizations with simple prompts. Instead of manually designing dashboards, you can:

✔ Ask AI to build reports using natural language commands.
✔ Generate professional-quality visualizations instantly.
✔ Optimize layouts for different audiences without manual tweaking.

This makes analytics more accessible, reducing reliance on technical teams and allowing decision-makers to interact with data more intuitively.

5. Automating Workflows: Reducing Manual Effort

Generative AI is now embedded in low-code automation platforms like Power Automate and Power Apps, allowing organizations to:

✔ Automate report generation and data summaries.
✔ Streamline notifications and alerts based on real-time data.
✔ Ensure insights reach the right stakeholders without manual intervention.

Instead of waiting for reports, teams get automated, AI-generated insights delivered directly into their workflow.

6. AI Agents: Handling Complex Analytical Tasks

AI agents take automation further by handling analytical tasks that require reasoning and adaptation. Unlike simple workflows, AI agents can:

✔ Break down analytical tasks into logical steps.
✔ Perform statistical analysis and generate insights automatically.
✔ Coordinate multiple AI models to process, analyze, and visualize data.

Agent frameworks like Mosaic, LangGraph, and AutoGen allow businesses to deploy custom AI-powered analytical assistants—but human oversight remains essential to ensure accuracy.

Best Practices for Using Generative AI in Analytics

AI is only valuable when applied strategically. To avoid common pitfalls and maximize impact, follow these best practices:

✅ Start Small & Scale Up – Test AI on well-defined use cases before expanding.
✅ Ensure Data Governance – Use AI responsibly to maintain accuracy and compliance.
✅ Keep Humans in the Loop – AI assists, but human validation is critical for decision-making.
✅ Leverage Existing Tools – Many platforms already have AI built-in—use what’s available.