AI Powered BI: Can LLMs REALLY Generate Your Dashboards? ft. Michael Driscoll

The Evolution of Business Intelligence: From Clicks to Code

Business Intelligence is experiencing a fundamental shift from traditional drag-and-drop interfaces to code-based approaches, accelerated by the rise of large language models (LLMs). This transformation promises to make data analytics more accessible while maintaining the precision and version control benefits that come with code-based solutions.

Understanding BI as Code

BI as code follows the same principles that revolutionized infrastructure management with infrastructure as code. Instead of configuring dashboards through complex user interfaces, the entire BI stack—data sources, models, semantic layers, and dashboards—exists as declarative code artifacts in a GitHub repository.

This approach offers several advantages:

• Version control: Every change is tracked and reversible
• Collaboration: Teams can review and contribute through standard development workflows
• Expressiveness: Code provides far more flexibility than any UI could offer
• AI compatibility: LLMs excel at generating and modifying code

The Role of LLMs in Modern BI Workflows

Large language models are particularly well-suited for BI as code frameworks. Unlike low-code or no-code interfaces that were trending a few years ago, English has become the universal interface. LLMs can take natural language prompts and generate the necessary code to build metrics, dashboards, and analyses.

English vs SQL: Finding the Right Balance

The debate between using natural language versus SQL for analytics reveals interesting nuances:

Local Development with DuckDB

DuckDB has emerged as a powerful engine for local BI development. By embedding DuckDB directly into BI tools, developers can:

• Work with multi-million row datasets on their local machines
• Get instant feedback on metric changes
• Avoid cloud processing costs during development
• Maintain complete data privacy

The combination of DuckDB's performance and BI as code principles creates a development experience similar to modern web development—write code, see immediate results, iterate quickly.

MCP Servers: Bridging AI and Data

Model Context Protocol (MCP) servers represent a new paradigm for AI-data interactions. Instead of developing separate integrations for each service, MCP provides a common interface that allows AI assistants to:

• Query databases directly
• Access real-time data
• Generate insights without manual data export
• Maintain context across multiple queries

Practical Implementation: From Data to Dashboard

The modern BI workflow demonstrates impressive efficiency:

1. Data Import: Connect to data sources (S3, local files, databases)
2. AI-Generated Metrics: LLMs analyze schemas and suggest relevant metrics
3. Code Generation: Create YAML configurations for metrics and dashboards
4. Instant Preview: See results immediately with local processing
5. Iterative Refinement: Use natural language to adjust and improve

The Future of BI: Notebooks, Dashboards, or Both?

The evolution of BI interfaces suggests multiple modalities will coexist:

Challenges and Considerations

Despite the excitement around AI-powered BI, several challenges remain:

Best Practices for AI-Powered BI

To maximize success with AI-powered BI:

1. Follow conventions: Use established patterns that LLMs recognize
2. Document thoroughly: Quality documentation improves AI performance
3. Start with schemas: Let AI infer from structure before accessing data
4. Maintain human oversight: Verify generated code and results
5. Iterate quickly: Leverage local processing for rapid development

Looking Ahead

The convergence of BI as code, powerful local databases like DuckDB, and sophisticated LLMs creates unprecedented opportunities for data analysis. As these technologies mature, we can expect:

• More sophisticated metric generation
• Better handling of complex business logic
• Seamless integration between exploration and production
• Continued importance of human expertise in context and validation

The future of business intelligence isn't about choosing between humans or AI, dashboards or notebooks, clicks or code. It's about combining these elements to create more powerful, flexible, and accessible analytics experiences for everyone.