The rise of large language models (LLMs) has sparked well-deserved excitement around AI-driven analytics. The concept is enticing: just send your data to an LLM and receive instant, accurate insights. But the reality is far more complex. Challenges like business semantics, data privacy, AI hallucinations, and correctness persist, requiring a more thoughtful approach—one that integrates AI and business intelligence (BI) to create true Conversational Intelligence (CI).
Four Considerations of Conversational Intelligence
At the core of an effective AI-powered analytics strategy are four key principles—principles that guide DataChat’s approach to Conversational Intelligence:
1. Privacy First
Your data should never be sent to an LLM. Period. While AI can assist in analysis, business data should remain within secure databases. Instead of exposing sensitive information, DataChat’s architecture ensures that AI is used for logic and interpretation, while all data processing and execution happen within your database.
2. Enhancing, Not Replacing
AI should complement existing analytics workflows, not replace them. Dashboards and reports remain crucial, but business users need the ability to ask deeper questions beyond predefined visuals. DataChat enables users to explore hypotheses, iterate quickly, and uncover insights that static reports might miss, without disrupting existing workflows.
3. Context-Aware AI
Generic LLMs lack an organization’s specific business context. DataChat bridges this gap by combining the language skills of an LLM with an organization’s unique business terms, without requiring a predefined semantic layer and without training the LLM. This ensures that insights align with real business logic rather than generic assumptions while maintaining privacy.
4. Human-in-the-Loop
True self-service analytics isn’t about eliminating human oversight, it’s about making it more efficient. AI-generated queries and insights should always be validated, just as dashboards are vetted before informing decisions. The difference? Writing a query from scratch can take hours; validating an AI-generated query takes minutes. DataChat facilitates this process by exposing AI’s reasoning in plain English, making it easier for a business user to refine, validate, and trust results
AI + BI = CI: Making AI Work for Business Users
For AI-driven analytics to be successful, it must be explainable. Business users should understand why an insight was generated and how it was derived. Conversational Intelligence ensures that AI doesn’t operate as a black box but as a collaborative tool that works in harmony with business intelligence.
DataChat is designed to empower users with AI-assisted analytics while maintaining transparency and control. By combining AI’s language capabilities with BI’s structured decision-making, DataChat enables business users to interact with data conversationally, without requiring deep technical expertise.
The goal isn’t to replace analysts or dashboards, it’s to bridge the gap between business users and their data. Conversational Intelligence, powered by AI + BI, makes this possible by providing explainable, context-aware analytics. The future of self-service analytics is AI and humans working together to drive better, faster decisions.