Traditional reporting is built around predefined dashboards, fixed metrics, and manual navigation. But what if users could simply ask the business question they have in mind and let the system determine how to answer it?
This session explores the shift from standard reporting to intent-based, conversational analytics, where natural-language questions become the starting point for analysis. Instead of searching through multiple reports, users can ask questions such as, “Why did repeat customer contacts increase this month?” or “Which suppliers are driving the largest cost variance?”
We will look at how LLMs, analytics agents, structured data queries, semantic search, and RAG can work together to interpret user intent, retrieve the right information, perform multi-step analysis, and generate meaningful insights. Using practical ERP and contact-center examples, the session will illustrate how organizations can move beyond static dashboards toward a more intelligent, interactive, and context-aware reporting experience.
The goal is simple: stop making users search for reports—let them ask the data.
Key Takeaways
- Conversational Analytics: Understand the fundamental shift from static, predefined dashboards to intent-based natural-language data exploration.
- Architecting Insight Engines: Discover how LLMs, analytics agents, structured queries, semantic search, and RAG combine to perform multi-step analysis.
- Enterprise Case Studies: Learn from practical ERP and contact-center examples demonstrating contextual, context-aware reporting in production.
- Actionable AI Adoption: Gain practical blueprints to empower business stakeholders to query enterprise data directly and extract actionable insights.
Target Audience
- Anyone interested in imagining and implementing how AI can reshape enterprise data insights, business intelligence, and reporting.
- Data engineers, software architects, analytics professionals, and engineering leaders building AI-powered data products.
Prerequisites
- None. An open mindset toward exploring the intersection of AI, analytics, and enterprise data systems.