Agenda

Business Intelligence was designed around a simple assumption:
humans are theprimary consumers of data. Analysts write queries, dashboards present metrics,and users interpret the results.

AI agents change that model.
As organizations move from dashboards and copilots toward agentic analytics, AIsystems are becoming direct consumers of enterprise data. The challenge is nolonger just how to connect an LLM to a database, but how to give AI the right
context, semantics, permissions, and analytical boundaries to work reliably.
This session will explore how Model Context Protocol (MCP) can act as a structured layer between AI agents and enterprise analytics platforms, enablingagents to discover available data, access governed analytical capabilities,interpret business context, and perform increasingly complex analytical tasks.

The focus will be on the transition from traditional BI to agentic analytics:
BI → Natural Language Analytics → AI Agents → Governed Analytical Actions
Using real customer problems and practical use cases, the session will examine scenarios such as:

• Investigating why a KPI changed, rather than simply reporting the number
• Reasoning across multiple systems such as CRM, ERP, marketing, support,and operational data
• Allowing agents to work with semantic models rather than raw database schemas
• Propagating user identity, permissions, and security controls through AI-driven interactions
• Determining what should actually be exposed through MCP: datasets, metrics, semantic models, tools, or actions
• Moving from “answer this question” toward “investigate this problem” and eventually “take an approved action”

ClicData will be used as a practical reference architecture, including dataconnectors, DataFlows, semantic modelling, AI-assisted exploration, AIDashboard Builder, and MCP.

The session will not focus on MCP as a protocol specification or on AI hype. Instead, it will examine the architectural decisions and real-world challenges that emerge when existing BI environments need to support AI agents.

The central question is:
If AI becomes the next major consumer of your enterprise data, is your BIarchitecture ready for it?