Agentic AI is reshaping business intelligence (BI) by moving beyond traditional dashboards and static reporting to systems that understand natural language, unify structured and unstructured data, and autonomously generate insights.

By orchestrating LLMs with specialized agents, enterprises can access deeper, faster, and more contextual analytics while reducing reliance on technical teams and eliminating long BI development cycles. 

How Agentic AI Improves Business Intelligence

Business intelligence has evolved significantly with the shift to agentic AI. The core concept is the orchestration of a large language model (LLM) with many sub-agents working as specialized tools. With a multi-agent architecture, each AI agent has a specific task, such as data unification, report production, or analytics, and cooperates with others to fulfill users’ requests.

Generative AI and natural language processing (NLP), integrated with enterprise data, have transformed the user interface and experience. A user can communicate in their own language to interrogate enterprise data.

Secure and Scalable Agentic AI for Enterprise BI

As BI gains autonomous capabilities, secure-by-design governance becomes essential. Organizations need strong identity controls, guardrails against prompt injection, multimodal data protections, and continuous red team testing to deploy agentic BI safely.

With the right platform – such as Capgemini’s RAISETM – enterprises can industrialize agentic AI across cloud ecosystems , accelerate time-to-insight, and unlock intelligent, automated decision-making at scale. 

Use case: Migration from Tableau to Power BI using a generative AI–enabled Report Conversion Protocol accelerator

The client

A financial institution sought to accelerate the migration of 6,000 Tableau business intelligence reports to Power BI in a reliable, efficient, and cost-effective way.

Key challenges in tableau to power BI migration

  • Both Tableau and Power BI store the semantic (translation and governance) layer and reporting layer data in proprietary formats.
  • Power BI does not have a software development kit (SDK), nor an application programming interface (API) to allow programmatic, i.e., automated report generation. This makes migration projects more complex.
  • Manual replication would normally be used in this situation, but this is error-prone and time-consuming.

AI-Driven approach for BI report conversion

  • Automating the migration of Tableau reports to the Power BI format
  • Migrating the business intelligence semantic/data model and the reporting/visualization layers with a reliable, efficient, and cost-effective process.
  • Generating a migration summary report that documented the constructs identified in the Tableau source and the equivalents18 generated in the Power BI target for auditing and documentation purposes.

Business outcomes and efficiency gains

  • Executing a Gen AI–enabled Report Conversion Protocol-based migration of six reports – two simple, two medium, and two complex – as a proof of concept to demonstrate feasibility and effectiveness of automated migration. This led to a 50% saving in effort across all report types.
  • Developing an AI reader agent to process the Tableau source reports, extract relevant details, and save it in a common metadata layer.
  • Creating an AI writer agent to convert the details from metadata to the target format.
  • Using an LLM for the conversion of Tableau-calculated fields to the equivalent Power BI DAX expressions.
  • Generating a migration summary report to capture the overall migration activities for documentation as well as auditing.