Case Study
Turning Enterprise Data into Faster Decisions
A leading automotive manufacturer needed to reduce the time business analysts spent searching, downloading and preparing data across multiple internal and external sources. Infosys developed an agent-driven AI platform that converts natural-language questions into automated data discovery, analysis and visualization, helping users move from manual data preparation to faster, more consistent insight generation.
Key Challenges
Too Much Time Spent Preparing Data
Analysts relied heavily on Excel and could spend 50–80% of their time preparing data instead of analyzing it.
Data Spread Across Multiple Sources
Information was distributed across internal and external systems, requiring repetitive downloads, merging and manual preparation.
Slow and Inconsistent Insight Generation
Manual processes increased turnaround time and made analytical outcomes dependent on individual expertise and advanced Excel skills.
Facing the same data challenges?
Talk To ExpertsThe Solution
Orchestrating AI Agents from Question to Insight
Natural-language Data Discovery
- Converts business questions into automated data discovery, analysis and visualization workflows.
- Reduces the need for users to manually search, download and combine information from multiple sources.
- Resolves ambiguity in user queries to support more consistent and validated outputs.
Multi-agent Insight Generation
- Coordinates specialized probing, orchestration, sales, analytics and charting agents across the workflow.
- Manages the journey from user query through data retrieval and analysis to final insight delivery.
- Generates structured outputs including pivot tables, charts and formatted Excel reports.
Enterprise-ready AI Foundation
- Uses an enterprise-approved large language model hosted through AWS Bedrock.
- Maintains conversational context and session state to support multi-step and multi-threaded interactions.
- Provides a web-based conversational interface through which users can interact with the agent ecosystem.
From Manual Data Preparation to On-demand Intelligence
- AI agents reduce repetitive preparation work, giving analysts more time to interpret information and support business decisions.
- Natural-language access lowers the barrier to enterprise data and reduces dependence on specialist analytical skills.
- Coordinated agents create a repeatable path from business question to structured insight, improving consistency across teams.
Potential Outcomes
25.3 K
active Excel users potentially in scope
~300K hours/year
of potential efficiency
~US$15M/year
potential productivity value at full reach
~US$3M/year
potential value at 20% realization
Creating a Foundation for Enterprise Insight at Scale
The agent-driven platform creates a more direct path between enterprise data and business decision-making. By automating discovery, analysis and visualization, it can reduce reliance on repetitive manual preparation while making insights more accessible to a broader user base. The coordinated agent architecture also provides a reusable foundation for extending AI-assisted analysis across additional data domains, workflows and business teams as adoption grows.
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