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CASE STUDY

Enterprise pharma achieved smarter IT license decisions and better vendor leverage using a custom build conversational chatbot

industry-iconCLIENT :Confidential
industry-iconINDUSTRY :Pharmaceutical
industry-iconDURATION :3 months
CLIENT :
Confidential
INDUSTRY :
Pharmaceutical
DURATION :
3 months

Business case

A global pharma leader’s digital team accurately tracked the utilization rates of all their software tools within a single source of truth. This resulted in a 10-20% optimization in software licenses usage and reduced operating costs. Read on to find out how.

The digital team was looking for a less manual and time-consuming workflow to view the usage report for their digital tools and software. This information was important to take strategic decisions on whether to continue renewing the license for a particular software/tool or discontinue it. As these decisions impacted cost optimization, they needed them to be accurate and quick.  

They were using an AI-powered chatbot trained on Natural Language Processing (NLP) to respond to queries and provide a quick response to critical technical information—such as internal application data, server details, ticket histories, support contacts for tools and software in use within the organization.

However, the team was still manually extracting software/tool consumption reports from their in-house tool. This report was in Excel format which contains basic details such as the name of the tool and consumption. There was no single, unified view to track software usage, license renewals, and SDLC compliance.

The client wanted to enhance the existing chatbot to give summary of the user reports as well along with the query responses. 

Our solution

After a thorough analysis of the business need, our solutioning experts built an enhancement to the existing chatbot. This approach established a single source of truth for all the data for easy user access, reducing the time consumed in manual data collation.

This enhanced module within the chatbot ingested and analyzed both structured data from chatbot queries and software management systems and unstructured data from internal tools and document repositories. It delivered concise, actionable summaries in response to user queries. This significantly reduced manual effort, accelerated information retrieval, and enhanced the overall user experience through a single conversational interface.

Complementing the enhanced chatbot, a comprehensive consumption analytics dashboard was also developed in a visual, user-friendly format. 

Challenges overcome

  • Training the LLM model to understand structured data and produce accurate summary reports. 

Benefits

  • Single source of truth for all software queries
  • Quick query resolution with Q and A interface
  • Accurate decision making on software licensing renewals
  • Saved time in manual data consolidation
  • Compliance status and workflow visibility via dashboard 

Results

Results