Pharma AI – Implementation Case Study at Takeda Pharma

Background

Takeda is a global biopharmaceutical company originating from Japan and currently operating in over 80 countries around the world. The company’s annual revenue in 2022 exceeded 4 billion yen. Takeda focuses its activities on drugs used to treat diseases related to the digestive system, oncology, neurological diseases, and plasma-based therapies. Additionally, the company is committed to developing new treatment methods in the areas of immunology, hematology, and metabolic disorders.

The implementation project described in this case study pertained to the Polish branch of the Takeda concern. Given its scale and the specificity of the industry, the company is involved in multi-million tenders. The documentation accompanying these processes is often extensive, characterized by complex formulations and difficult terminology. Consequently, analyzing all documentation and managing further information throughout the tender presents numerous challenges.

Challenge Faced

The most common problems faced by the team of the Polish Takeda branch before implementing the new solution include:

Long, complex, and complicated bidding process.
Complexity and incomprehensibility of the language in the tender documentation.
Failure to provide adequate protection for sensitive data.
Ineffective data processing.
Data scattering.

Solution Provided

Due to the high degree of complexity of the tender procedure and the large amount of accompanying documentation, the decision was made to use artificial intelligence. Our solution has been designed to effectively manage requests for proposals and facilitate the decision-making process in a complex tender procedure. The new solution is based on the Salesforce CRM system. It allows for the storage of contractor data and combines artificial intelligence responses with existing contractor data. Moreover, it ensures integration with other external systems, including a data processing system and an AI system:

Analysis of RFPs: The system enabled automatic analysis of RFPs, allowing for the identification of key information and criteria such as offer size, completion date, formal requirements, and more.
Extraction and preliminary assessment of criteria: our solution used AI to extract relevant parameters from requests for proposals and to make a preliminary assessment of compliance with the given parameters.
Quick analysis and reporting: after analyzing queries, the system provided quick analysis with a summary of the most important parameters, which allowed users to quickly understand the essence of the query and make appropriate decisions.
Offer Validation: our solution enabled monitoring the compliance of offers with set parameters and identifying possible deviations, which allowed us to quickly respond to potential problems.

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Outcome

The benefits our client noted after implementing the new system include:

Time saving: Automation of the request for proposal analysis process allowed the company to save valuable time for employees, which could be allocated to more strategic tasks.
Increased efficiency: The new tool led to faster and more precise analysis of requests for proposals, contributing to increased efficiency in the bidding process.
Improved decision quality: Thanks to the ability to quickly analyze key parameters of requests, our client can make more accurate decisions and better adapt to market requirements.

It is worth emphasizing that this was one of the first commercial implementations of systems based on artificial intelligence for clients in the biopharmaceutical industry of this type. Currently, the system is in the maintenance phase, and users analyze new documents every day. It should be emphasized that the tool has several improvements that allow for maximum utilization of the advantages of artificial intelligence. These improvements include:

Automation that clarifies the accuracy of the information found.
Automation for indicating a fragment of a document with selected important fragments of text.
Automation for paralel processing of many documents at the same time.
Automation for loading document scans and correcting typos.
Automation for assessing the results obtained.

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Conclusion

The implementation project for Takeda Pharma is an excellent example of using advanced technologies and artificial intelligence capabilities, such as natural language processing and data analysis, to improve key business processes.

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Case study of Salesforce Integration with an AI Tool at Takeda, a Biopharmaceutical Company

Background

Challenge Faced

Solution Provided

Outcome

Conclusion

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