What challenges did Sanofi face before AI implementation?

Reduce the time from data discovery to therapy. Improve efficiency across R&D, clinical, commercial, and manufacturing functions. Build and operationalize AI and machine learning capabilities quickly in a complex global pharmaceutical organization.

What was the impact of the AI solution?

Sanofi introduced eight products in 18 months through the Digital Accelerator. The accelerator enabled delivery of full API sets in less than 24 hours according to the article.

Source

ProductionEvidence: Medium65/100

Sanofi Digital Accelerator uses AWS SageMaker for predictive AI and deploys Bedrock for generative AI pilots

Use case typeDrug discoveryUpdated Jun 13, 2026

Sanofi launched its Digital Accelerator to speed up digital innovation across research and development, clinical, commercial, and manufacturing workflows. The initiative aims to shorten the time from discovery to therapy and improve patient, provider, and employee experiences through AI-powered solutions.

Organization: Sanofi
Industry: Pharma
Location: France
Published: April 2026

Reported outcomes

Time: Less than 24 hours

Time & speed

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Time: 24 hours

AWS Customer StoryApr 29, 2026Customer storyInferred claimMedium evidence strength

The accelerator enabled delivery of full API sets in less than 24 hours according to the article.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Sanofi
Provider: AWS
Maturity: Production
Linked source: AWS Customer Story

Sanofi explored generative AI with Amazon Bedrock, ran a hackathon to evaluate more than 10 projects, and deployed three models for employee productivity, streamlined business processes, and medical-legal content review automation

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 4

1Predictive maintenance and manufacturing efficiency
2Drug discovery acceleration
3Clinical trial efficiency

Sanofi standardized on AWS as the foundation for its Digital Accelerator and used a small agile pod model with product owners, scrum masters, engineers, data scientists, and business partners.
The team uses Amazon SageMaker to build, train, and deploy machine learning models and uses Amazon SageMaker Studio as a single web-based environment for end-to-end ML development.
The AWS stack described in the article also includes Amazon S3, AWS Lambda, and Amazon API Gateway for data storage, serverless compute, and API delivery.
Sanofi explored generative AI with Amazon Bedrock, ran a hackathon to evaluate more than 10 projects, and deployed three models for employee productivity, streamlined business processes, and medical-legal content review automation.

Technologies

Amazon SageMaker Amazon SageMaker Studio Amazon Bedrock Amazon S3 AWS Lambda Amazon API Gateway

Sanofi introduced eight products in 18 months through the Digital Accelerator.
The accelerator enabled delivery of full API sets in less than 24 hours according to the article.

Architecture

The article describes an AWS-centered serverless and ML stack built around Amazon SageMaker and Amazon SageMaker Studio for model development, with Amazon S3 for storage, AWS Lambda for event-driven compute, and Amazon API Gateway for API management. Sanofi also evaluated and deployed generative AI pilots on Amazon Bedrock.

Sources & evidence1

Evidence: Medium65/100Evidence strength