What challenges did Novartis face before AI implementation?

Slow and costly drug discovery processes hamper delivery of new treatments. Clinical trial efficiency and patient accessibility need major improvements. Manufacturing next-generation therapies requires advanced data-driven processes. Employees lack accessible tools for AI-driven insights without extensive data science expertise. Large, siloed scientific datasets are underutilized for R&D breakthroughs.

Source

MicrosoftExpandedEvidence: Low25/100

Novartis redefines pharma R&D with global AI-driven workflows

Use case typeLife sciences innovationUpdated Jun 13, 2026

Novartis initiated a five-year collaboration with Microsoft to accelerate drug discovery, enhance clinical trials, and streamline manufacturing processes using AI and machine learning. The establishment of an AI Innovation Lab across international locations aims to integrate AI tools into daily workflows for all employees. The partnership includes joint research projects focusing on personalized treatments for eye diseases, next-generation manufacturing for advanced therapies, and rapid design of new drug molecules. AI and data science techniques such as image segmentation and analysis are leveraged for personalized medicine development and efficiency improvements. Data-driven insights are delivered directly to associates—enabling 'citizen data scientists' without deep technical expertise to access and utilize AI-powered analytics in decision-making. Projects include remote digital clinical trials and digital therapeutics, emphasizing accessibility for underserved populations, and the data42 platform mining company data for key patterns. Co-working centers in Switzerland, Ireland, and the UK serve as hubs for scalable AI experimentation and innovation. The collaboration seeks to address key pharmaceutical industry challenges, lower patient costs, and reimagine data-driven R&D processes. Both companies share expertise: Novartis with biology and medicine, Microsoft with cloud/data/AI technologies. The project has led to greater workflow efficiency, faster therapeutic discovery, and empowered a data-first organizational shift at Novartis. Microsoft’s AI tools further enhance access to the company’s large clinical/scientific datasets, providing an accessible interface for non-data scientists.

Organization: Novartis
Industry: Pharma
Location: Switzerland
Published: October 2019

Planned next steps

The source says the organization aims to achieve: Accelerated drug target discovery and design.

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

Customer: Novartis
Provider: Microsoft
Maturity: Unknown
Linked source: Fierce Biotech

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 4

1AI-driven drug discovery and design
2Personalized medicine development
3AI-empowered remote clinical trials

Slow and costly drug discovery processes hamper delivery of new treatments.
Clinical trial efficiency and patient accessibility need major improvements.
Manufacturing next-generation therapies requires advanced data-driven processes.
Employees lack accessible tools for AI-driven insights without extensive data science expertise.
Large, siloed scientific datasets are underutilized for R&D breakthroughs.

Established an AI Innovation Lab to experiment and scale AI adoption in pharma workflows.
Integrated Microsoft Azure AI, Azure ML, and Azure OpenAI services into R&D and manufacturing processes.
Developed machine learning-powered platforms (e.g., data42) to pattern-match across vast company data.
Embedded AI tools for routine work—from image analysis to digital clinical trials—supporting all employees.
Co-development of digital therapeutics and remote clinical trials increases trial reach and accessibility.

Technologies

Azure AI Azure ML Azure OpenAI AI Machine Learning

Enabled non-expert staff to access and utilize AI, democratizing data-driven insights.

Architecture

Data from research and clinical trials are processed and analyzed using Azure AI and Azure ML platforms. These insights feed workflows for drug discovery and manufacturing. The data42 platform mines internal datasets using machine learning, while AI tools (including Azure OpenAI) support experimentation across teams. AI tools for image analysis assist personalized medicine development, and remote digital clinical trials leverage the same Microsoft cloud and AI stack via an AI Innovation Lab in Switzerland, Ireland, and the UK.

Sources & evidence1

Evidence: Low25/100Evidence strength