GSK
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GSK has 3 source-linked AI deployments documented in AIUseCaseHub, across 1 industry and 2 countries. Key partners include Accenture, BenevolentAI, Capgemini.
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2
Hyperscaler mix
See whether GSK's cases are powered by Microsoft, AWS, GCP, or multiple providers.
How GSK builds AI
Build / Buy / Compose across this company's documented cases
3 of 3 cases classified (100%) · Compare all use-case types
Use case portfolio
Use case types at GSK
Drug discovery leads with 1 of 3 documented cases; 3 distinct types appear across the visible portfolio.
Reported outcomes
1 case reports measurable results
−75%
Time & speed
median · 2 metrics
Medians of results published in GSK cases, normalized for comparability. See all benchmarks →
Evidence persistence
3 of 3 judgeable cases are still publicly referenced · 3 show the organization expanding AI use.
Durability of public evidence, not whether systems remain in production. How this is measured →
Technology snapshot
What GSK uses across visible cases
AI Agents appears in 1 of 3 indexed cases; 8 named technologies are mentioned, led by Azure AI.
All Use Cases (3)
Global Innovators Accelerate Scientific Discovery with AI-Driven Agents
Microsoft Discovery is a new enterprise AI agent platform designed to transform research and development (R&D) across sectors. By orchestrating specialized AI agents with a sophist...
AstraZeneca and Novartis Scale AI Across Pharma Value Chain
This article provides a comprehensive overview of how leading pharmaceutical firms—including AstraZeneca, Novartis, Sanofi, GSK, Genentech, and AbbVie—are integrating Microsoft technologies such as Azure AI, Machine Learning, Cognitive Services, and Power Platform to transform drug development and operations. Real-world examples cover AI-powered drug target identification, generative molecule design, clinical trial acceleration, pharmacovigilance, supply chain optimization, and patient engagement via chatbots. The piece details both the tangible business benefits (shortened timelines, reduced costs, improved trial precision, and better patient outcomes) and persistent challenges such as data fragmentation, legacy systems, regulatory complexities, and change management. Strategic priorities for CIOs and IT leaders on how to industrialize AI, ensure enterprise-wide adoption, and promote responsible, cross-functional scaling of Microsoft technologies are emphasized. The article highlights collaborations like AstraZeneca’s enterprise AI roadmap, Novartis-Microsoft innovation lab, and Sanofi’s infrastructure modernization to demonstrate mature, scalable uses of cloud-based AI.Challenges with data interoperability, legacy infrastructure, talent and cultural adoption, and regulatory risk are addressed alongside solutions such as human-in-the-loop designs, explainable AI, and real-time learning cycles aligned with scientific and compliance goals.
Pharma Investment in AI for Drug Discovery
Pharma companies are increasingly investing in AI to enhance computer-aided drug design (CADD) and reduce drug development time and costs, creating a multibillion-dollar market. Fi...
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