Gen AI in Life Sciences: Improving supply chain resilience

Life sciences companies are under growing pressure to make their supply chains more resilient and cost efficient.

Ongoing challenges like geopolitical instability, inflation, and severe weather events are boosting costs for materials and transportation. They’re also causing delivery disruptions and product shortages that can further drive up spend, expand an organization’s overall carbon footprint, and ultimately impact the bottom line.

Generative AI (Gen AI) and other data-driven technologies are poised to solve these challenges by helping companies rapidly uncover new market opportunities and accelerate product development. Large language models make access to data easier for non-technical users – enabling data scientists and business users to quickly research issues and develop solutions.

Gen AI can also play a role in strengthening supply chain resilience by identifying product anomalies across the value chain and tracing them back to the source – stopping potential issues before they become something bigger.  

But, until companies address some fundamental issues in their organizations, many will be unable to take full advantage of these tools. In our point-of-view, Life sciences enterprises must leverage generative AI to improve their supply chains, Capgemini and Microsoft experts share their perspectives on what life sciences organizations need to consider when implementing initial Gen AI use cases across the supply chain. You’ll also learn about some of the innovative ways leading life sciences companies are using Gen AI to improve supply chain resilience today.

Download your copy of How life sciences companies use generative AI to improve supply chain resilience now.

To learn more about how Capgemini helps pharmaceutical, med tech, and other companies in the life sciences sector use generative AI and predictive analytics to improve supply chain resiliency, please contact Capgemini’s Ayan Bhattacharya.