For Sanofi, one of the world’s largest pharmaceutical companies, efficiency is key. It’s an organisation that puts the end-user – the patient – first. This requires an unwavering focus on R&D and continuous improvement, for the sake of the world’s health; every patient counts. So, when opportunities arose to improve services through data and advanced technology like AI, Sanofi brought in experts to steer and develop the journey.
Snehal Patel is the Head of Global Data and AI Platform. Sanofi has a mandate to develop an internal technical platform that enables everyone within the company to develop and benefit from data, AI, and GenAI at scale. That’s what Patel oversees and drives. His background is in data and AI engineering, platforms and developing solutions at scale. He’s led global engineering and platform teams to develop data and AI products and platforms, and prior to that, he was a consultant for 13 years. During that time, he focused on helping large organisations use AI across their value chain, and scaling AI across lines of business and geographies. In effect, he operated at the intersection of data and AI, engineering, architecture, and business.
Seeing AI Differently
Patel’s history of enabling businesses to scale their AI came in clutch when the time came to do the same at Sanofi. Overall, Sanofi is an R&D-driven and AI-powered biopharma organisation. Innovative technology lies at its core. So, the catalyst for the company choosing to scale AI was the advancements in data and AI technologies and the offerings in the industry.
“We’ve witnessed an explosion in data and AI capabilities over the past decade, including, more recently, in GenAI,” Patel states. “The second catalyst is equally important: companies are increasingly recognising AI’s strategic potential. There are significant benefits for business operations, new revenue streams, and even completely transforming old, outdated ways of working.”
Success Stories
All of this hard work is producing impressive results. Previously, Sanofi’s data and AI teams were facing significant inefficiencies. They were hindering development and scaling. Data discovery and validation presented a major bottleneck, because identifying appropriate datasets for AI projects required weeks or longer.
To address this issue, Patel’s team implemented a comprehensive set of capabilities. Firstly, it established a centralised visibility layer, to improve data accessibility. This features standardised data products through a data marketplace, which allows Sanofi’s teams to search, understand, and validate datasets for AI applications. This reduces certain discovery time from weeks to minutes.
Alongside this, the team deployed automated infrastructure solutions. These combine automated environments with reusable components and Infrastructure-as-Code standards. Again, time spent managing environments has been slashed from weeks to minutes.