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COGNIZANT ADVERTORIAL Data quality is the key to
unlock generative AI in life sciences The quality and safety of your data make out the cornerstones of successful generative AI solutions. Long-standing partners Cognizant and Amazon Web Services (AWS) collaborate to enable top-of-the-line cloud services that power their customers’ AI journey. G ENERATIVE AI CAN BENEFIT the entire life science value chain, from discovery through to commercialization. It has the potential to improve efficiency and quality in areas such as research and development, manufacturing, and everything in between. “It’s been estimated that you can cut the time from lab to patient by one third, and cut cost by potentially up to a quarter, if you enable generative AI in each of the functional areas across the value chain,” Lone Harboe, Life Science Consultant at Cognizant, says. “Generative AI is about serving our customers better, and freeing up our people to do the work that only humans can do. Ultimately, it means that our people can spend their time on something more meaningful.” she says. The clients that Cognizant provides with generative AI solutions today are looking for a wider scope of applications than previously. In the earlier days of generative AI, companies were primarily looking for improved efficiency, cost take-out, and saving time. Nowadays, they are looking more to process data in order to get to a quality lead faster. Some of the most exciting use cases, according to Lone, can be found in the discovery phase. Generative AI can scan enormous amounts of potential targets to find leads with the right attributes, producing a better qualified sample of leads for preclinical and clinical development. “It’s about getting medications to patients faster,” she says. “We know there is enormous attrition of the drug pipeline, we’re scanning a lot of different molecules during the discovery phase and a lot of them fail, because our pool of targets is not well qualified enough. Generative AI can help there.” Anamaria Todor, Principal Solutions Architect at AWS, has also seen a shift in how generative AI is used. “We’re starting to give AI a bit more autonomy and injecting it into processes that are more around creative explorations and getting to conclusions faster – as opposed to just being a tool that augments human thinking,” she says. However, in order to get there, life science companies must first ensure that the data and data governance is tight, as both the data and the regulations surrounding it are complex. “You can’t have quality machine learning without having quality data. First off, make sure that you have as high quality data as you can, meaning cleaning it up, tagging it, connecting it, and so on,” Anamaria says. Another key aspect is considering if it’s better to apply different data governance rules to different data sets and different parts of the organization, according to Anamaria. Research: Gen AI adoption in the Nordics Read the full article by scanning the QR code: A system with different governance models is more complex, but in the end it allows for better innovation and acceleration. “For example, few companies are prepared to apply different data governance rules for research and development activities, compared to the rest of the organization. That leads to challenges in innovation. The same, strict rules that are needed in other parts of the organization may not make a lot of sense in a more development-focused or more experimental-oriented environment, where they need more room to explore,” Anamaria says. Lone muses that with solid data management and the right application of generative AI in place, the life science industry can serve patients even better. “We’re going to help patients get their medication faster, and we’re also going to get medications to patients that we weren’t able to find before. There will be health outcome benefits, health economics benefits, and as a whole generative AI will be a growth engine for the industry,” she says. Lone Harboe, Life Science Consultant, Cognizant Anamaria Todor, Principal Solutions Architect, Amazon Web Services (AWS)