Success story #2 Real-world AI implementa�on: A masked interview with a large enterprise Teaching through learning Life Sciences firm gains knowledge-driven opera�ons and improved bo�om line from AI integra�ons and data-centric strategies Country: US Type of company: Biotech/pharma Role: Head of AI/GenAI & Natural Language Processing Revenue: $2 billion business unit of a $35 billion company Overview People are s�ll trying to experiment. [But we have] to consider the trust aspect, ethics, and compliance aspect. And there is this whole aspect: 'Should we stop, or should we just keep doing something?' So I think a large por�on of this industry has kind of come together, sort of indirectly aligned, so that we can use technologies like OpenAI, large language models, and ChatGPT to take on some low-hanging-fruit items, which is what we are actually doing today. "[Most] of our uses cases today are [applicable across our] broad industry, and 50% of the use cases are handled by these chat technologies... We're moving the rest of our data to the cloud and ra�onalizing it so it can be used by new AI capabili�es. "One of the examples is where we're really trying to develop interfaces ... to integrate a lot of ques�ons that we get in our [service management] environments, which is like customer desk, customer support, call center, etc. and for all of these we have knowledge-based ar�cles. So, we are compiling all of these and providing answers to our employees at their finger�ps just by providing large language models. That's our number one use case in my company today, and I think the adop�on is by more than 15,000 or 20,000 users. In biotech/pharma, it is especially necessary to take a conscien�ous approach and ensure effec�veness and accuracy before diving in and blindly adop�ng the latest technology. Cau�onary tales of companies in other industries facing lawsuits due to AI-driven decisions are increasing. Such instances have made execu�ves at this company circumspect in their approach to GenAI technologies. Nevertheless, AI has already proven its worth. For example, when this par�cular biotech/pharma company has a project or product with 20 related documents affected by con�nually published government updates, GenAI tools are able to quickly update the en�re document set. The same applies to informa�on from the many related scien�fic applica�ons and publica�ons. This means the end users—scien�fic people, engineers, process people, and factory people—are ge�ng the latest and most relevant government updates, which can also helps in terms of cost savings. TCS AI for Business Study Key Findings Report 12