When will pharmaceutical companies’ use of AI tools generate revenue? This has drawn much attention. The application progress of large AI models in the pharmaceutical industry is relatively fast at both ends of the chain. One end assists in new drug research and development, and the other end is drug market research. It is currently transitioning from both ends to the middle. Large models have different solutions for helping pharmaceutical companies generate revenue through marketing.
Assisting pharmaceutical companies in marketing: “Bai Xiaoqi,” a digital employee, is Yunnan Baiyao’s attempt. It can improve employee office efficiency and assist in drug sales. There are already vertical large model products designed for enterprise marketing, such as the marketing large model released by Dongxin Group. Healthcare investors believe that professional digital marketing companies need large models for market screening to match potential customers.
Better at performing single tasks: Pharmaceutical companies mostly introduce large models to improve work efficiency. For example, Moderna has introduced generative AI with the support of OpenAI, and Oscar Health has developed an AI assistant. However, people expect large models to bring more creativity to market analysis and marketing. For example, Yunnan Baiyao trains “Bai Xiaoqi,” and a retail data analysis company launches a competitive intelligence platform. Practitioners believe that large models can currently only be applied at a single point in the marketing field.
Uncertain returns: Most industry insiders believe that the future value of pharmaceutical companies’ use of artificial intelligence tools is promising, but currently most pharmaceutical companies are waiting and watching. A McKinsey report shows that more than 70% of healthcare organizations are using or testing generative artificial intelligence tools, and about 60% of those who have implemented them have seen or expect positive return on investment. Domestic pharmaceutical companies are concerned about the cost of using AI. There are two ways for large models to be implemented in enterprises: private deployment and calling APIs. Due to insufficient digitalization in the industrial field, the application cost of large models is high. Moreover, training a large model requires both a sufficient amount and quality of data; otherwise, it will affect performance. There are also opposing views on whether the investment in artificial intelligence and revenue generation in the global market are matched.
Published on December 26, 2024, Thursday at 00:17:12.
