Among the pharmaceutical DX-digital transformation professionals, 67% responded that they were not ready when asked about how ready the life sciences sector is to effectively go ahead and leverage generative AI in terms of infrastructure, regulatory considerations, and expertise. This is according to a new report published by Axendia Market Research. A total of 43 percent of those working in research and development agreed with this statement.
There were a total of 200 people who participated in the study, and almost half of them projected that it would be more than four years before generative artificial intelligence went on to become mainstream in their functional area.
At the very least, the early public use cases of artificial intelligence in the biopharmaceutical industry have originated from research and development (R&D), and more particularly, from target identification and molecular design. These use cases have been made available to the public. The key questions that need to be posed are: beyond discoveries and designs, where are the use cases when it comes to the manufacture of biologics, quality control systems, supply chain management, and operations, and what is preventing one from moving forward?
Using artificial intelligence in the production of biopharmaceuticals
A staggering 79% of those who participated in the survey expressed their belief that generative artificial intelligence has the ability to bring about a revolution in the landscape of medicine production, both in terms of the quality of the products and the efficiency with which they are produced. The fact that just five percent of respondents claim to be current users of generative artificial intelligence, on the other hand, paints a picture that is evolutionary rather than revolutionary.
The application fruit, which is actually low hanging as per the industry, is sort of a far-fetched process modeling and simulation in which more than a quarter of the respondents said that generative AI happens to be having the potential to be the most useful of everything it is possible to be. It also happens to be a field in which the majority of the pharmaceutical companies have an abundance of data, which is a resource that is considered essential for artificial intelligence.
It is important to note that the top four opportunities that happen to be the most highlighted are process optimization, which accounts for 19% of the total, medication synthesis and formulation, which accounts for 12%, and real-time monitoring, which accounts for 10% of the whole.
Another reason why modeling and simulation are considered to be the first and even the greatest chance when it comes to generative artificial intelligence in order to have an influence on the pharmaceutical industry is because, in addition to the data availability, there is another rationale. When a modification to the formulation, method, or tracking is being considered, it is the discipline that is least burdened by external influences, which cause a stop in the process. It should go without saying that regulatory impact is the most important of these factors, yet the reality remains that it is the most widespread. However, the figures do support the reality, which is that 69 percent of those who participated in the Axendria poll went on to declare that worries over regulatory compliance are the most significant problem or barrier in terms of using generative AI within the context of the drug manufacturing industry. At 36%, data security and the expertise or lack thereof of the staff are tied for a distant second place in terms of the problem or barrier category. This shows that all of these factors are equally important. Regarding the level of knowledge that exists between the workers and the C-suite, it seems that there is a significant gap between the two groups. When asked how acquainted they are with the notion of generative artificial intelligence, 82% of the executives in the C-suite responded that they were extremely comfortable with it. However, as the question spread down the ranks, their confidence began to decrease, with 62% of the vice presidents and general managers and 31% of individual contributors answering in the affirmative. Among the directors and department heads, just 23 percent of those who expressed the same thing were in agreement.
AI in the management of pharmaceutical supply chains
A third of the people who responded to the research from Axendia Market Research said that they are searching for artificial intelligence (AI) as a way to get an advantage in order to prevent shortages in supply chain management in the future. This is because they are still shaken up as a result of the pandemic supply chain impact. There are a number of applications that anticipate generative artificial intelligence to be of assistance, the most prominent of which being demand forecasting, predictive analysis, and inventory management. There were approximately 77% of respondents who stated that they are not currently utilizing AI-driven technology or other analytical tools in order to manage supply chain resilience. This is in contrast to the 56% of respondents who stated that they are either confident or very confident that generative AI can very well upgrade the efficiency and resilience of supply chains. Why is there such a lack of effort, and who is to blame for it?
The inequality in the supply chain is the first thing to consider. When it comes to retail, it is important to keep in mind that the merchants are the ones who dominate. It would seem that Walmart has the ability to exert enough persuasion to compel all of its suppliers to either comply with their demands or move to Amazon. When this sort of power is used, the process of data collecting and the subsequent analysis are rather uncomplicated. In the field of life sciences, there is no other power that exists apart from the chosen few individuals who are at the pinnacle of the biopharmaceutical pyramid.
AI in relation to quality management systems (QMS) Post-market surveillance and quality management systems (QMS) happened to rally to high levels in terms of support for generative AI solutions within the Axendia report. Seventy-seven percent of respondents went ahead and indicated that performance reporting as well as metrics enhancement, as well as efficiencies, are going to hold a prominent potential. On the other hand, the current adoption rate of technology for that specific application is rather low, as it sits at 12%.
This happens to be yet another instance of an application for technology in a field that is abundant in data; nevertheless, the quality and control of that specific data are what give the individuals who are considering adopting it pause. It should be brought to your attention that a staggering 88 percent of those who participated in the study expressed their belief that generative artificial intelligence has the ability to bring about some possible quality concerns.
When it comes to the activities of the laboratory
Particularly noteworthy is the fact that data analysis in the context of laboratory operations has proven to be the most prevalent use of generative artificial intelligence technology. Only two out of ten people who participated in the poll said that they are currently using it, despite the fact that seventy percent of them feel that the technology has the potential to change the laboratory process in terms of both its efficiency and its quality. It is the data analysis and interpretation that seems to be leading the charge, with 94% of respondents claiming that such activities would gain the most from AI. Workflow and process optimization comes in a distant second, with 54% of respondents stating that they would profit the most from the AI.
