Industry Outlook 2025: Large Language Models

Barriers to the use of artificial intelligence (AI) and machine learning (ML) in the pharmaceutical industry include the cost of implementation and training, according to Will Gordon, senior vice president of Product Management at ArisGlobal. “As you're training your models, and you're starting to use them in the industry, some of them were successful, some of them were not. But one of the things that always took a long time was the actual training. So, you have to go out and source the data. If you had [the data] in house, or you had to go get it externally, [it] was costly and time-consuming, from not only trying to find it, but then cleaning it, and then understanding the data and then training the models.”

Large language models (LLMs) have made implementation easier, according to Gordon. “As LLMs are coming into the industry, you can actually change the speed to which you can pull some of this technology, AI/ML, into your business, right? And so, of course, there's privacy concerns, intellectual property concerns, and I have to think that that will just get sorted … in time. But if you're not playing with this technology, if you're not trying to look for every space of what you do, no matter what department you're in, whether it's R&D, post-market, [or] sales, you have to be using [AI/ML], and the large language models have really kind of sped your ability to test.”

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About the speaker

Will Gordon is senior vice president of Product Management, ArisGlobal.