Pharmaceutical Technology® spoke with Will Gordon, senior vice president of Product Management, ArisGlobal, about the impact of artificial intelligence and machine learning on the bio/pharmaceutical industry.
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.”
Click the video above to watch the full interview.
Will Gordon, senior vice president of Product Management, ArisGlobal.
Will Gordon is senior vice president of Product Management, ArisGlobal.
A Novel, Enhanced, and Sustainable Approach to Audit Trail Review
July 4th 2025Eli Lilly and Company developed an innovative and sustainable approach to audit trail review (ATR) aimed at reducing the ATR burden while adhering to regulatory expectations and data integrity (DI) principles. The process has transformed employees' understanding of ATR and complemented the DI by design approach, leading to better system designs that meet expected controls and reduce non-value-added data reviews.