AI has been here for many decades, but the buzz around this latest wave of AI is louder than any I can remember. You can find articles about AI in every publication and medium. These articles deal mostly with broad themes, rarely delving into how AI might be applied in our work lives. There are plenty of stories about self-driving cars and the latest human expert to be outwitted by an algorithm. But what about the unsexy business that most of us find ourselves in? How can we apply AI to improve customer interactions, maximize product launches, optimize clinical trial operations, or find patient insights?
As a leader of the analytics practice at ZS, I have the good fortune of being able to see many applications of AI across clients, industries and practice areas. In this series of blog posts, I’ll talk to thought leaders at ZS and at our clients who collaborate to implement and operationalize AI, so you can hear more specific examples of AI in practice. To kick this series off, we’ll start with an introduction to AI for the pharmaceutical industry. Naturally, I turned to the leader of the pharmaceutical practice at ZS, my good friend and co-conspirator on all things AI, Pratap Khedkar.
A: There are three elements that are different.
The first is simply the impact of better performing AI, what it can solve for, what it can do. It’s noticeably superior. When I compare it to the second or the first wave, the wide range of problems that are falling to AI is markedly different from a broad perspective.
The second piece is that the data itself has exploded thanks to the internet, thanks to the fact that everybody's got a phone, thanks to the fact that the paper economy is disappearing, and everything is digitized—even digital healthcare records. None of that existed 20 years ago. So, because the data was not in electronic form, the algorithms were essentially starved.
A third factor is that AI algorithms themselves have been improved. New ones have been invented, starting around 2004. So, over the last 10 or 15 years, we've seen a profusion of new techniques as well.
A: You can use AI for everything from producing the product, to designing the product, to manufacturing the product, to selling the product. And then there are underlying enterprise applications—HR, for instance. In these areas, I think what we’re finding is you can get good use cases if you look hard. But I would say that the two that are the most exciting now are R&D and commercial.
There’s been a lot more excitement in R&D because there was a lot more data from clinical trials and consumer activity. But because of the focus on R&D, a lot of commercial uses of AI have been under-served. So, what the industry needs to do is fire up more uses of AI in commercial, where there’s more room for using AI to make good decisions, from micro-decisions to macro-decisions. You just need to gather specific use cases.
One small example: Perhaps I want to use AI to help a rep. It could be a rep has to consume 40 reports. That's a lot of information. So, if I can ask the AI to sift through these reports and give me the top three insights for a doctor for today, then that's a very specific use case.
It takes years to see the impact from AI in R&D. In commercial, if you solve the problem, you can see the impact in three months. There is a lot of opportunity for AI to work its magic in commercial.
A: Don't start with, "Oh, I need to hire 15 people and get a $20 million budget.” Start with clear, specific and even small use cases. What are the three, four, 10 use cases I can think of to start with? Maybe two or three good ones in sales, two or three good ones in marketing for a given brand, maybe there are a couple of use cases for outpatient services. Go into manufacturing and R&D as well. Go pick out these use cases, brainstorm, do feasibility studies, do some "hackathons." Without these use cases, your AI will not succeed.
Second piece of advice: Use cases cannot be solved without data. Now, in pharma, we’ve fallen into the trap of saying, "Well, data is only useful if it's 90% complete and 90% accurate." Pharma needs to get over that mindset because AI works when you have a lot of broad data, the depth and accuracy are not as important. Quick and dirty is better than trying to be very precise with the data itself, because when the data is a little off, the algorithm can compensate by being a little smarter.
And then the third advice I would give you is around organizational mindset. It’s about getting the human to adopt, use, understand and trust this AI output. You can solve a problem well, but nobody uses the AI. That's quite common. So, how will you change human minds in your organization to use this AI output? Work on that and put energy into adoption and change management from the outset.