Breaking Bottlenecks in Life Sciences R&D with AI Innovation - with Aziz Nazha of Incyte Pharmaceuticals

**Daniel Faggella** (0:14)
Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Aziz Nazha, Global Head of AI Innovations Institute at Incyte Pharmaceuticals. Incyte Pharmaceuticals is a biopharmaceutical company focused on research and development. Aziz joins Emerge's Matthew DeMello on today's episode to discuss why life sciences organizations struggle to turn advanced AI capabilities into meaningful business outcomes, pointing to cultural readiness, talent gaps, fragmented data environments, and unrealistic expectations as the core barriers. He describes how rethinking existing processes can shorten scientific cycles, reduce avoidable delays, and enable organizations to capture measurable gains over time. Today's episode is sponsored by Deloitte. Just a quick note for our audience that the views expressed by Aziz Nazha on today's program do not reflect that of Incyte Pharmaceuticals or its leadership. For our solutions partners, position your brand alongside the Fortune 500 leaders in finding the Enterprise AI roadmap. For the opportunity to showcase your solution to the executives currently funding and scaling global initiatives, partner with Emerge. Secure your partnership at go.emerge.com/partner.
That's go.emerj.com/p-a-r-t-n-e-r.
Now the conversation with Aziz.

**Matthew DeMello** (1:40)
Aziz, welcome to the program. It's a great pleasure having you.

**Aziz Nazha** (1:42)
Thank you, Matt. It's a pleasure to be here.

**Matthew DeMello** (1:44)
Absolutely. Life Sciences leaders today are telling us that they sit at a difficult crossroads. They're trying to build AI systems that offer unprecedented scientific capability, yet R&D organizations struggle to translate these advances into measurable business outcomes. We had Ben Nino on the show from Deloitte a little bit earlier talk about that we now have the scaling capabilities to develop more medicines for rare diseases than there are grains of sand on the planet. And that is a fantastic development. Definitely like what I love to repeat at the proverbial high school reunion about all of the good that AI can do. But then there comes to the question of, well, that's a lot of inputs. What are the outputs going to look like on the business value side? Teams are facing cultural resistance. There are unclear blueprints for workflow integration, major gaps between prototype success and enterprise wide adoption. Across discovery clinical development, we're seeing the same core friction over how to run massive scientific practical gains. In so many words, we're trying to just nail down what those incremental challenges are, especially as we turn those inputs into outputs. What challenges are you seeing as defining the current state of R&D and AI adoption in life sciences for turning that awesome capability into business value?

**Aziz Nazha** (2:59)
Yeah, that's an excellent question. And I think it's in the mind of a lot of leaders today. And one side, when you listen to the news and the hype about AI, it looks like AI is sort of redefining pharma, redesigning drugs. We're living in this utopia of the new drug discovery and the innovations and Alpha Fold when the Nobel Prize and all of the stuff. On the flip side to that, on the ground, organizations trying to implement AI systems are still having difficulty capturing return on investment. And I think the problem of the issue is really not the technology. The technology is great, and it's going to significantly enhance what we do every day. I think the challenges are in four things, culture, talent, infrastructure, and setting up the right expectations. And before we talk about the technology and how we apply the technology, we need to take care of those four things. And if we do, then the technology is going to have significant impacts. So we start with the culture. Most cultures, the question with the culture is, is the culture amenable for AI? Meaning, do they know what the capability of, or they know the knowledge and have the capabilities of those AI systems? Do they understand them very well? Is there like a fear of this AI replacing them?
So what does that mean is that we need a lot of education for our workforce about AI, the proper use of AI, what do we mean by AI, and how that's going to incorporate with them. And also the type of AI systems we're building and those stuff. So bringing up the culture up. And that's happened by upskilling your organization. And I would argue what we need to do is re-scale people. And that's very hard to do, re-skilling. I think we should start with upskilling, having people use the technology in their job first, understand the shortcomings of the technology, as well as the fabulous stuff that it does. Because I think sometimes we only talk about the great things that the technology do, but also there are some challenges and ethical challenges and problems, especially when we apply it to our industry, which is in healthcare, which is different than other industries.