Modernizing Targeting to Close the Field Execution Gap - with Damion Nero of Daiichi Sankyo

**Daniel Faggella** (0:12)
Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Damion Nero, Global Head of Statistics for HEORHDA at Daiichi Sankyo. Damion joins me to examine why pharma commercial teams continue to receive field intelligence too late to act on it, and what the structural causes of that delay actually are. The conversation covers why pharma AI pilots fail before reaching production, and how focusing on routine high certainty use cases produces more durable commercial lift and pursuing ambitious automation from the start. Today's episode is sponsored by Odaia. A quick note for our audience that the views and opinions expressed by Damion Nero on today's program are his own and do not reflect those of Daiichi Sankyo or its leadership. To go deeper on this topic and learn how leading organizations approach AI investment more like a venture portfolio, and why interdisciplinary collaboration is critical to defining the right data for AI success, download our free PDF report Beginning with AI at emerj.com/aik1.
That's emerj.com/aik1 to download your copy. Now the conversation with Damion Nero.
Damion, welcome back to the Emerge AI in Business Podcast.

**Damion Nero** (1:54)
Thank you for having me again.

**Daniel Faggella** (1:55)
It's great having you back, and I know that our conversation is gonna go deep in pharma today. I think let's start off strong. In preparation for this conversation, I was reading through recent relevant publications, and there was a line that stood out for me, and it said something like, dynamic targeting has now reached about 25% of large pharma companies. And that went up from 17% in 2023, which means that about three quarters of the industry is still running their field teams of targeting models that were built once, filed somewhere, and they're already going stale by the time the rep opens their laptop on Monday morning. And I know that you've been close to the data side of this for a very long time in drug development and in real world evidence. So I want to ask it plainly, what is actually driving that gap and why is it so stubborn?

**Damion Nero** (2:42)
I mean, I think that ultimately what's happening is, is that you are having sort of resistance or challenges, I should say, in adoption of some of this technology. There's a lot of talk about bringing in new types of data, bringing in AI, bringing in various different technologies. But actual execution implementation is really lagging behind. And I think that has really created sort of the barrier that we're facing right now. And it's especially affecting our commercial and field teams in that they have difficulty really sort of leveraging any of this technology because it's not integrated into our systems. And so they are having to deal with either solutions that exist outside of our ecosystem, which are patchwork and frankly incomplete. Or they are having to rely on sort of older approaches and technologies in order to really just do the basics of their roles.
So it's definitely a challenge that we are actively working towards overcoming. I would say there's multiple different aspects of this that we're trying to attack this from. One is sort of more pooling or integration of a lot of the data channels that we have coming in. Whether you're talking about certain HCP behaviors, RWD, signals from digital engagements, market analysis, territorial data, various different things that we're hoping to pull into either a shared domain in terms of just the data existing in one place and being accessible through singular or multiple channels, or to have something more complex where there are more customizable reports that are being generated by AI agents who are curating the data in a way that we can create insights from all of these multiple sources.

**Daniel Faggella** (4:30)
There's definitely more than one thing to take into consideration here. I'm thinking you've worked across both research and medical science of pharma. When insights get generated to basically get this to advance, whether this is now real-world evidence or targeting models or analytics, what typically happens to them on the journey to the field? Where do they get lost when we have these insights to get it from the top to the field agent?
Where do things go wrong?

**Damion Nero** (4:56)
I mean, there's several points. One is really how we're operationalizing this data internally. There's oftentimes no real pipeline set up. A lot of times, data ingestion, transformation and operationalizing that data are fragmented and disconnected processes that ultimately are reliant on systems that, in many cases, are somewhat outdated or are not really optimized towards this process. At any one of these stages, this breaks down. What you fundamentally have happening is that, you have delays in getting this intelligence out. It's not as if the information isn't there, but rather is it timely in the sense that oftentimes, especially our field teams, when they're getting this information, it's a day late, it's a week late, and that's all it takes for them because they really have that one window to get engagement, whether it be from HCPs, whether it be from payers. If they miss that window, well, it's better luck next time.