Proving AI ROI in Pharma Marketing - with Kellen Winters of Novartis

Daniel Faggella (0:12)

Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Kellen Winters, Director of Customer Success and Business Operations for AI at Novartis. Kellen joins us today to discuss why proving the value of AI investments begins with measurement, establishing a data-driven baseline, and anchoring each use case in the business problems where work stalls before any technology is selected. Just a quick note for our audience, that the views and opinions expressed by Kellen on today's program are her own and do not reflect those of Novartis or its leadership. According to Nielsen, 91 percent of podcast listening happens alone, indicating deep, undistracted attention, ideal for complex B2B messaging. To learn how leading brands and AI startups connect with enterprise AI buyer audiences at scale, you can download our media kit at emerge.com-ad1. That's emerj.com-ad1. Now the conversation with Kellen.

Kellen, welcome to the show.

Kellen Winters (1:28)

Thank you so much for having me. I'm really excited to be here.

Daniel Faggella (1:31)

You planted a seed in my brain last week, and ever since then, I cannot stop thinking about the amount of money moving through a marketing organization's vendor and contractor line before anyone actually asks, listen, is this working?

What feedback do we have on this? And it always felt like, okay, this is invisible spent until someone really looks into it, which is a very scary concept. So before I break my brain about this, I'm going to ask you to dissect the problem first and tell us, how would you describe the challenge in large pharma marketing organizations in terms of proving their return on investment for their AI and technology parts of that?

Kellen Winters (2:13)

Yeah, a great question. And again, it's so awesome to be here with you all to share some of the ideas and things that we've been seeing across the enterprise over the last two to three years I've been working in this space. And I think that this is not an old problem that we're having. So when we think about technology investments and we're using these within enterprises, it's thinking through how are we actually driving a return on an investment in that technology. And I think with the rise of AI, this has become much more on the forefront. So this has been a problem for, I would say quite some time, but with these increasing investments in AI and the increasing pressure to embed AI in existing processes across any enterprise, I think that we're starting to see and have more of these real conversations.

Daniel Faggella (3:05)

And I'm sure with starting those conversations, resistance is a big part of that, especially walking into legacy organizations. Well, I would imagine. Would you say the resistance is more about the money spent or more about the admitting that it wasn't monitored?

Kellen Winters (3:20)

I think it's a little bit of both. I don't know that there was necessarily that pressure within any components of the business to really sit down. Of course, I'm not speaking for any enterprise, but with a lot of the clients that I work with, that pressure to really show the return on investment on the spend was never really hammered into an organization. But again, I think with the pressure on AI, for the pressure on how to think about it and how to effectively apply AI, is just going to continue to increase as AI becomes more embedded within any existing process across enterprises.

Daniel Faggella (3:57)

Is this a pharma-specific blind spot or have you picked up on this across different industries because something that our audience members might not know about is that you also consult. So this is not just looking from a specific industry's perspective. Is this an entire world thing or just a pharma-specific blind spot?

Kellen Winters (4:16)

Yes.

And we started to see this really early on because before I started my own consulting work, I was working at Microsoft for about six years. So when we were taking Copilot to market, one of the biggest components of customer feedback that we were hearing was like, hey, I don't necessarily care. And I mean that in full honesty. We don't necessarily care if I'm saving people five minutes of time a day. So really show me on paper something that's tied to my existing workflows, my existing business that justifies this huge spend that I'm going to put on my organization. And so we really had to go back to the drawing board and say, hey, how do we make this real for an enterprise? And today you see things like the business value dashboard came out of a lot of those conversations at Microsoft that we have that really allows customers to plug in their existing workflows, what KPIs those are tied to, to really understand if the AI is driving the enterprise value, that our AI vendors are touting towards us. And also, are we choosing, are we even choosing the right workflow to align AI to? So I think we see a lot of people snapping to AI for everything. But then the real question is, is are we choosing the right workflows? Are we aligning it with the right technology?