AI Adoption and Skepticism in Regulated Industries - with Ylan Kazi of Blue Cross Blue Shield of North Dakota

**Matthew Demello** (0:16)
Welcome, everyone, to the AI in Business Podcast. I'm Matthew Demello, editorial director here at Emerge AI Research. Today's guest is Ylan Kazi, chief data and AI officer for Blue Cross Blue Shield of North Dakota. Ylan joins us on today's show to discuss balancing AI innovation with risk in regulated healthcare, framing risks realistically compared to medical errors and human inaccuracies. Our conversation also covers practical workflow changes, including cross-functional governance teams for policy development, starting AI experimentation with low-risk use cases to build standardized processes, and leveraging AI to improve patient experience such as lab result explanations and wait time predictions. But first a quick message from our sponsors. AI agents accelerate workflows, but errors can multiply fast. Rubrik Agent Cloud provides full visibility, enforces policies and rewinds actions in minutes. It runs continuously, giving guardrails tracking activity and providing a safety net so teams can scale AI without risking critical operations. This segment is sponsored by Rubrik Agent Cloud. If your business relies on AI agents, you can get exclusive early access to monitor, govern and rewind their actions at rubrik.com. That's rubrik.com rubrik.com. Interested in putting your AI product in front of household names in the Fortune 500? Connect directly with enterprise leaders at market leading companies. Emerj can position your brand where enterprise decision makers turn for insight, research and guidance. Visit emerj.com/sponsor for more information. Again, that's emerj.com/s-p-o-n-s-o-r.
Without further ado, here's our conversation with Ylan.

**Nick Gertsch** (2:03)
Ylan, thanks so much for returning to the show. It's phenomenal to have you back.

**Ylan Kazi** (2:08)
Yeah, thanks, Nick. Great to be here again.

**Nick Gertsch** (2:10)
Yeah, so, Ylan, in highly regulated sectors like healthcare, the conversation around AI isn't just about innovation, it's about risk too. Whether that's the inherent risk in data handling and exposing medical records or PII, or bias in trials to model reliability, regulations, etc. There's a lot of risk there, but there's a paradox too. Many leaders face this paradox that extreme caution can actually become its own form of risk. So, while compliance and safety are currently dominating the dialogue, it's kind of easy to overlook the fact that if you delay adoption, it could cost you even more in lost opportunity, in efficiency, in patient outcomes, than taking those calculated and well-governed steps forward. So, with all of that being said, what is your take on risk aversion and how do you see it shaping AI adoption in healthcare and other regulated health sectors right now?

**Ylan Kazi** (3:19)
That's a great, great question. And I think you're right. When we look at healthcare in general and the different sectors, we're working with some of the most sensitive data in the world, patient data, member data, health conditions. And so there always is just that very high bar and that high standard for making sure that we're keeping PHI and PI very safe and secure. When it comes to AI adoption and some of the safety conversations, the regulation, there are elements where it is always going to differ from other industries. You know, if we look at retail, for instance, one of the worst things that could happen is somebody gets ad targeted incorrectly and they don't want to buy that t-shirt. With healthcare, right, obviously very different. I think that what we're seeing and what I've been seeing very recently is we do need to still be talking about safety and governance.
But in a way that enables innovation and adoption. And I don't think that those two things are necessarily mutually exclusive. I think in our minds and especially for those that have been in healthcare a while, sometimes we can have like self-limiting beliefs. But I think this is really an opportunity to push the envelope. And when it comes to risk aversion, we can't be at the point where we just don't apply AI to healthcare. That's way too risk averse. I think what we need to be doing more is actually detailing out, okay, what are those risks that we have in our minds? And then actually looking at the data or looking at the use cases to really see does that make sense in a realistic standpoint. And one of the examples that I use a lot is when we look in healthcare, medical errors and harm and death from medical errors, it's pretty high. It's in the hundreds of thousands of use cases, you know, potentially even more. And yet we still practice medicine, we still do surgery, we still do all these functions with an understanding of the risk and putting safeguards into place. And I think that's how we can also treat AI so that we get all the benefits from it, while also still, you know, reducing any potential risk or even reducing any potential harm.