AI Use Cases, Deployment, and Measuring Real-World ROI - with Ylan Kazi of Blue Cross Blue Shield of North Dakota

**Matthew Demello** (0:14)
Welcome, everyone, to the AI in Business Podcast. I'm Matthew Demello, editorial director here at Emerj AI Research. Today's guest is Ylan Kazi, chief data and AI officer at Blue Cross Blue Shield of North Dakota. Ylan joins Nick Gertsch, Emerj client narrative and content strategy lead on today's show, to explore what it really takes to deploy AI successfully in regulated healthcare. From making smart build versus buy decisions to ensuring AI systems remain explainable, auditable and trusted. Our conversation also covers the practical workflow shifts leaders need to focus on, including aligning executives early on organizational AI posture, building internal capability alongside vendor partnerships, and grounding every use case in real customer value and measurable ROI. Not just dollars, but reduced friction, improved experiences and cultural readiness for AI first work.

**SPEAKER_2** (1:08)
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**Matthew Demello** (2:00)
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Without further ado, here's our conversation with Nick and Elon.

**Nick Gertsch** (3:22)
Ylan, welcome back to the show. It's great to have you again.

**Ylan Kazi** (3:26)
Thanks, Nick. Great to be here again.

**Nick Gertsch** (3:28)
So for most enterprises, the conversation about deploying AI has obviously shifted from an if to a how. The challenge now, though, is less about the technological or technical feasibility, but prioritization. Are we going to prioritize governance? Are we going to prioritize measurable outcomes, et cetera? So leaders are realizing that a successful deployment depends less on kind of the brilliance of a model and more its integration into people, into processes, accountability structures that can actually scale across an organization. From your perspective, how should a leader in the space approach the ever-present question of build versus buy for AI capabilities?

**Ylan Kazi** (4:23)
That's a great question. And I think that there really is no easy answer to it. It's very, very unique to the individual organization. And so I would say starting off with just understanding at an organizational level, does your organization want to be a first mover when it comes to AI?
Do they want to be a fast follower or do they want to take a more cautious wait-and-see approach and do more of the things that are tried and true? And there's no wrong answer to that. And it depends on the company, it depends on the industry that they're in, it depends on their aspirations. But I think having those conversations and coming to a point of alignment amongst your senior executives is key to really starting off the process. Because once you understand that, it's really going to drive many of the other decisions around governance and teams and build, buy, etc. So I would say definitely starting there. And then I think from there, it's understanding, do you want a fully outsourced model where you're just working with vendors, whether that's from a technology standpoint, a people standpoint, do you want a fully in-house model? Do you maybe want elements of both? And from what I've seen and where organizations have had success is building up some level of internal capabilities, just because if you're always reliant upon external vendors from a cost standpoint, it's going to be very, very cost prohibitive, and you're not going to create a high level of sustainability. And so I think, you know, what I would recommend in many cases is at least understanding and identifying what are the in-house skill sets that you want to create within your organization. And then determining if it is more of like anything around cutting edge, or if it's something that is cost prohibitive, like nobody would go about creating their own foundational LLM models, like an OpenAI, right? Or a Microsoft. You know, most organizations do not have that level of resources. So it doesn't make sense to try to do that. What it may make sense to do, though, is having an internal data science team or having some internal AI engineers that can work across the organization. The other piece is understanding value. So as your organization is applying AI to different use cases, what is the actual value that's going to be delivered? Because in many cases, what I've seen is everybody gets excited about AI. But if the overall impact or value is low, why are you using AI?