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Today: The Biggest Mistake a CIO can Make with AI Strategy? What do you think it is?

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  📍 Today in health, it we're going to take a look at the biggest mistake, a CIO's in healthcare. Can make with their AI strategy. My name is bill Russell. I'm a former CIO for a 16 hospital system. And creator this week health set of channels and events dedicated to transform health care. One connection at a time. We want to thank our show sponsors who are investing in developing the next generation of health leaders. Notable service now, enterprise health parlance. Certified health and Panda health.

Check them out at this week. Health. Dot com. Slash today. We're gonna talk about some news stories, this story, and all other new stories we've talked about on this show, you can find on our website this week, health.com/news. Check it out today. All right. One last thing, share this podcast with a friend or colleague use it as a foundation for daily or weekly discussions on the topics that are relevant to you in the industry.

This is part of our drive to get people to mentor at a very basic level. And that is listen to the show, have them listen to the show, have a conversation and just talk about these topics and other topics. Very simple way to get started on mentoring. They can subscribe wherever you listen to podcasts. All right, let's get into it. I'm pulling today from an article from constellation research Dion. Hinchcliffe enterprises must now cultivate a capable and diverse AI model garden. Is the article.

He's a really cool diagram in here. And I understand about 80% of it. But essentially he has multiple access in terms of tasks, specific gender AI models, and generate general multimodal models. At any as another access, which has specialization in complexity and size. And he puts all the different kinds of models out there and he makes the case. For the fact that there's a lot of different models with a lot of different capabilities. And that we are being asked by our leadership to lead this, to get in front of it.

And I love his last Last paragraph here while the lore of specific AI models and vendors remains a siren song, even as there are growing worry, some organizations won't see immediate returns, which is typical. Of many advanced technologies. Building a sustainable internal capability for model ops and AI model management remains crucial for longterm sustainability. Of the business, preparing a strong foundation for data AI models and associated operations for future Preuss. The organization's AI strategy allowing for. At adaptation. To evolving technologies and business needs ultimately maximizing the return on investment. In AI and ensuring its readiness and strategic alignment with an organization's goals. As vital opportunities arise. All right.

So I read that and I'm reading a lot of stuff right now around architecture and specifically. Around AI model architecture. And I realized I was getting in too deep into this specific. Architectures of specific models. And I started to step back and try to figure out as a CIO, what would I be looking at? Right now.

And what really revealed itself to me is that when you're in a time of Fast moving fast, paced change in an industry. The best strategy that you can have is a strategy that allows for adaptation that allows for change. As things emerge when things are gray and you're not really sure you're heading in a direction. But

you're not heading to a point on the map. You're heading. West or you're heading east, you're heading directionally and. And the direction that you want to go. And that is. Our AI strategy right now, the biggest mistake a CIO can make right now is probably twofold. One is to have a very specific. AI strategy. And that you're heading towards and you're making big bets on certain technologies right now. The models are emerging, the models are evolving.

The use cases are evolving how these things are going to come together. How they're going to work together is evolving. And so having a specific point that you're saying, Hey, these are our big bets. That would be a mistake. Another big mistake would be. I'm just going to wait this out. Ah, it's just hype right now.

I'm just going to wait this out. We'll see what emerges and we'll get there. The problem is. When it starts to emerge, you're going to be two to three years behind, probably three years behind. There's a process that needs to be started today. Or three years ago, quite frankly. And that is a model that helps your culture, the culture of your organization. Be ready for AI. And it's you and exploring its use cases. And not the CIO and it exploring its use cases.

Although those will be some big users of AI models. It is. How do you generate momentum around the organization and changing the culture of the organization? And we've had some great conversations with leaders that I think see at that level who understand the CIO role. And even some other roles, C CDA chief digital officer role, or even a chief medical officer role, they see their role as culture stewards. And culture builders.

And when they look at the things that are emerging, they look at them with a, an eye towards possibility. But they know they're not going to get to that possibility unless their culture is ready to accept. And experiment with. And become a part of seeing how that technology can be applied. Two. Usefully to their work. That they're doing today. And so when you talk to those kinds of CEOs, they are establishing governance.

In fact, they established governments. Multiple years ago. That was looking at AI and digital governance or digital governance. But mostly around AI models and that kind of stuff. It was obviously it was multidisciplined you had you had nurses on it on those. Governance groups.

You had doctors on it, you had different. You had it and technology people on it. When you had a lot of different representation at, on that AI governance level and that group. In the beginning was really a learning group. They were bringing in as much information as they can to try to figure things out.

How will this be applied? What does this look like? And there. They're wrestling with very deep questions. Like, how does work change and how do we look at Th the role of nurses in the future, the role of doctors in the future, the role of artificial intelligence in the future. And those changes they're thinking through what are the HR employment implications?

What are the ethical implications to. Patients to the the dignity of the worker. They're having those kinds of conversations, a couple of years ago, because they know this represents a dramatic change potentially in the way we work. And there's enough movement here to know that it is going to impact the way we work.

So they have formed this group and they've given them big questions. Gnarly questions, ethical questions around the use of AI. Now that group is also used as a group that now as we progress forward they identify use cases. With what they know about the technology, not just it saying, Hey, we should do this.

We should do this. With what that group knows about the technology. What's a use case. What's a problem set. That is a good target for the use of AI. And they explore that. And they do small pilots and they get results and they learn things and I've talked to CIS. Who are saying, look, we learned what works in this whole idea of AI generated notes. And we learned what works in, in terms of the AI generated. Correspondence with patients and what doesn't work. They're way ahead on that curve because they've been playing with it for awhile. And. But, and they're not waiting for their vendors, but that's also part of it.

They're getting, trying to get ahead of their vendors. They're not allowing their vendors to set the pace at which AI gets adopted within their health systems. They are essentially that group is determining, where will this be brought in? How will it be brought in what kind of, what are we going to require from our partners?

Are we going to require transparency? Are we going to require some sort of validation of those models, some sort of testing of. Those models. The thing of the biggest mistake a CIO can make right now is to have a specific point that they're moving towards and saying, that's it.

And they're making big bets, the other mistake they can make. Is essentially not moving forward with engaging the culture and changing the culture and finding those co-creators of the code culture of the future. That group should be built. I don't care how small your organization is, and I don't care how small that group is.

That group could be four people. But those four people are reading articles. They're sharing articles, they're having discussions around it. Now as a CIO, we could go into this article. It is really fascinating. And it was a good article. So if you get a chance, hit our website. It will be one of the top, a couple of articles. At least until tomorrow when we add seven more articles every day we had at least about seven more articles. To this. To this site, our contributors keep. Giving us articles to place on our site that are relevant. And enterprises must now cultivate a capable and diverse AI model garden.

I like the thinking here. I like the idea. That right now, we are looking at creating models and we are creating. Frameworks for model ops. Think of dev ops and sec at dev sec ops and all that is we're talking about AI model ops now. The deployment of these models, the operation of these miles governance monitoring of these. Models and all the things that are associated with that and how that integrates with all the rest of the stuff you're doing, the architecture that you're utilizing the legacy applications, the flow of data. In analytics. Compliance practices. And obviously dev ops and data ops and all those things. So very interesting. Ah, article highly recommended and a directionally.

Get your organization ready for change. There is change coming. And to sit around. And when I hear CIO say I'm sick and tired of talking about AI. I'm like. Okay. I don't understand that. This could be the biggest change. That we've seen in technology. In my lifetime. Quite frankly, and I've seen the internet, I've seen the mobile phone. I've seen the PC for heaven sake.

This could be one of the most dramatic changes we've seen in the history of computing and to say, oh, I'm just tired of talking about it is almost irresponsible. I understand it's everywhere. It's a buzzword and that kind of stuff. Maybe the better way of saying this is I'm tired of people talking about this in ways that aren't helpful. And I'm tired of people talking about this. In ways that only confuse the issue. I get that, I read a lot of articles and I'm like, man, this, I just read this article.

I don't think this person. Has any clue what they're talking about at this point, but that's fine. Because everybody's on their journey to really understand this at a deeper level, but the CIO has to be ahead. The architects have to be ahead. The people in it have to be ahead. Reading as much stuff as they can on this.

This is going to be a dramatic change. And and we're going to lead a lot of it and we need to be ready for it.

All right. That's all for today. Don't forget. Show this podcast with a friend or colleague.

Use it as a foundation for mentoring. We want to thank our channel sponsors who are investing in our mission to develop the next generation of health leaders. Notable service now, enterprise health parlance, certified health and 📍 Panda health. Check them out at this week. health.com/today. Thanks for listening.

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