From Experimentation to Clinical-grade AI in Healthcare - with Alex Tyrrell of Wolters Kluwer

**Daniel Faggella** (0:12)
Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Alex Tyrrel, SVP and CTO of Health at Wolters Kluwer. Wolters Kluwer is a global leader in information solutions, software and services for professionals in healthcare, tax and accounting, financial and corporate compliance, legal and regulatory, corporate performance and ESG. Alex joins Emerge's Matthe DeMello on today's show to explain how agentic systems can be used behind earlier generative AI by executing tasks, adapting to real-time conditions and interacting directly with operational systems. He describes how these capabilities depend on infrastructure maturity, domain-adapted models and safe integration into regulated environments. He outlines how these approaches can ease administrative load in areas like EHR processes, improve accuracy through reasoning and model adaption, and increase the volume and speed of system interactions, requiring stronger API design, entitlements, observability and security. He also underscores the ongoing need to manage model drift, maintain compliance and prepare back-end systems for higher velocity automated activity. Just a quick note for our audience that the views expressed by Alex Tyrrel on today's program do not reflect that of Wolters Kluwer or its leadership. In AI, we see a lot of skepticism, and for good reason. The challenge isn't excitement, it's execution. Data readiness, integration complexity, security concerns, proving ROI, it all has to align. Our sponsor, SHI, has been a major global IT solutions provider for over 35 years, helping 17,000-plus organizations navigate exactly these challenges. SHI guides enterprise leaders through their proven framework. Imagine the right AI strategy for your business context. Experiment in their AI and cyber labs to validate solutions with your actual data and workloads. Start your business rapidly in 2-6 weeks and adopt solutions that deliver measurable outcomes in production. If you're looking for guidance on moving from AI ambition to real results, check out shi.com. That's shi.com. Look for the AI and cyber labs or schedule a consultation with their team. Now, the conversation with Alex.

**Matthe DeMello** (2:18)
Alex, welcome to the program. It's a great pleasure having you.

**Alex Tyrrell** (2:20)
Awesome to be here, Matthew.

**Matthe DeMello** (2:21)
Yeah, absolutely. Over the past few years, we've heard a lot of health care leaders come on the show, talk about how they've explored generative AI in narrow, low risk ways, and they're often achieving incremental efficiency gains without fundamentally changing how care is delivered. All at the same time, clinical complexity, administrative burden and workforce strain, they're all continuing to rise, and there's then this pressure created to move beyond pilots and toward systems that can operate reliably inside real clinical workflows. That's just a synopsis, folks coming on the show and telling us this. Just from your vantage point, what are you seeing in terms of why must health care organizations move beyond AI pilots now? What are the forces behind that?

**Alex Tyrrell** (3:06)
Yeah, so I mean, I think you touched upon quite a few themes, and one I'll pick up on pretty quickly is really addressing that burnout, the administrative burden, really the time spent away from delivering care.
And I think these technologies over the past couple of years have certainly matured. I think in the beginning, there was a bit of trepidation around hallucination and some of the potential for risk. But I think over the last couple of years, the technology sort of continues to mature. And we've actually gone a long way to kind of what we say mitigate or sort of ameliorate, really the big risks like these hallucinations, these gross errors. And I would also emphasize another point, which is sort of tangential. Listen, folks are seeing the value in these tools. And if as an organization, you take the mindset that we're sort of not ready, or we need to delay, or we need to really rethink our governance, that may be fine, but what you're going to start to potentially see is this rise of the shadow AI, right? And I would say, when you think about ghost IT and shadow IT, these concepts have been around for a long time. But really, shadow AI plays to that idea of, you're talking about unregulated, unobservable, un-metered use of these technologies in a workplace setting, which for a personal use or at home, it might be okay. And people tend to let their guard down and think, well, this is just a really good useful tool. And they don't think about that creep from, you're answering some basic questions or helping with an email or something like that. Now, you're getting closer to bedside. You're getting closer to patient care. You're getting closer to PHI. And you have to be really, really careful. And I think that's a really another strong reason why you can't simply delay. The demand is there.