From Hype to Health System Value: A CFO’s Take on Sustainable AI Growth

AI in healthcare is at a turning point. Investment continues to grow, but health systems are demanding more than excitement. They want solutions that deliver measurable results today and have a foundation for long-term value. We sat down with Barrett Dwyer, CFO of Rad AI, to discuss what separates sustainable growth from short-lived excitement and what both startups and health systems should consider in this age of AI.

You’ve led finance through multiple inflection points. What made Rad AI stand out from other health tech companies on your radar?

For me, it came down to two things: solving an urgent problem and doing it in a way that scales. Radiology is not a niche market; it is the backbone of modern medicine. When imaging departments are stretched thin, the entire care chain feels it.

Rad AI is tackling that pressure head-on with solutions that integrate directly into the workflow. That is a significant differentiator. Too many companies build interesting technology that sits on the sidelines, disconnected from daily operations or doesn't solve an actual problem. Those solutions rarely stick because they create extra work rather than removing friction.

As a CFO, I look for companies solving problems that will remain critical five, ten years down the line. Burnout, workload imbalances and efficiency challenges in radiology are not going away. When you combine that need with strong adoption and a product that delivers ROI, you have the foundation for sustainable growth.

For health systems, I recommend asking vendors a simple question: if we turned your product off tomorrow, what would break? If the answer is “not much,” it might not be a long-term solution.

Radiology isn’t always seen as the “first place” to invest in AI. Why do you think that’s changing and what are the economic signals you’re seeing?

Radiology has become one of the most valuable levers for health systems. Virtually every specialty depends on imaging: oncology, cardiology, orthopedics, you name it. When radiology runs efficiently, the entire hospital benefits: care moves faster, physicians stay on schedule, and patients get answers sooner. That translates into stronger clinical outcomes and healthier margins.

The workforce challenge in radiology has been a catalyst for innovation. There’s simply no way to meet today’s demand through staffing alone, so health systems are turning to AI to unlock capacity. It’s one of the rare investments that improves both cost structure and quality. We’re seeing that reflected in budgets, which are moving toward solutions that embed into daily operations and deliver measurable ROI.

What’s something you’ve learned about the business of radiology that surprised you?

Radiology is a true pioneer in adopting technology. Decades ago, they led the way with PACS, then voice recognition, then teleradiology. They’re not just early adopters, they’re continual innovators because they understand how central they are to care delivery.

What’s exciting now is how quickly they’re embracing AI when it shows real value. Radiology leaders are pragmatic and outcomes-driven. If a tool improves turnaround times, supports their teams, and integrates seamlessly into workflow, they’ll run with it. It’s a great environment for companies like ours that focus on solving meaningful problems.

There’s pressure in the market to show both growth and discipline. How are you balancing those imperatives at Rad AI?

For us, it’s about building like an investor, not just operating like a startup. Early on, we chased learning, we experimented, tested ideas and moved quickly to find where we could deliver real impact. That phase was essential, but you can’t scale on experiments alone.

Now, growth for us means doubling down where the evidence is strongest. We invest in areas where customers are expanding, outcomes are proven and adoption is sticky. That creates a growth engine that isn’t just fast, but sustainable.

Discipline is the other side of that coin. It’s not about saying “no” to ambition; it’s about saying “yes” to the right things. We’re rigorous about measuring impact, aligning spend with value, and making sure every new initiative strengthens the foundation we’re building.

That’s how you evolve from a promising startup to a company that can lead an industry by combining the urgency of innovation with the structure and focus needed to endure.

We’re seeing more health systems become active investors in innovation. What do you make of that trend and how should startups think about building financial models that align with both VC and strategic healthcare capital?

It’s one of the most encouraging signals in the market because it illustrates that providers want to shape the tools they use, not just adopt what’s handed to them. For startups, it changes the way you build and how you prove value.

VCs look for growth potential: scalable models, big markets and the ability to execute fast. Health systems, on the other hand, want to see how you solve their problems today: operational impact, clinical alignment and financial results they can measure.

To earn both, you need to bridge those priorities. Anchor your model in customer ROI. If you can show a hospital CFO that your solution reduces turnaround times, improves capacity, or drives margin improvement, you have something that resonates with every stakeholder at the table. That’s how you go from a “nice-to-have” pilot to a strategic, long-term partner.

At Rad AI, this alignment has been core to how we’ve built. We focus on proving outcomes in real workflows, and that credibility opens doors with investors and health systems alike. When you can demonstrate impact on patient care and the bottom line, growth follows naturally—and it’s the kind of growth that lasts.

What metrics or leading indicators matter most to you when evaluating the health of a B2B healthcare company like Rad AI?

The standard SaaS metrics, ARR growth, net retention, sales efficiency, are table stakes. They tell you if the business is financially sound. But in healthcare, those numbers alone don’t tell the full story. You have to look at how deeply the product is embedded in the clinical workflow.

I pay close attention to how quickly new customers go live. Long implementation cycles can kill momentum. I also look at clinician adoption rates. Are the people on the frontlines actually using it? If usage isn’t strong, retention will suffer no matter how great the sales numbers look today.

Expansion is another key indicator. Are health systems increasing their footprint with the product once they see results? That’s a strong sign you’re delivering real value.

When you combine these leading indicators, fast time-to-value, high clinician engagement and consistent expansion, you have a business that isn’t just growing revenue but is positioned for long-term durability. In healthcare, that’s the true test.

Rad AI is entering its next chapter, what does long-term financial durability look like in your view, and how do we build it?

For me, long-term durability comes down to three things: diversified recurring revenue, deep customer integration, and strong unit economics. If you have those, you have a business that can withstand market shifts and keep growing.

For Rad AI, that means continuing to expand within health systems. Not just adding new logos, but becoming the partner they rely on across multiple parts of radiology. When your products are essential to how care is delivered, you earn staying power.

Financially, it’s about scaling with discipline. Every dollar we invest needs to compound; driving adoption, improving retention and building trust. That’s how you turn growth into something that lasts. If we keep executing on those fundamentals, Rad AI won’t just lead in radiology AI, we will be a company built to endure in healthcare for decades.