AI in healthcare is entering a reality check phase, with investors now waiting for startups to clearly demonstrate their financial and operational value.
Here are five excerpts from interviews with investors last week at LIVE conference in Los Angeles on how they think about ROI for AI tools in healthcare.
The financial director test
Health systems’ tight finances mean AI purchases increasingly require CFO approval, which means there has to be a strong case for financial performance, said Larry Cohen, CEO of Health2047THE American Medical Association‘s Venture Studio.
“You can see it in the way people talk about their products and try to present what they do. Now, everyone’s not talking about how much easier it is or how much happier everyone is. Now they’re looking at the financial return on investment,” he said.
In today’s market, Cohen said, AI tools need to prove they can be profitable.
The long game
Many healthcare AI companies are still in their early stages when it comes to proving their financial returns, especially for tools to support clinical use cases, pointed out Uma Veerappan, vice president of Flare Capital Partners.
In the short term, she believes that demonstrating adoption could be more important than demonstrating immediate ROI for some startups.
“Given the early stage nature of many of these companies, I don’t necessarily believe that the ROI should show up in the first six months or so after deployment. Rather, I think the ability to show that you have a sticky product is what’s really important. After a longer period of time, that’s probably when the financial and clinical ROI becomes a major issue,” Veerappan noted.
Return on investment depends on the tool
The return on investment is different depending on the type of AI tool, noted Rachel Feinman, managing director of Tampa General Hospital Businesses.
Some tools, such as ambient clinical documentation scribes, generate ROI indirectly rather than through immediate financial gains, she pointed out. For example, health systems can adopt these tools to remain competitive in recruiting doctors, even if they don’t increase visit volume or revenue in the short term.
“Already, health systems and clinics are starting to announce that they’re offering ambient listening to providers, and providers are putting that at the top of their list in terms of where they want to go — that’s where they want to be,” Feinman explained.
At the same time, she said AI that automates manual administrative work – especially for tasks like revenue cycle automation and data abstraction – typically delivers clearer and more immediate financial returns.
Economic models focused on return on investment
Vig Chandramouli, partner at Oak HC/FTsaid he sees more AI startups moving away from traditional SaaS pricing and toward models directly tied to outcomes, such as charging per task or completed transaction. This approach helps customers feel like they’re only paying for measurable results, he said.
“Companies are forced to set prices three to five times higher than their return on investment. Value capture is at the center of almost every argument,” Chandramouli said.
He cautioned, however, that transaction-based pricing can create hidden risks. If a company underestimates the true cost of each successful transaction, unit profitability can quickly erode.
For the move to ROI-based pricing to work, Chandramouli believes startups will need to carefully analyze each contract – clarifying what counts as a successful action, what fees are fixed or variable, and how realistic projected revenue is.
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