Why Rapid AI Adoption Doesn’t Guarantee Immediate Returns in Clinical Settings 

Even when AI produces real gains, proving a direct financial return in the short term can be difficult. 

“Clinical outcomes — such as reduced readmissions, earlier diagnoses and lower mortality — play out over long horizons and require enough patient volume to be statistically meaningful,” explains Atif Chaughtai, global vice president of strategic ecosystem sales at Strategy.  

Chaughtai points out that when AI reduces the time a provider spends on paperwork, that only translates into a financial return if it’s converted into more visits or less overtime. “Relief from burnout is valuable, but it rarely appears as a line item,” he says.

Whether workers actually adopt the technology is another critical factor impacting ROI. If physicians and other providers weren’t included in discussions about which AI tools would be most helpful, for instance, and a new platform doesn’t integrate well with existing workflows or causes alert fatigue, then workers might not use it.

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How Healthcare Leaders Should Calculate AI ROI

A strong data governance infrastructure is vital for tracking data over time to prove healthcare AI ROI. Establishing a baseline before deployment will help IT and medical leaders demonstrate how an AI tool is creating a benefit over months and years.

In the short to mid-term, leading indicators — such as documentation time per encounter, error rates, after-hours charting and clinician adoption — “let us judge whether an investment is on track early and decide whether to scale it,” says Chaughtai, explaining that true ROI then comes from monitoring lagging outcomes, such as cost per patient case and worker retention.

Healthcare leaders can also move beyond financial ROI to demonstrate AI benefits for both the organization and the communities it serves. Alex Sushko, an account executive for regulated industries at Glean who specializes in AI integration, suggests connecting AI use cases with the health system’s stated goals.

“Map every AI use case to a goal,” he says. “For instance, how can we use AI to better monitor heart failure patients so they don’t end up in the emergency room? Measuring ROI by hours saved or costs cut is important, but why are you taking these steps? What is it all leading to? Tie ROI into the hospital’s overall mission.”