How Health Systems Are Measuring ROI for AI Tools in 2026
According to Michael Muecci, CEO of the health data platform Arcadia, health systems are changing their thinking about ROI for AI projects. With providers facing tighter budgets, they are broadening how they measure AI’s ROI.
Using Internal Data to Guide ROI for AI Tools in 2026
The market is flooded with various vendors pitching different AI solutions. However, Meucci suggested that health systems should use their own data to identify problems and find inefficiencies. It would then be wise to address those specific issues using AI tools that fully cater to them. Earlier this month, during an interview at the HIMSS conference in Las Vegas, Muecci stated:
“What I keep encouraging my customers to do is use the data they have to tell them, ‘Hey, you’ve got clinical variation over here. You should go look for a solution to reduce that because the ROI opportunity could be X.’ It’s changing from an inbound demand to a data-driven project.”
According to Muecci, this approach helps organizations to benchmark outcomes and properly attribute ROI among different AI tools.
Expanding What Counts as ROI for AI Tools in 2026
With health systems becoming more disciplined in evaluating technology, they are also broadening how they define ROI for AI tools, as mentioned. Meucci pointed out that not all AI solutions can produce immediate financial returns, for example, increased visit volume. However, it can still generate meaningful value.
He further explained that AI tools can also reduce administrative burden while improving physician satisfaction and retention. While such outcomes may not appear directly in revenue findings, but they can significantly contribute to overall organizational performance.
This expanded definition reflects a shift in how health systems evaluate success, moving beyond purely financial metrics to include operational and workforce-related outcomes.
Ambient Documentation and Its Impact
Meucci used a clear example of ambient documentation to highlight how AI contributes to ROI for AI tools. Physicians are able to reduce the time spent on documentation with solutions from companies like Microsoft, Abridge, and Suki.
These tools significantly ease clinician burnout by lowering administrative workload and, therefore, contribute directly to improving job satisfaction. At scale, this has meaningful implications for retention. According to Meucci, replacing a physician can cost about $1 million, which suggests that even small improvements in retention can lead to significant cost savings for health systems.
This makes clinician-focused AI tools an important part of how organizations evaluate long-term ROI, even if the immediate financial impact is not always obvious.
AI as Part of the Physician Experience Strategy
As per Meucci, AI is increasingly being incorporated into the broader physician experience strategy. Health systems are starting to view their technology stack as a way to support recruitment and retention, not just operational efficiency.
He compared this to the concept of “developer experience” in software, where organizations provide tools that allow engineers to work more efficiently. Similarly, health systems are evaluating technologies that reduce workload and make it easier for clinicians to focus on patient care.
In one example, Meucci described a large health system in Massachusetts that formed a workgroup focused on improving physician experience. This group evaluates tools such as ambient documentation and computer-assisted coding solutions to help reduce administrative burden.
Interoperability and Flexibility
Meucci also noted that interoperability is improving, which in turn is influencing ROI for AI tools. Open APIs, standardized data formats, and more unified data infrastructures are making it easier for health systems to integrate and switch between AI vendors.
With this increased flexibility, organizations are able to deploy new tools more quickly without being locked into a single platform. Health systems can pilot different solutions, compare results, and replace tools that do not deliver sufficient ROI.
Over time, these lower switching costs encourage more experimentation and faster adoption of effective AI models, helping organizations continuously refine their technology stack based on measurable outcomes.
Final Thoughts
With all of the above, these trends are pushing health systems toward a more structured and data-driven approach to evaluating ROI for AI tools in 2026. Instead of relying on vendor claims or isolated financial metrics, organizations are increasingly using internal data, benchmarking outcomes, and considering both operational and workforce-related benefits.
This approach allows health systems to better identify where AI can create value, measure its impact more accurately, and ensure that investments are aligned with real organizational needs.
