Health systems are increasingly talking about the value of technology and AI, but the ability to demonstrate that value financially has not caught up, according to new research from Deloitte.
The Deloitte Center for Health Solutions surveyed 64 healthcare CFOs and finance leaders and separately analyzed more than 17,000 articles published by health systems and health plans between January 2023 and May 2026. The findings point to a growing divide between confidence in AI’s potential and organizations’ ability to attribute measurable financial results to the technology.
The survey highlights a gradual shift in how healthcare organizations talk about technology. Narratives citing demonstrated value increased from 9% of coverage in 2023 to 19% in 2026. Still, expected value remained the dominant framing, accounting for roughly 35% to 45% of coverage. When organizations did report demonstrated value, it was more often tied to operational or clinical improvements than clear financial outcomes such as cost savings, revenue growth or margin improvement.
In other words, health systems and health plans are increasingly talking about what their technology investments have accomplished, but communications about what technology could deliver remain far more common than evidence of what it has delivered.
That distinction is becoming more important as health systems scale AI investments and CFOs face growing pressure to quantify their returns.
Deloitte’s “2026 U.S. Healthcare CFO Survey” included 32 finance leaders from health systems with more than $1 billion in revenue and 32 from health plans with more than 500,000 members.
Forty-four percent of surveyed organizations qualified as “AI scalers,” meaning more than one-third of their generative AI initiatives launched during the previous two years had reached scaled deployment across multiple functions.
But among those organizations, just 18% reported mature financial attribution capabilities, meaning they consistently measure AI’s effect on revenue growth or cost savings using defined baselines and clear ownership of key performance indicators. By comparison, 31% of organizations earlier in their AI journeys reported that level of measurement maturity.
The challenge extends beyond Deloitte’s research. An April report found that while 42% of health systems had deployed AI across multiple use cases, just 4% had achieved scaled implementation with measurable outcomes.
The issue is not necessarily a lack of tangible results.
Some health systems are already attaching significant financial and operational outcomes to their AI investments. Chicago-based CommonSpirit, for example, generated more than $100 million in annual value from AI and robotic process automation in 2025 and now has more than 240 AI applications deployed across the system.
Boston Children’s Hospital has reported more than $7 million in redeployed labor savings after reclaiming about 60,000 hours through AI-enabled workflows. More than one-third of its employees use AI daily.
New York City-based Mount Sinai Health System has gone further in tying AI projects to specific financial targets. The system expects about $50 million in bottom-line impact from its AI portfolio in 2026 and has reported a return exceeding 3-to-1. Mount Sinai evaluates projects against metrics spanning financial impact, patient experience, operational efficiency, quality and safety.
Those examples show that measurable returns are possible. The broader challenge is creating a consistent framework for identifying and attributing that value across an enterprise.
That has become an increasingly prominent issue for health system technology leaders. In a recent Becker’s survey of more than 60 healthcare technology, financial, clinical and operational leaders, executives described technology ROI as increasingly multidimensional, extending beyond direct cost savings to measures such as workforce productivity, patient experience, quality and capacity.
The measurement gap could become more consequential as AI moves beyond experimentation. Deloitte found initiatives launched over the previous two years were roughly divided among those that had scaled across multiple functions, those remaining in pilots or limited use and those that had been paused or abandoned.
For CFOs, the next phase may therefore be less about whether their organizations are using AI and more about whether they can establish what those investments are producing.
That discipline could become particularly important as health systems face competing demands for capital and greater scrutiny of technology spending. Deloitte recently urged health systems to become more selective about AI investments, establish clear timelines and performance metrics for pilots and be willing to scrap initiatives that cannot demonstrate measurable value or scale effectively.
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