Health systems are rapidly adopting AI, but few have successfully scaled those investments into measurable results, according to an April 9 report from Qventus.
The report, based on a survey and one-on-one interviews with more than 60 CIOs, chief AI officers, CMIOs and other senior IT leaders, found that just 4% of organizations have achieved scaled AI implementation with measurable outcomes, even as adoption accelerates across the industry.
While 42% of health systems report deploying AI across multiple use cases, many remain stuck between pilot programs and full enterprise rollout. In this phase, return on investment remains difficult to prove.
This gap is creating mounting pressure on technology leaders. About 65% of respondents rated the pressure to operationalize AI as 7 or higher out of 10, signaling urgency to move beyond experimentation.
Despite growing investment, healthcare AI spending reached $1.4 billion in 2025, yet most organizations are still struggling to quantify value.
Four out of five respondents said they have difficulty measuring AI ROI, and 39% reported lacking a clear process for benchmarking performance. At the same time, expectations remain high, with 74% of leaders saying they need to demonstrate ROI within a year to justify continued investment.
This disconnect is compounded by long implementation timelines, with 26% citing delays in achieving measurable results as the greatest barrier to proving AI’s value.
Reliance on electronic health record vendors also continues to hinder execution. The report found 74% of CIOs cite dependence on their EHR vendor’s AI roadmap as a top barrier.
As a result, some organizations are shifting strategies. When given the choice between waiting 18 months for an EHR-based AI capability or deploying a third-party solution in three months, 40% said they would choose the faster option, 38% said it depends and 22% said they would wait.
Managing multiple AI vendors is also emerging as a significant operational challenge. Two-thirds of respondents said vendor management is straining IT resources, with 51% of organizations dedicating 11% to 25% of IT bandwidth to integrations and implementations.
In response, CIOs are increasingly looking to consolidate vendors. Seventy-two percent said they would prefer to work with a single, comprehensive AI partner, though only 11% have achieved that model.
As organizations refine their AI strategies, leaders are prioritizing fewer, higher-impact use cases. More than 70% of respondents said automated care operations platforms are “very critical” or “mission-critical” to their 2026 objectives.
Top near-term opportunities include patient scheduling and access, cited by 72% of respondents, and patient flow coordination, cited by 58%.
The report underscores a growing sense of urgency among healthcare leaders. Ninety-four percent said delays in AI operationalization would put their organization at a competitive disadvantage, while 68% said delays could worsen clinician burnout and turnover.
As health systems face ongoing margin pressure, workforce shortages and rising demand, the ability to operationalize AI, not just pilot it, is becoming a defining factor in performance.
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