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Gus Iversen, Editor in Chief | April 29, 2026
A new industry report from the Indianapolis-based clinical asset management firm TRIMEDX highlights a growing disconnect between the rapid advancement of artificial intelligence and health systems’ ability to implement it at scale.
The report, based on discussions with health system executives, describes an industry still constrained by labor shortages, legacy infrastructure, and regulatory complexity. While AI is often framed as a transformative tool, its adoption remains uneven, with many organizations limited to pilot programs rather than enterprise-wide deployment.
“Health care is particularly vulnerable to AI driven disruption due to its heavy reliance on human labor, vast data volumes, and slow adoption of technological advancements,” said Eric Larsen, president of TowerBrook Advisors.
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The report argues that moving beyond experimentation will require clearer governance, fewer but more integrated technology partners, and stronger executive involvement. Without those elements, organizations risk falling behind as early adopters build operational advantages.
Operational use cases, such as asset management, supply chain, and administrative workflows, are identified as more practical entry points for AI than direct clinical applications. These areas offer lower risk and clearer performance metrics, including equipment uptime and inventory accuracy. According to the report, some health systems using AI-enabled asset intelligence have achieved more than 99% equipment uptime and reduced unplanned downtime significantly.
Trust in AI tools also emerged as a key barrier. “A poor first experience can set adoption back months. Trust builds faster when AI behaves like an assistant, not a supervisor,” said Steven Martin, chief technology officer at TRIMEDX.
Beyond technology,
the report points to ongoing supply chain instability as a parallel concern. Health systems are shifting priorities from cost alone to resilience, emphasizing supplier transparency, redundancy, and real-time visibility.
The findings suggest that successful adoption will depend less on individual technologies and more on organizational readiness, including data quality, leadership alignment, and workforce engagement.
