PartsSource has appointed Nick Marzotto as Vice President of AI and Data, tasking the 15-year Epic Systems veteran with building an intelligence layer that connects fragmented clinical asset data across health systems. The move signals a strategic push to apply predictive AI to the technology infrastructure that underpins patient care - moving hospital teams from reactive maintenance to proactive decision-making at enterprise scale.
From patient records to clinical assets
Marzotto spent a decade and a half at Epic Systems, most recently as Vice President of Clinical Applications and Artificial Intelligence. There, he led a generative AI initiative that drove a 32x increase in adoption across 500 health systems. He also worked directly with health system customers to build the governance frameworks for monitoring and responsibly deploying AI. His appointment at PartsSource reflects a broader bet: that the same data unification the EHR brought to patient information can now be applied to the medical devices and equipment those patients depend on.
"Over the past two decades, the EHR has transformed healthcare by bringing previously fragmented patient information and clinical workflows into a connected digital environment," said Philip Settimi, President and CEO of PartsSource. "We believe a similar transformation is now possible for the clinical asset environment."
Connecting fragmented asset data
Health systems operate large fleets of connected medical technology - imaging machines, infusion pumps, ventilators - but the data about those assets sits in silos across manufacturers, service vendors, and internal workflows. PartsSource's platform aims to create a unified data and intelligence layer that turns those scattered signals into operational decisions. The company serves more than 5,000 hospitals and 15,000 clinical sites with software that spans parts, service, asset, and workforce solutions.
Marzotto described the opportunity in terms of a shift in mindset. "By bringing together asset data across vendors, modalities and sites of care, and applying AI to that data, we can move from understanding what happened to anticipating what is likely to happen," he said. "This will fundamentally transform teams into a proactive mindset instead of reactive."
AI as foundation, not feature
Settimi was direct about how the technology fits into the product roadmap. "AI will not be a feature bolted onto our platform," he said. "It will increasingly be part of the foundation for how PartsSource turns data into better decisions, better workflows and ultimately greater clinical capacity for our customers." Marzotto will work across the product and technology organization to advance machine learning, data science, and intelligent workflow automation throughout the platform.
For executives tracking how AI strategy translates into organizational structure, the hire is a concrete signal. PartsSource is embedding AI leadership at the VP level with a mandate that spans product, data, and implementation - not creating a separate innovation lab disconnected from core operations. This mirrors a pattern seen across AI for Healthcare, where operationalizing the technology requires deep integration with existing clinical and supply chain workflows rather than standalone experiments.
Why this matters for executives and strategy
Marzotto's move from a dominant EHR company to a clinical asset platform signals where the next wave of healthcare AI investment is heading: into the physical infrastructure layer that has lagged behind digital patient records. For strategy leaders, the takeaway is not about one hire. It is about the model - appointing a senior executive who bridges product, data science, and customer governance, with a mandate to make AI part of the platform's foundation rather than a separate initiative. The 32x adoption metric from Marzotto's prior role also underscores a practical reality: technology deployment matters as much as model development. Governance infrastructure and change management with customers determined whether the tools got used. Organizations building their own AI for Executives & Strategy should note that adoption velocity depends on those organizational factors, not just algorithmic performance.
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