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SUCCESS STORY
How a global healthcare organization scaled AI across care for 5,000+ physicians and 7,000+ nurses
A Digital Health and AI Center of Excellence embeds reusable AI capabilities into clinical and operational workflows enterprise-wide
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Most enterprise AI efforts stall the same way: promising pilots that never scale because there's no unified data foundation, no reusable framework, and no governance structure to move fast without cutting corners on clinical accuracy. This organization took a different approach, standing up a Digital Health and AI Center of Excellence to unify data, build reusable AI accelerators, and deploy solutions ranging from virtual nursing assistants to discharge optimization and clinical trial matching, all under one governance model. This success story breaks down how that foundation was built and what it enabled across the care continuum.
Key takeaways:
- How to establish a Center of Excellence that drives enterprise AI strategy, governance and adoption together
- Ways to build reusable AI accelerators that scale high-value use cases faster across the organization
- Approaches to embed AI into clinical workflows without adding burden to physicians and nurses
- Tactics to balance rapid GenAI innovation with governance, so speed doesn't come at the cost of clinical accuracy
Frequently Asked Questions
What is a Digital Health and AI Center of Excellence?
It's a centralized structure that governs an organization's AI strategy, standards, and adoption across departments, rather than leaving AI initiatives to run as disconnected, one-off projects. A CoE typically owns the data foundation, reusable AI components, and governance model that individual use cases then build on, which is what allows AI to scale enterprise-wide instead of staying stuck in pilot mode.
Why do enterprise AI initiatives typically fail to scale past the pilot stage?
The most common reason is fragmentation: disconnected data ecosystems, one-off frameworks built for a single use case, and no standardized way to reuse what already works. Without a unified platform and reusable components, every new AI use case effectively starts from zero, which limits how fast an organization can move and how many use cases it can support at once.
What are reusable AI accelerators, and how do they speed up healthcare AI deployment?
Reusable AI accelerators are pre-built, modular AI components (data pipelines, models, integration patterns) designed to be applied across multiple use cases rather than rebuilt each time. Instead of engineering a new solution from scratch for every clinical or operational need, teams can assemble accelerators to stand up new use cases faster, which is part of how this organization was able to deliver solutions spanning expert search, EHR personalization, virtual nursing and discharge optimization under one foundation.
How is AI embedded into clinical workflows without disrupting clinicians?
The goal is to bring AI-driven insights directly into the tools clinicians already use, like the EHR, rather than adding a separate system they have to check. In this engagement, that meant personalized, contextual EHR experiences and decision support built into existing workflows, so the AI reduces administrative burden instead of adding another step to a clinician's day.
How much can AI-enabled clinical documentation actually save per patient?
Savings depend heavily on documentation volume, clinical specialty, and how deeply AI is embedded into the workflow, but in this engagement, AI-enabled clinical documentation delivered approximately $3,200 in savings per patient annually. Want to understand what that could translate to at your patient volume? Consult with our experts to work through the numbers.
Can AI meaningfully reduce hospital discharge delays?
Yes, when AI is applied to discharge planning and care coordination specifically, surfacing the information and flagging the bottlenecks that typically slow discharge down. In this engagement, that translated to discharge delays reduced by up to 8 hours, a meaningful improvement for both patient flow and bed availability.
How do healthcare organizations balance AI innovation speed with governance and clinical accuracy?
This is typically the hardest part of enterprise AI, moving fast enough to capture value while ensuring every AI-driven recommendation is clinically sound and auditable. The answer is usually structural: pairing an incubation and rapid-prototyping model for new use cases with a centralized governance function (like a CoE) that reviews and monitors solutions once they're in production, rather than treating speed and oversight as competing priorities. If you're figuring out how to structure that balance for your organization, consult with our experts to talk through it.
What kinds of AI use cases can an enterprise AI foundation support at once?
When the underlying data and AI foundation is built to be reusable, it can support a wide range of use cases simultaneously rather than being locked into a single application. In this engagement, the same foundation powered expert search, personalized EHR experiences, virtual nursing assistants, discharge optimization, clinical trial matching, and AI-enabled revenue cycle insights, all running on shared infrastructure and governance.
