Healthcare leaders across the country are moving quickly to adopt artificial intelligence. Boards are asking about AI strategy. CIOs are evaluating vendors. Product leaders inside electronic health record companies are building roadmaps for AI-powered capabilities.
Most of these conversations begin with the same question:
These questions are understandable. But they focus on the wrong level of the problem.
The next power shift in healthcare will not happen at the application level. It will happen at the intelligence layer that orchestrates how information moves across the system. The organizations that control that layer will shape how healthcare operates. Those that do not will increasingly find themselves managing a growing ecosystem of vendors.
Healthcare is adopting AI one task at a time.
Documentation. Coding. Messages. Workflows.
Each tool works. That’s not the issue. The issue is what happens when you step back. You now have multiple systems making decisions:
They don’t move together. So instead of one smarter system, you’re managing a collection of tools that all operate slightly differently.
That’s not transformation. That’s fragmentation.
There’s a reason so many AI initiatives don’t make it past early rollout.
Industry research shows nearly 70% of healthcare AI projects stall in pilot, not because the models don’t work, but because the systems around them don’t.
At the same time, 85%+ of healthcare organizations are actively exploring or deploying generative AI, and 86% of health systems report some level of AI in production.
Adoption is moving fast. Execution is not.
In conversations with CIOs, this is the real concern: not whether AI works, but how quickly it becomes difficult to manage.
On paper, most AI tools look manageable.
In practice, each one brings:
Healthcare organizations already manage 100+ SaaS systems on average.
AI is accelerating that number.
A $1M AI investment rarely stays $1M.
It often becomes 2–3× that once fully operational.
Now multiply that across multiple tools. At some point, you’re not adding capability. You’re building a second system just to manage the first one.
This isn’t a future problem.
Across large physician groups and health systems, it’s common to see 4–8 AI vendors already in use, often without a clear plan for how they work together.
Each tool solves a piece of the problem, but none solve it end-to-end.
The result:
Even clinicians are feeling it. Over 70% say better connectivity across systems is critical to reducing complexity in care delivery.
The result isn’t progress. The system gets harder to run, not easier.
For EHR platforms and large healthcare networks, this shift is bigger than efficiency.
If AI lives outside your system, something subtle, but significant, happens.
The place where decisions are made is no longer the place you control.
It doesn’t happen overnight. But it’s how platforms lose relevance, and how networks lose consistency.
Every major technology shift follows the same pattern.
Mobile didn’t belong to the companies that made phones. It belonged to the companies that controlled the experience.
Cloud didn’t belong to the companies that owned servers. It belonged to the companies that made everything run on top of them.
In each case, control moved to the layer that connected everything.
Not the tools.
Not the infrastructure.
The layer in between.
Healthcare is heading in the same direction.
This is what EHR leaders and healthcare networks should be deciding right now:
These aren’t technical decisions.
They define how your platform evolves.
How your network performs.
And how sustainable your operations become at scale.
For both EHR platforms and healthcare networks, the path forward comes down to a few critical decisions:
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