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Our May blog series explores one of the biggest questions in healthcare AI right now: build or buy? We’ll examine what it takes to bring AI into production, the operational realities that come with it, and how to make the right decision for your organization.
The Demo Trap
The trouble with demos starts when they work.
They make it feel like the hard part is over, when it’s really just beginning.
Demos live in controlled environments, with clean inputs, limited variability, and no real operational pressure. The real world is nothing like that.
A demo shows it can work, but production proves whether it holds up.
Where Things Get Real
Once AI moves into real workflows, the cracks begin to show.
Data varies, systems don’t always behave, and edge cases emerge. What works in a demo doesn’t hold up across hundreds or thousands of interactions.
At that point, something becomes clear. This isn’t a feature you plug in. It’s a system, one that connects scheduling, intake, EHRs, and communication workflows.
That shift, from tool to system, is where most approaches fall short.
As UnityAI’s Director of Product, Matt Powell, explains:
“Most people picture an AI agent as a clever set of instructions wrapped around a model. We’ve designed a system instead of a single-purpose set of instructions.
When OpenAI or Anthropic ships a new model that follows instructions differently than the one before, an agent built on static instructions breaks. A system adapts.
In healthcare, when it breaks, it matters. A missed step isn’t a minor issue. It’s a missed appointment, a delay in care, or a breakdown in coordination."
It has to work—every time.
AI Doesn’t Sit Still
Traditional software has a relatively stable foundation. AI does not.
The underlying models change, APIs evolve, and performance shifts. The same input can produce different outputs across versions. At the same time, scope expands. New workflows are added, edge cases surface, and integrations expand.
In other words, what you built doesn’t stay contained. It’s constantly changing and growing.
From Build to Burden
That’s when the work shifts. What looked like a feature becomes an operational responsibility.
Teams now have to monitor performance, validate outputs, update integrations, and manage compliance.
They also inherit the gray areas—when something is technically correct but contextually wrong, when to trust the system and when to intervene, and how to build guardrails, audit trails, and escalation paths for when it goes off script.
This is where many efforts begin to break down.
In the early 2010s, IBM’s Watson was positioned as a breakthrough in healthcare AI but struggled in real-world deployment, where complexity, data quality, and workflow integration proved harder than expected.
In the late 2010s, Google Health saw similar challenges. Its diagnostic AI performed well in controlled settings, but real-world adoption stalled by the complexity of integrations into daily clinical workflows.
More recently, Olive AI ran into the same situation. It aimed to automate hospital operations at scale, but what worked in pilots didn’t hold up across fragmented systems, ultimately proving difficult to sustain the business.
The pattern is consistent: building the model is one challenge, but running it in the real world is something else entirely.
The Real Tradeoff
That burden leads to a question. What do you actually want to own?
Where do you want to spend your time? What’s core to the business and what isn’t?
There’s a reason healthcare organizations don’t build their own EHRs, payment systems, or drug supply chains from scratch. These systems are too complex, too critical, and too far removed from core expertise.
The same is true of AI.
Once AI becomes part of how work gets done, it starts to shape everything, from access to outcomes to the day-to-day operations.
At that point, the infrastructure isn’t behind the product. It is the product.
So the real question isn’t whether you can build AI. It’s whether you want to run it.
And whether it’s something you should own. What’s core to your business—and what’s not?
UnityAI exists so healthcare organizations can use AI without the burden of operating it.
