“Production AI” has become a loaded phrase.
For some, it means automation at scale.
For others, it means fully autonomous systems.
And for many healthcare leaders, it simply means something they can rely on without losing sleep.
This post is an attempt to define production AI in plain language – based on what we’ve seen work, fail, and mature inside real healthcare operations.
A simple definition
In healthcare, production AI is not defined by how advanced the technology is.
It’s defined by this question: “Can this system run every day, inside real workflows, with clear ownership and predictable outcomes?”
If the answer is yes, it’s production AI.
If the answer is “it depends,” it’s still an experiment.
What production AI is – and isn’t
Production AI is often misunderstood because it’s described in technical terms instead of operational ones.
Production AI is not:
- a successful pilot
- a high-performing model in isolation
- a tool that works only under ideal conditions
- a system no one knows how to override
Production AI is:
- designed to operate continuously, not occasionally
- integrated into existing workflows and systems
- governed with clear accountability and escalation paths
- built to handle exceptions, not just happy paths
In short, production AI behaves like infrastructure – not like a demo.
The four characteristics of production AI
Across organizations that have successfully moved AI into production, we consistently see four shared characteristics.
1. Clear ownership
There is no ambiguity about who is responsible for outcomes.
When something breaks, someone knows – and someone acts.
2. Humans in the loop by design
People aren’t an afterthought or a safety net.
They are intentionally part of how the system operates, intervenes, and knows when not to act alone.
3. Failure is visible and recoverable
Production systems expect exceptions.
They surface issues early and fail safely instead of silently.
4. Success is measured operationally
The metrics that matter aren’t model scores.
They’re time to treatment, throughput, backlog reduction, and consistency over time.
If one of these is missing, the system may be impressive – but it isn’t production-ready.
Why this definition matters now
Healthcare doesn’t need more AI capability.
It needs more AI clarity.
When “production” is left undefined, organizations talk past each other. Vendors oversell. Buyers hesitate. Promising initiatives stall.
A shared definition gives leaders a way to evaluate AI honestly – without hype or fear.
It turns the question from “Should we try this?” into “Are we ready to run this?”
And that shift is what allows AI to become part of the operating model – quietly, responsibly, and at scale.
