Our Take
The problem is not AI. It's that teams build demos instead of systems, skip the infrastructure, and let vendors measure their own success.
Why it matters
Healthcare organizations are burning budget on prototypes that solve surface problems without fixing broken workflows underneath. The difference between a failed pilot and a scaled system is governance, not better models.
Do this week
Your data/AI leads: identify one active AI project and name the clinical or business owner who will be fired if it fails—if nobody answers, shut it down before next quarter.
Why AI pilots become abandoned projects
An MIT study found that over 95% of AI initiatives never reach production or deliver measurable return on investment. Michael Privat, chief data and engineering officer at Availity, an IT services and consulting firm, estimates the failure rate in healthcare is even higher. The pattern is consistent: teams ship prototypes quickly, generate internal excitement, then hit a wall the moment they try to operationalize. Real patient data, regulatory compliance, security requirements, and governance constraints make the gap between proof-of-concept and production work.
Most organizations skip the foundational layer. They acquire a model, notebook it up, call it a pilot, and declare success. True operationalization requires data lineage and identity resolution at production scale, audit trails that satisfy HIPAA and SOC 2 compliance, and a continuous evaluation framework. A model that performs in January will silently degrade by July if no one is monitoring it. Drift is real and constant.
Three failure modes are predictable and avoidable
The biggest trap: using AI as an additive layer on top of a broken process. AI amplifies what exists. If your prior authorization workflow has 14 handoffs and a two-week queue, generative AI will produce more material for the same queue. You have automated the wrong bottleneck.
The second pitfall is scoping pilots to demo rather than to deliver. Clean data, hand-picked cases, a special team, no integration constraints. When integration time arrives, the cost eats every dollar of margin.
The third: letting the vendor define success. If the only people measuring the model are the people selling it, you have marketing, not evaluation.
Projects that survive have two things: an accountable owner on the business or clinical side (not the engineer who built it, not the vendor who sold it), and a real metric tied to profit-and-loss or patient outcomes, not engagement counts or queries served. They are also designed to end. When the problem changes, the initiative ends or evolves. Projects without exit criteria become infrastructure departments with no one able to shut them down.
Scaling requires consolidation, not novelty
Organizations that run AI at scale use one inference platform, one evaluation framework, one identity and data model, and one set of guardrails across teams. Standardization is what makes AI scale; novelty is what makes it expensive. Availity runs on AWS Bedrock across roughly 60 engineering teams precisely because no team is building its own stack.
Governance must be infrastructure, not a committee. If every new use case requires three reviews, two architecture diagrams, and a steering meeting, you have a toll booth, not governance. Capability gates, behavioral guardrails, and audit trails enforce policy at the platform level so teams ship without waiting for human approval.
End-to-end observability is not the same as monitoring. Monitoring tells you the model is responding. Observability tells you whether the response is still correct. You need input drift detection to know when training data has shifted, output quality signals tied to continuous evaluation, latency and cost per call, and full audit trails of every prompt, response, and decision. In healthcare, regulators will ask you to reproduce why a specific case was handled a specific way. "The model said so" will not survive scrutiny.
The model you launched a year ago is not the model you have today. Without observability, you are paying for software you can no longer describe.