AI seems easy. Until you actually do it.
Building an AI demo takes an afternoon. Getting AI to work inside a real healthcare organization takes much more.
The demo and production are different things
Anyone can build an AI demo in an afternoon: a prompt, a model, a chat window. Putting it into a real healthcare organization is a different story. Production means EHRs and billing systems, data warehouses, legacy SaaS, and security and compliance.
The use cases are real
- Prior authorization
- Clinical documentation
- Claims processing
- Patient intake
- Document and image understanding
But they live inside EHRs, billing systems and legacy SaaS, not a chat window. Those systems were never built to talk to AI.
An API call is not a product
- Token management across models and infrastructure
- Agent frameworks for RAG, workflows and multi-step agents
- SSO and access control, so the right people see the right data
- HIPAA compliance at every step of every data flow
- Connectors to legacy systems and SaaS platforms
- Vision for documents, images and forms
None of it comes out of the box, neatly stuck together. That is where most enterprise AI efforts stall.
The hidden risk: lock-in to one model company
Many agentic frameworks lean toward one frontier model company. When a better or cheaper model arrives six months from now, how do you undo that?
The model is one layer
Enterprise AI isn't just signing up for an enterprise AI plan. A working system has five layers, and the model is only the bottom one:
- End-user experience
- Agents and workflows
- Security, SSO and HIPAA
- Connectors and data flows
- Models: GPT, Claude and more
ROI comes from the mechanisms that connect the model to your IT backbone.
One platform across the whole stack
Any model. Your systems. Your data. Your compliance. From agent building to a unified, familiar chat experience your teams will actually use. At Inference Analytics that means Dev Studio for building and governing agents and Inferch as the multi-model workspace, both on one HIPAA-compliant core.
The hard part of AI was never the demo. It's everything after.
