We are a well-funded Medical AI startup (cancer detection) with an innovative tool that physicians loved during the pilot phase. However, as we move toward broader adoption, we’ve hit a wall.
You likely have clinical validation... not scalable adoption.
In Medical AI, pilots prove performance. Scaling depends on structural alignment:
1. Economic Buyer
Doctors liking it is insufficient. If the budget holder (hospital admin, procurement, payer) doesn’t see direct ROI, adoption stalls.
2. Incentive Structure
If reimbursement is unclear or financial upside isn’t explicit, usage plateaus after pilots.
3. Workflow Friction
Any added complexity in EHR/PACS integration or clinical workflow reduces expansion velocity.
4. Sales Repeatability
Pilot success is often founder-driven. Scaling requires a standardized, multi-stakeholder enterprise sales motion.
In short:
You solved for accuracy.
You haven’t fully solved for institutional economics and distribution.
This is usually not a technology issue but a commercialization gap. Physicians may love the product, but hospital procurement, finance, IT, and compliance teams make the buying decision, and without a clear reimbursement pathway or proven cost savings, adoption stalls. Workflow friction, integration challenges with existing systems, medico-legal concerns, budget cycle timing, and overreliance on pilot champions rather than institutional buy-in often prevent scaling. In short, you may have technical product-market fit, but not economic and operational product-market fit across the broader healthcare system.
Did your pilots cover different market/ user/ regulatory segments? Perhaps try a dipstick with a totally different segment of audience or market and see what comes of it. Maybe you need a tiny pivot.
