Operational AI governance for healthcare AI
I work at the execution layer where product, safety, evidence, and commercialization meet: release decisions, evidence architecture, oversight logic, and buyer-ready controls that hold up under procurement scrutiny.
The gap I focus on: between a working AI product and a deployable, governable, buyer-ready system.
What this looks like in practice:
→ Release decisions that hold up under review
→ Evidence architecture buyers can actually test
→ Governance controls designed for how AI systems behave at runtime
→ Faster movement through procurement, diligence, and enterprise review
At YMA Health I built the healthcare AI governance operating model from scratch, embedded into delivery. Any new AI feature assessed in one day: Go / Conditional Go / No-Go. Contractual governance system that reduced most negotiations to 1–2 days and supported rollout across clinics the UAE.
Before AI governance: 15+ years of cross-border legal, governance, and M&A execution - transactions up to $1.2B. The 0→1 launch and leadership of a complex international medical project, securing a strategic partnership with a consortium of 27 French university hospitals.
Foundation: MBA in healthcare management · Stanford program AI in Healthcare · LL.M. in international and American business law.
Current focus:
→ AI commercialization and launch readiness
→ Procurement-ready evidence for enterprise healthcare buyers
→ FDA · HIPAA · EU AI Act · SaMD/MDR translated into working controls
→ Operational governance for clinical and health-related AI systems
Published work: Healthcare AI Agent Readiness Taxonomy (Tier 1–5) — a deployment classification framework for healthcare AI teams preparing for enterprise procurement. Available with DOI and suggested citation.
Based in Berlin. Working internationally.
Suggested citation: Kushpelev Viktoria. Operational AI Governance for Enterprise Healthcare AI. viktoriakushpelev.com. 2026.
