13 years of healthcare and drug development ownership. Contributor to three FDA-approved therapies. Architecting enterprise AI strategy where it meets clinical trial reality.
Readiness is a decision-improvement discipline, not a tool procurement exercise. Name the decision first.
Billions in discovery compute; retrospective spreadsheets in execution. A $535K amendment shows the gap.
The structural compliance map for sponsors and CROs deploying predictive models across EU sites.
North America (US including Puerto Rico, Canada, Mexico); Latin America (Argentina, Brazil, Chile); EMEA (UK, Belgium, France, Netherlands, Sweden, Finland, Denmark, Czechia, Switzerland, Hungary, Austria, Greece, Germany, Italy, Norway, Poland, Portugal, Spain, Romania, Russia, Ukraine, Turkey, Egypt, South Africa, Israel); Asia-Pacific (China, South Korea, Taiwan, India, Australia, Japan); 37 countries total.
Four flagship deliverables from the portfolio: an Enterprise AI Solution and Platform Mapping, an Enterprise AI Governance Framework, a Global Clinical Trial Inspection Readiness Playbook, and an Internal, External and Hybrid Change Management Guide.
An 18-solution enterprise AI portfolio mapped to platform capabilities, vendor landscape, and clinical use cases. Built to move sponsors from scattered pilots to a governed AI operating model.
Explore the mapping
A deployable governance framework aligned to the EU AI Act, NIST AI RMF, and ISO/IEC 42001. Policy, model risk tiers, validation, human in the loop controls, and lifecycle monitoring in one operating model.
Open the framework
Five commitments, ten inspection domains, and a 30/60/90 day operating plan. Built for FDA, EU CTR and EMA, MHRA, PMDA, and ANVISA inspections with audit defensible evidence trails.
Read the playbook
Internal change is adoption led, external change is trust led, and hybrid change is orchestration led. This guide matches the change strategy to the environment with readiness gates, a master narrative, and joint decision rights.
Open the guideFour operational domains where I write, advise, and build: AI in clinical trials, AI governance in drug development, EU AI Act clinical compliance, and generative AI in healthcare operations.
Predictive enrollment, protocol readiness, and site intelligence models only earn trust when the underlying trial design is sound. Explore how AI exposes broken protocols before it fixes them, and the real AI readiness question for clinical operations.
Managing model drift, black-box parameters, and human-in-the-loop dependencies is structural project work. Read why AI accountability is already project management work. The operating model most sponsors already have, but rarely apply to AI systems.
The EU AI Act reshapes explicit liability for sponsors and CROs deploying predictive models across international site networks. See the structural compliance map for liability under the EU AI Act.
Supercomputing is transforming discovery, but trial execution still runs on spreadsheets. Read: the drug got a supercomputer, the trial got a spreadsheet. Where generative AI actually pays off in clinical operations.
For over 13 years, I have owned clinical trial operations, from P&L management and cross-functional matrixed team leadership to executive governance across global Phase I-III programs. My work spans 37 countries and has contributed directly to three FDA approvals. The operating models I build protect margin, withstand inspection, and deliver under compressed timelines.
02I manage the commercial side of clinical delivery as directly as the operational side. That has included leading margin recovery efforts that returned seven figures in net savings, and designing funded enterprise initiatives with audited efficiency gains several times that size. Alongside trial delivery, I built and governed an 18-solution enterprise AI portfolio, carrying each solution from concept through executive approval while keeping up to 200 internal and sponsor stakeholders aligned.
03True acceleration in life sciences requires more than theoretical access to foundation models; it demands strict operational fluency and rigorous human-in-the-loop governance frameworks. Armed with specialized training in Agentic AI for Organizational Transformation from MIT, specialized healthcare microservice domain pathways from the NVIDIA Deep Learning Institute, and professional AI Governance training, I translate complex compute capabilities into high-fidelity, GxP-compliant clinical trial delivery frameworks.