Healthcare Case Studies
Data Analytics & Health Risk Stratification
Goal: A healthcare insurance company aimed to increase enrollment in care management programs to engage high-need members, meet federal contract requirements, and improve health outcomes for those at high risk.
Action: As senior program leader, I directed a cross-functional team of domain experts to develop data pipelines and risk models that stratified 318K members by health risks and behavior. The models helped clinicians quickly identify and enroll priority individuals in care programs.
Results: Clinicians were able to target the right members for care programs exceeding contract enrollment goals, resulting in incentive payouts for the health insurer.
Automation of Medical Record Reviews
Challenge: Clients for a healthcare insurance company demanded reductions in their insurance costs without compromising care quality and outcomes for covered employees.
Action: As the product owner, I directed a proof of concept for a custom AI solution using NLP and machine learning to enhance medical record review and pre-authorization recommendations. The process included clinician validation, explainability features, and compliance checks to ensure industry standards were met.
Results: Proof of concept product solution led to a 27% reduction in review time for clinicians, enabling a new capability to "do more with existing staff”. I secured approval of $8.9M multi-year business case.
Manufacturing Case Studies
Enterprise AI Architecture & Infrastructure
Goal: A North American manufacturer aim to adopt industrial AI to optimize and extend its production capabilities.
Action: I was responsible for developing and executing the AI strategy, including selecting and implementing enterprise AI infrastructure, tools, and platforms to advance the manufacturing technology.
Results: After assessing various technologies, I secured over $3M in funding and led the deployment of a SMART manufacturing ecosystem, integrating cloud AI/ML platforms, digital twin tools, and on-prem data sources. This enabled enterprise-wide intelligent automation, advanced analytics, and AI/ML solutions to improve manufacturing processes and product quality.
AI Core Competency
Goal: To create a scalable, repeatable, and agile AI/ML development process that was responsive to business needs and ensure production grade manufacturing IT solutions.
Action: I designed and implemented the AI/ML development and QA process while my team upskilled 20+ manufacturing engineers in building robust ML solutions.
Results: Process engineers sharpened their data science skills, started creating impactful production AI/ML solutions, and participated in the AI community of practice. Equally important, this established an AI core competency and reduced the company's dependency on hiring expensive external AI professionals.
Defining AI Strategy to Power Digitalization
Challenge: A supply chain and manufacturing company aimed to modernize IT systems, advance its digital transformation efforts by integrating enterprise AI for better products, services, and operational efficiency.
Action: I partnered with leadership to thoroughly assess AI readiness across technology, governance, data, security, and skills.
Results: I delivered a custom, strategic AI roadmap aligned with company goals, which launched AI investment initiatives focused on operational efficiency, workforce upskilling, and establishing AI and data governance processes as well as tools.
Upskilling & Productivity Gains with GenAI
Opportunity: An industrial supply chain company new to GenAI sought to assess the feasibility and benefits of using GenAI tools for staff and address related data security concerns. Staff and management had little experience with these technologies and were unclear about the impact and value on their daily workflows and tasks.
Action: I directed a 10-week Microsoft Copilot proof-of-concept with a cross-functional team, focusing on GenAI best practices, enterprise integration, and agent development. The project delivered insights on user adoption and data security within Copilot and GenAI models.
Results: POC participants saw a 43% productivity gain, saved 50 minutes weekly, and 67% reported positive impacts on workflows. The POC exceeded some industry benchmarks, helping to prove GenAI’s business value and justify staff upskilling.