Representative Healthcare Product Team

The product and engineering leadership team had identified several high-potential AI use cases — including clinical note assistance, patient triage support, and internal knowledge retrieval — but had no clear framework for evaluating which ones were viable, and no shared understanding of what "production-ready" looked like in a healthcare context.

Privacy and governance concerns were at the forefront. The team needed to understand how to ground AI responses in enterprise data sources, how to manage prompt injection risk, and how to align with existing compliance obligations. The gap between what demos showed and what secure, governed enterprise deployment actually required was creating friction between product ambition and engineering confidence.

A two-day focused workshop was delivered to a cross-functional group including product managers, senior engineers, and a data privacy representative. The first day covered use-case framing — helping teams evaluate AI opportunities using a feasibility-governance-ROI lens — and introduced prompt engineering patterns, grounding architectures, and RAG design for healthcare data.

The second day shifted to implementation. Teams worked through prototyping exercises using Azure OpenAI in a sandboxed environment, applying responsible AI content filtering, reviewing security boundaries for PHI, and walking through a governance checklist for AI feature releases. The session concluded with a prioritized shortlist of three validated use cases with scoped next steps for each.