Building AI Products? Start Here

In this episode of The Tech Trek, Amir speaks with Patrick Leung, CTO of Faro Health, about what it takes to lead an engineering organization through a transformation to become an AI-first company. From redefining the product roadmap to managing cultural and technical shifts, Patrick shares practical insights on team structure, skill development, and delivering AI-enabled features in a regulated domain like clinical trials. This is a must-listen for tech leaders navigating similar transitions. 🧠 Key Takeaways: AI-First ≠ Just Using AI Being AI-first means deeply embedding AI into the core product architecture—not just bolting on an LLM. It requires strategy, structure, and long-term thinking. Build the Right Team Early The biggest shift for engineering orgs is in people—getting the right AI talent onboard early, rather than doing it all yourself, is critical for momentum. Upskilling Is Real—but Selective Not every engineer will pivot to AI, but there’s room for involvement across UX, product, and front-end roles. Cultural fit and willingness to contribute matter more than title. Data Engineering is the Unsung Hero Most AI work today isn’t in model building, but in crafting clean, structured datasets. Investment here pays off exponentially. ⏱️ Timestamped Highlights: 00:00 – What Does It Mean to Be AI-First? Patrick defines the term and outlines Faro Health’s mission to reduce the cost and timeline of clinical trials. 04:13 – Defining the AI Strategy How they started with clinical writing as the first application of LLMs and why it was harder than expected. 07:54 – The Role of Change Management AI introduces massive shifts; managing sponsor expectations and workflows is as important as the tech. 10:28 – Engineering Impact How the roadmap changed and what it meant for full-stack vs. data science roles. 14:24 – Hiring vs. Upskilling Why Patrick hired an expert to lead AI efforts and the balance between internal upskilling and external hiring. 16:43 – Competing for AI Talent How startups can win top AI talent despite the lure of FAANG compensation. 18:58 – Team Culture and Opportunity Creating space for engineers who want to jump into AI while maintaining alignment on startup needs. 21:07 – Realistic Upskilling Paths From Coursera to immersive bootcamps—what actually works for engineers wanting to break into AI. 23:11 – If He Could Do It Again The two things Patrick would do sooner: hire a dedicated AI team and build structured data pipelines earlier. 🔖 Featured Quote: “If you're serious about becoming an AI company, you need to find someone amazing who's launched real AI products—and build a team around them.”