Rethinking Healthcare Velocity: The AI-Native Engineering Advantage
A Genzeon + Sharecare webinar featuring Douglas Jones
July 16, 2026 · Genzeon × Sharecare
Speakers:
Douglas Jones — Sr. Director of Engineering, Sharecare
Prashant Krishnakumar — CTO Services, Genzeon
Pranita Rai — Client Partner & AVP Healthcare, Genzeon (moderator)
“AI-native is not taking the human out of the loop. In AI’s current state, the human is more critical than ever.”
— Douglas Jones, Sr. Director, Engineering, Sharecare
My Perspective
One of the themes I emphasized in this conversation is that becoming AI-native is not primarily about giving developers better tools. It’s about redesigning the engineering operating model around a fundamentally different implementation constraint.
For decades, software organizations have been structured around human production velocity. Requirements, development, code review, testing, security review, and release processes all evolved around how quickly people could create and evaluate software.
Agentic engineering changes that assumption.
As implementation accelerates, the question shifts from “How quickly can we produce code?” to “How do we preserve intent, quality, security, domain judgment, and accountability when production moves faster than traditional human review can absorb?”
Much of my work at Codifide — including Stage-Gate-Loop and Loop Engineering — grew from wrestling with that problem in production environments. The governance framework isn’t theoretical. It emerged from real engineering organizations navigating the transition from AI-assisted to AI-native.
The other theme worth highlighting: people come first. Adoption should never begin as a mandate. Curiosity scales; mandates don’t. The teams that succeed start by investing in their people — building understanding of the new vocabulary (agents, skills, context windows, orchestration, guardrails, evals, token economics) before expecting anyone to build systems around it.
Key Topics Discussed
- The AI-native maturity spectrum — from individual AI experimentation through the "power-tool era" to genuinely AI-native engineering
- People before technology — why adoption starts with curiosity, training, and early champions, not mandates
- SDLC redesign — when AI is the fulcrum of the development process, every assumption about sprints, reviews, and handoffs changes
- Governance at AI speed — rebuilding checks and balances for a world where agents produce output faster than traditional review cadences
- Token economics — matching the right-sized model to each task rather than defaulting to the most expensive option
- Human accountability — human always in the loop, zero PHI in prompts, nothing to production without review
External Resources
The Genzeon recap includes publicly reported outcomes from the engagement.
Related Codifide Research
- Agentic Stage-Gate-Loop Governance — how checks and balances change when implementation moves at machine speed
- Loop Engineering — why repeatable verification becomes more important as AI accelerates implementation
- AI Security Changed in 90 Days — why security governance has to operate at the same velocity as AI-native engineering
- Agentic SDLC Governance — the system of controls required when AI agents produce software
- Building a Regulated Healthcare Platform — production lessons from the same domain