AI Coding & AI-Assisted Delivery Model
After the development of multiple solutions with an AI-Coder, I would like to share methodology details that have made such efforts possible. While working with the machine, we are engaged in a continuous conversation that shapes both the solution and the pace at which it evolves. Let me introduce the AI-Assisted Delivery Model: allowing humans to be humans and machines to be machines. Successful…
The AI-Assisted Delivery Model enables humans to leverage their judgment and decision-making while machines handle code refinements and rapid iterations. This approach, detailed in the AI Center of Excellence (CoE) Operating Framework, allows iterative development without hefty change management or significant technical constraints.
Initial requirements focus on establishing direction and baseline architecture, with the solution evolving as work progresses. The division of responsibilities clearly separates human oversight from machine execution, ensuring velocity increases with AI tooling.
A critical phase before sprint execution involves setting up a solid foundation by resolving cloud access, IAM roles, SSO integration, API endpoints, schema definitions, and sample data extracts. This foundation work is essential to avoid delays caused by operational blockers. The framework prioritizes projects based on strategic alignment, business impact, data readiness, risk level, and executive sponsorship, minimizing governance bottlenecks.
Risk classification and security guardrails are integrated into the intake phase, categorizing initiatives into four risk tiers. Low-risk projects have fast-track approval, while high-risk applications undergo mandatory InfoSec and Legal controls. The adaptive delivery model, based on one- to three-day sprints, pairs human strategic direction with automated AI assistance, ensuring continuous adaptation without the delays typically associated with changing scope.
Post-deployment, value realization protocols mandate documenting baseline performance metrics and formally auditing them at 30-, 60-, and 90-day intervals to confirm measurable improvements in efficiency and outcomes.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
