DEV Track Spotlight: AI Native Development - Strategies and Impact across Amazon and AWS (DEV323)

Source: Dev.to

Quick show of hands. Who here has tried to scale AI adoption across a large organization and found it much harder than the blog posts make it sound?

This opening from Lilia Abaibourova perfectly captured the reality many organizations face. While individual developers might successfully use AI coding assistants, scaling that success across thousands of engineers requires something fundamentally different. In DEV323, Lilia (Principal Product Manager at Prime Video) and James Hood (Principal Software Engineer at AWS) shared their journey from isolated AI experiments to company‑wide AI Native transformation.

What makes this session particularly valuable is the honest discussion of both successes and failures, the grassroots movement that sparked adoption, and the organizational support needed to sustain it. This wasn’t a polished success story – it was a real transformation with real challenges.

Watch the full session: (link omitted)

From AI‑Assisted to AI Native: What’s the Difference?

Lilia drew a critical distinction that many organizations miss.

• AI‑assisted development – using tools like autocomplete and chatbots in isolated, ad‑hoc ways, pasting context between tools throughout the workflow. The impact is on individual productivity for isolated tasks.
• AI‑native development – fundamentally different:
  • AI tooling embedded throughout each step of the software development lifecycle
  • Every role enabled with AI – PMs, developers, designers, not just engineers
  • Agents owning multi‑step workflows end‑to‑end, not just autocomplete
  • Context embedded and shared throughout tooling and infrastructure
  • Organizational productivity transformation, not just individual gains

As Lilia emphasized: “We reimagine how we build software” rather than just accelerating existing processes.

The Accidental Movement: From Skeptic to Power User

James Hood’s journey began as a self‑described “AI skeptic” caught in a vicious cycle: flashy demos sparked curiosity, he experimented on real problems, the tools failed, and skepticism deepened. Sound familiar?

His breakthrough came when he realized that LLMs have emergent behaviors based on how you prompt them. By combining better tools with specific techniques, he became what he calls a gen AI power user.

The Two‑Day Feature Story

James shared his first major success: building a feature into Amazon Q Developer CLI in a codebase he’d never seen, written in a language he didn’t know, going from concept to pull request in two days and production launch in seven days.

His key insight: Software development is a process. You can’t give an AI (or a human) a vague idea and expect perfect implementation. You need to go through research, requirements clarification, design, implementation planning, and then implementation.

How James “speed‑ran” the process with AI

Research the Codebase (5 minutes)

• Asked Q CLI to analyze the codebase structure
• Focused on the specific component he needed to modify
• Had it document slash‑command implementation details

Clarify Requirements (24 questions, ~35 minutes)

• Used a specific prompt pattern to turn the agent into a partner
• Agent asked clarifying questions one at a time
• When James didn’t have answers, the agent provided options
• Iterative refinement until requirements were clear

Design Solution (~20 minutes)

• Generated a detailed design document based on planning docs
• Reviewed and refined the design
• Ready for implementation planning

Implementation Plan (14‑prompt sequence)

• Converted design into step‑by‑step prompts for code generation
• Each step was self‑contained and meaningful
• Generated a to‑do list to track progress

Implementation (remainder of 2 days)

• Executed prompts sequentially, testing along the way
• Used human judgment throughout – not “vibe coding”
• Leveraged two decades of programming experience to review and guide

“I was following along as it was implementing. I was going back and forth with the agent, questioning aspects of the code and using my human judgment and experience to make sure we were headed in the right direction the whole time.” – James Hood

Sparking the Grassroots Movement

This success led James to create an internal Slack channel in March 2025: amazon-buildergenai-power-users. It grew to tens of thousands of members – a massive grassroots movement of builders experimenting, sharing techniques, and learning from each other.

James described this as the AI native virtuous cycle: curiosity → experimentation → sharing (both successes and failures) → healthy skepticism → more curiosity.

Organizational Support: Three Pillars of Transformation

Lilia explained how Prime Video provided the organizational scaffolding needed to scale this grassroots energy. Transformation requires both bottom‑up momentum and top‑down support.

Pillar 1: Access and Infrastructure

Prime Video built an AI enablement stack that goes far beyond coding assistance.

Tools Across the SDLC

• Kiro – AI‑native development IDE with CLI interface; gaining traction with PMs and designers for prototyping, not just developers
• Amazon Quick Suite – Business intelligence and AI platform for researching requirements, learning from past experiments, analyzing data
• Custom agents – Test agents for hundreds of device types; operational agents for incident diagnosis and troubleshooting

Shared Context and Capability Layer

• Model Context Protocol (MCP) as the “context backbone”
• Knowledge bases connecting AI tools to business and technical content
• Context flows from PM specs → development (via Kiro) → operational agents – no more pasting context between tools