How Dynata scaled AI across the development lifecycle with AI-DLC and Kiro IDE

This article describes how Dynata scaled AI across the development lifecycle using AI-DLC methodology and Kiro IDE to compress multi-sprint feature delivery into a single sprint.

• Started with AI-assisted coding using Amazon Q Developer, then expanded to connect AI with Jira, Confluence, Lucidchart, GitHub, and Figma
• Adopted AI-Driven Development Lifecycle (AI-DLC) methodology with Kiro IDE for structured requirements, design, and implementation phases
• Conducted three-day workshops: Day 1 for requirements alignment, Day 2 for design and task planning, Day 3 for code generation
• Achieved approximately 70% reduction in development phase time for full-stack features
• Reduced time to agreed requirements from days/weeks to a single structured session day
• Surfaced requirement gaps and blockers during planning instead of implementation, reducing post-production bugs
• Used steering files to enforce coding standards and architectural patterns consistently across generated code
• Teams applied AI-DLC selectively for specific phases like requirements discussions, design-only sessions, or code generation

By aligning all teams on a shared plan before coding begins, Dynata transformed cross-functional collaboration and achieved significant delivery acceleration without adding engineers or cutting scope.