DTS Rapid Development Playbook & AI Enablement
Methodology achieving 50-70% dev time savings and 84% first-year ROI
The Business Challenge
As AI-assisted development tools matured, our department faced a strategic question: how do we adopt these capabilities at scale while maintaining the governance, security, and quality standards an enterprise organization requires? The risk of doing nothing was watching other teams and competitors move faster. The risk of moving recklessly was introducing security vulnerabilities, ungoverned code, and technical debt.
Separately, the department lacked a unified development methodology. Different projects followed different processes, making it difficult to forecast timelines, ensure consistent quality, or demonstrate ROI to leadership.
Discovery & Collaboration
Dave Gould and I co-created the "DTS Rapid Development Playbook" through extensive iteration — we're now on version 4. The process involved researching AI-assisted development workflows across the industry, mapping our existing development practices, identifying bottlenecks and governance gaps, and designing a methodology that incorporated AI at every appropriate stage while maintaining human oversight at critical quality gates.
The key philosophical insight driving the AI enablement strategy: focus on empowering employees to build community and share wins, and show what IS possible instead of focusing on what's not possible or what they shouldn't do. Leverage tools already available to the organization. Move faster, but incorporate data governance and security best practices along the way.
The Strategic Decision
Rather than issuing top-down AI usage policies, we designed a complete end-to-end methodology that makes AI adoption natural and governed. The playbook includes five development phases, six quality gates, two deployment tracks, an ROI framework, and clear RACI accountability — turning AI-assisted development from an ad-hoc experiment into a repeatable, measurable capability.
Five Development Phases:
- Planning & Track Assessment (requirements, stakeholder alignment, tech assessment)
- Prototype Build & Dev Deploy (AI generates specs, builds prototype, deploys to dev)
- Code & Architecture Validation (software engineers review, testing continues)
- SecOps, Governance & UAT (formal testing on deployed dev, security review, governance sign-off)
- Production Release (merge dev to prod, release notes, documentation, support handover)
Six Quality Gates:
- Specification Approval + Track Decision
- Dev Environment Ready
- Code Review Approval
- UAT Sign-off (on dev environment)
- Governance & SecOps Review (on dev environment)
- Production Readiness
Two Deployment Tracks:
- Enterprise Track: ECS/Fargate in AWS (complex applications)
- Rapid Track: Amplify with branch-based deployments (smaller tools)
The Build
The playbook is a comprehensive operational document, not a software application — but it produces measurable outcomes:
ROI Framework:
- Development efficiency: 50-70% time savings vs traditional approaches
- Time-to-demo: 2-3 weeks vs 2-3 months
- Legacy refactor example: 84% first-year ROI, 254% three-year ROI, ~7 month payback
- Application value: legacy risk elimination, maintenance reduction, SaaS cost avoidance
Product Management Lifecycle:
- Rapid Dev Team owns all product management post-launch
- Release planning, release notes, metrics/analytics, roadmap management
- Lifecycle and deprecation decisions based on usage data
Jira Integration:
- Ideas Board (Product Discovery) to Triage to NextGen Dev Board or Product Spaces
- Unified views via JQL for developer boards and leadership dashboards
RACI Matrix: Clear accountability across Rapid Dev Team, Software Engineers, Director of Technology, DevOps, Governance/SecOps, Stakeholders, DTS Support, VP Technology.
The Outcome
- Playbook documented (v4) — prepared by Dave Gould & David Royes, January 2026
- Enterprise Forms Platform serves as the proof point, achieving 30% completion in ~2 weeks
- ROI framework provides leadership with clear business case for AI-assisted development
- Methodology is repeatable and scalable across the department
- Seeking broader organizational buy-in based on demonstrated results
- Represents a shift from "AI as a tool" to "AI as an integrated development methodology"