A framework for measuring and scaling AI-DLC — from ad-hoc tool usage to a governed, observable lifecycle with ROI visibility.

AI-DLC Maturity Levels

Most teams use AI but can't measure the impact. The workshop takes you from L1 to L3. Advanced engagements target L4–L5.

L1
Experimental
Ad-hoc AI use. No shared tooling, no metrics, no governance.
L2
Structured
Spec-driven development. Metrics flowing. Basic eval gates in place.
L3
Integrated
Full pipeline operational. Executive visibility. Iterating on thresholds.
L4
Orchestrated
Multi-team platform. AI FinOps. Governed agent scope across the org.
L5
Autonomous
Agents contributing to architecture. 20%+ autonomous deployments.

7 Steps to Get There

Each step builds on the last. By the end, every AI-assisted action is tracked, validated, and visible.

Explore what you get ↓

The PRISM Bootstrapper

Everything your team needs to sustain AI-DLC practices after the workshop. Copy into any repo and go.

Day 1Install hooks + CLAUDE.md. AI-origin tagging starts immediately.
Week 1Add GitHub workflows. AI-to-merge ratio and lead time tracked.
Week 2Configure eval harness. Automated quality checks on every PR.
OngoingWeekly DORA assessments. Full dashboards. Continuous improvement.

Connects to Your Existing SDLC

PRISM layers on top of tools your team already uses — no rip-and-replace required.

Project & Agile Tools

Jira, Linear, VersionOne, Azure Boards, Shortcut, and other sprint/kanban trackers. PRISM correlates AI metrics with story throughput and sprint velocity.

Source Control & CI/CD

GitHub, GitLab, Bitbucket, CodePipeline, Jenkins, CircleCI. Git trailers and webhook collectors work with any Git-based workflow.

Observability & Reporting

CloudWatch, QuickSight, Grafana, Datadog, Jellyfish, Swarmia. Export metrics via EventBridge to any downstream analytics platform.

AI Platforms

Amazon Bedrock, Claude Code, Kiro, Q Developer. Track which models, tools, and sessions contribute to each commit and PR.

Traditional DORA + AI-Native Dimensions

10 metrics tracked end-to-end, from commit to executive dashboard.

DORA Metrics Elite Deployment Frequency 4.2/day Lead Time for Changes 2.1 hrs Change Failure Rate 3.2% Mean Time to Recovery 28 min Weekly Trend (8 wk) Deploy Freq Lead Time AI-DORA Extensions AI AI Acceptance 85% AI-to-Merge 70% Eval Gate Pass 91% Spec-to-Code Turnaround 1.8 hrs Post-Merge Defect Rate 1.4% AI Test Coverage Delta +12.3% AI Contribution Trend (8 wk) Human AI-Assisted

AWS-Native Metrics Pipeline

No third-party observability dependencies. Everything stays in your AWS account.

DEVELOPER INGESTION STORAGE VISIBILITY CC Claude Code K Kiro Specs Git Hooks GH GA GitHub Actions BE Bedrock Evals API API Gateway REST endpoint for metric ingestion EB EventBridge Event bus for metric routing λ Lambda Enrich, normalize, transform CT CloudTrail Audit trail for all AI actions DB DynamoDB Raw event storage CW CloudWatch Time-series metrics S3 S3 Spec & eval artifacts CW Team Dashboard Real-time velocity per repo Live QS Exec Readout Weekly trends & ROI for CTO Weekly AL Alarms AI acceptance < 70% triggers Alert Git Commit AI Trailer EventBridge DynamoDB + CloudWatch Dashboard & Alerts