Delivery Toolchain & Data Transparency
How reliably you capture real-time SDLC flow from delivery tools instead of surveys and estimates.
AI-SDLC ASSESSMENT
AI-SDLC Maturity & Visibility Assessment
See where your organization stands across AI-assisted engineering visibility, delivery intelligence, attribution, governance, and outcome-based metrics.
12 questions 5 maturity stages DORA + SPACE aligned
Maturity Model
Traditional to AI-Autonomous
Traditional
Manual SDLC
Your organization likely relies on manual coding, manual testing, and localized tool data. AI usage may exist, but visibility is mostly anecdotal.
AI-Supported
Basic AI usage
Teams are using AI coding support, but leadership visibility is still thin. Adoption is easier to see than actual delivery or quality impact.
AI-Assisted
Measured workflows
You have a measurable foundation for AI-assisted development. The next opportunity is connecting AI-origin signals to delivery, quality, and governance.
AI-Native
Connected intelligence
Your organization is close to an AI-native operating model, with connected visibility across coding, review, testing, and shipping loops.
AI-Autonomous
Intent-led execution
You are approaching an adaptive SDLC where automation, governance, and improvement loops are tightly connected.
Core Dimensions
Measure the signals that decide whether AI actually improves delivery.
Attribution & Quality Signals
How clearly you connect AI-assisted code to review, defects, rework, security, and release outcomes.
Human-in-the-Loop Governance
How well review boundaries, agent permissions, exceptions, and quality gates are measured.
Balanced Engineering Metrics
How far your organization has moved from vanity metrics toward DORA, SPACE, and system health signals.
Take The Assessment
Score your current AI-SDLC operating model.
Choose the answer that best describes your current state. Your result appears after the survey and email submission.
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