Most enterprises are already betting big on AI… but very few have turned it into a reliable, industrial‑grade software factory. On the backend, most engineering leaders know they need AI SDLC, but few know how to measure whether they’re actually doing it well.
Download the white paper to run a self-service AI SDLC maturity assessment and see how your current practices stack up across eight critical dimensions of AI‑driven software delivery.
Download the white paper to run a self-service AI SDLC maturity assessment and see how your current practices stack up across eight critical dimensions of AI‑driven software delivery.
This white paper lays out a practical, research-backed view of where AI delivers meaningful productivity and quality gains today, where it quietly introduces risk and technical debt, and what has to change in your tooling, workflows, and org design to move from chaotic experimentation to governed, scalable delivery. You’ll see how AI reshapes each stage of the lifecycle, from planning and requirements through implementation, debugging, QA, and security, with concrete patterns drawn from real teams using tools like Cursor, Claude Code, and Gemini in production environments.
The paper also unpacks key architectural choices such as chat-based versus IDE‑centric coding agents, explains why token economics now matter as much as headcount, and shows how practices like semantic QA and AI‑driven testing are becoming guardrails around machine creativity rather than optional extras.
At the center of the paper is a self-service AI SDLC maturity assessment: an eight‑dimension model you can score yourself against to understand how effectively your organization is using AI across development environment, automation, agent autonomy, governance, quality, and value measurement. It gives technology leaders a shared language to discuss current capabilities, surface gaps, and build a roadmap for making AI‑driven development safe, scalable, and sustainable inside the enterprise.
Download the white paper to access the full AI SDLC maturity assessment and get a clear, structured view of where your organization stands, and what to do next to make AI a first-class part of your software development lifecycle.
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