Scaling Vibe-Coding in Enterprise IT: A CTO's Guide to Navigating Architectural Complexity, Product Management, and Governance
This is a preview for my paid subscribers. It will publish later this week on thectoadvisor.com for free.
Introduction
Enterprise IT organizations increasingly explore innovative approaches such as vibe-coding—a practice that leverages AI-driven tools and large language models (LLMs) to rapidly prototype and develop software with minimal traditional coding. Vibe-coding prioritizes functionality, user experience, and iteration speed over strict adherence to conventional software engineering methodologies.
The timing is no coincidence. The surge in accessible, high-performance LLMs—alongside AI code assistants like GitHub Copilot and integrated APIs from OpenAI and Google—has lowered the barrier to entry for building intelligent applications. What once required full-stack developers can now be initiated by analysts, architects, and subject matter experts.
While vibe-coding initially promises accelerated innovation and simplified development, scaling it across broader, less technical audiences introduces significant complexity, governance risks, and operational challenges. Using the evolution of the CTO Signal Scanner application as a detailed case study, this report helps CTOs understand the intricate balance required to scale vibe-coding effectively and sustainably.