Vibe Coding: Moving Beyond the Hype to Practical Enterprise Use

By Virginia Fletcher, CIO

A New Paradigm:  Coding by Intent Rather than Syntax

In the age of AI, how we think about building software is changing in profound ways. A new term, vibe coding, has surfaced to describe the practice of describing intent in natural language and letting an AI generate the implementation. Instead of painstakingly writing instructions line by line, a developer or even a non-developer simply communicates the outcome: “Create a mobile app that lets users track their daily wellness habits.” Within moments, a working prototype appears.

This is more than autocomplete on steroids. It represents a new paradigm, coding by intent rather than syntax, and it has already begun to change the way teams innovate and deliver.

The Dual Reality: Promise and Pitfalls

The allure of vibe coding is undeniable. Prototypes that once required weeks of work can now be produced in hours. The barrier to entry is significantly lower, allowing product managers, designers, and even non-technical founders to test ideas directly. By shifting the burden of boilerplate code to machines, engineering talent is freed to focus on higher-order concerns such as product design, architecture, and long-term scalability.

Yet the risks are equally real. AI-generated code often looks more robust than it is. Behind a polished interface may sit fragile foundations, with gaps in security, scalability, or maintainability. Debugging can become more difficult when developers inherit code they didn’t fully author or understand. And perhaps most dangerously, teams can fall into what I call AI-washing, promoting software as “AI-powered” simply because it has been vibe coded or dressed up with superficial AI features.

We’ve seen versions of this before: greenwashing in sustainability, cloud-washing in IT. AI-washing creates false expectations, erodes trust, and ultimately harms credibility with customers, boards, and investors. Leaders must be able to distinguish between experiments that are vibe coded for speed, and solutions that are engineered for resilience.

Platforms and Their Sweet Spots

Different platforms support vibe coding in different ways. For those building software that needs to be secure, governed, and maintained, tools like Cursor, GitHub Copilot Enterprise, Claude Code, or Cloudflare’s VibeSDK provide stronger guardrails and deeper integration into professional workflows. They support entire repositories, enforce policies, and provide the observability necessary in production environments.

For rapid prototyping, other platforms shine. Replit Agent and Lovable are particularly good at helping non-technical users go from idea to demo with little friction. Bolt.new is excellent for generating quick, browser-based applications that can be tested or pitched to investors. And visual builders like Webflow and Bubble are beginning to integrate AI in ways that make design-driven prototyping even more accessible.

The lesson is clear: the right platform depends on your intent. If you need something you can show users tomorrow, start with a lightweight tool. If you are preparing to scale into production, choose an environment that supports proper governance and engineering rigor.

From Vibe to Value: A Lifecycle for Leaders

For technology leaders, vibe coding should not be viewed as an end state but rather a starting point. A practical lifecycle begins with exploration, where product managers or innovation teams use fast-moving platforms to spin up prototypes. At this stage, the goal is to surface ideas quickly, share them with stakeholders, and test whether they resonate. It is essential to disclose clearly that these are experimental prototypes, not enterprise software.

The next stage is validation, where prototypes are exposed to customers or internal partners to measure desirability and usability. Only the most promising concepts move forward. From there, engineering teams take the baton, hardening the code in environments that support real development practices. Fragile sections of AI-generated code are rewritten or replaced, security reviews are conducted, and the product is re-architected for scale.

Finally, the system moves into deployment and governance. Here, it is treated no differently than any other production asset: it runs through DevSecOps pipelines, is monitored for reliability, and is reviewed for compliance. The continuous learning loop then feeds back into prototyping, improving how teams prompt and setting new boundaries for responsible AI use.

Embracing Vibe Coding as a Leader

The practical question for CIOs, CTOs, and product executives is not whether vibe coding will influence your organization: it already is. The real question is how to harness it responsibly. That means creating safe spaces for experimentation, where teams can try vibe coding without fear of failure. It means establishing governance frameworks to ensure AI-generated code is reviewed, validated, and hardened before reaching production. And it means educating stakeholders so that business leaders and boards understand the difference between an AI-powered prototype and a resilient, enterprise-grade system.

By building a dual-track culture, one that encourages rapid exploration while maintaining engineering discipline, you give your organization the ability to innovate at the speed of AI without sacrificing trust.

Closing Thought

Vibe coding is neither a gimmick nor a silver bullet. It is a new tool in the enterprise toolkit, powerful for speed, risky for scale. The leaders who thrive in this era will be those who can separate vibes from value: using vibe coding to accelerate innovation, while still insisting on the rigor required for production.

The companies that learn to prototype with vibes but ship with resilience will be the ones that define the next generation of digital products.