The Builder’s 3-Step Checklist for Deploying AI-Generated Apps

Excellent! You developed an application using an AI code generator, vibe coding tool, or vibe no-coding platform. Now, you want to share it with your team, department, or entire business. How do you make the working application — or what seems to be working — available to everyone?

Let's back up a bit, review the journey that got you here, and pave the way for what's next: developing and supporting your application. You've been on a mission — now it's time to bring your colleagues on board and gain support from IT, security, and other risk management leaders to keep your app resilient.

A citizen developer's guide for deploying AI apps

I started my career as a software developer, then became a CTO and CIO over nearly 20 years. I've developed many applications, both pro-code and low-code, and I can share my main secret to deploying successfully.

Prototyping is easy. The challenge is deploying applications employees want to use, that deliver business value, and that run on platforms that simplify releasing enhancements. If you want to accelerate AI's path to business value in your organization, you need a development process that enables rapid feedback.

Here's my checklist for shipping AI-generated apps.

1. Define the app's vision

Let's look at an example. You built one app that tracks time in field operations by letting users record their work by audio, and the app uses AI to break it down into a task schedule and cost codes. You built another app that scans statements of work (SOWs) to capture key deliverables and dates, then feeds them into a tracking app that uses AI to help assign and schedule the work.

These are application descriptions, but they don't tell the whole story of why you developed each one. More specifically, who will use it, how does the app help employees, and what are the intended business benefits and outcomes?

Before deploying the application or sharing it with colleagues, document your vision. This communication doesn't have to be a complex requirements document. In fact, shorter and simpler is better. You can use my one-page vision statement template or use it to craft your own version.

2. Demo your prototype and ask for feedback from users and stakeholders

With a vision statement and prototype ready, you can now showcase your app and ask for feedback. The objective is to get buy-in on the vision, learn how to improve the app's user experience, and determine what workflow improvements are required.

Workflow enhancements can take several forms. Sometimes, feedback will point to an integration; for example, ensure cost codes are integrated from the ERP and updated daily. Other times, you may receive feedback on a use case you hadn't considered, such as capturing audio in Spanish and other languages.

When building an AI agent, the most important feedback comes from end-user and stakeholder expectations about the AI's capabilities and accuracy. For example, what is the target accuracy for capturing deliverables from the SOW, and should a human review occur before assigning dates and ownership?

Stakeholder feedback should help you:

• Define what users and stakeholders will count as a win, such as time saved, fewer errors, or faster decisions, so you can prove value after launch and prioritize the next round of improvements.
• Identify additional metrics and data fields used to analyze performance, customize reporting and support integrations. Add knowledge for the AI's context.
• Capture user experience improvements, especially around mobile user interfaces, navigation, naming conventions, and data validation.
• Detail where workflows need exception handling, added human-in-the-middle validation steps, or AI guardrails to improve recommendations.
• Determine role-based access controls that optimize the user interface and workflows for different user types, while ensuring data access rules meet business requirements.

You will need to prioritize the feedback you receive. Determine the minimum requirements for launch, then create a roadmap for future enhancements.

3. Consult with IT, security, and compliance on architecture, testing, and deployment

If your organization has an IT, information security, or risk management function, then they will likely have standards and best practices for deploying applications and AI capabilities. If this is the first AI app you're building, here are my suggestions on how to respond if they characterize your work as shadow IT or AI.

Below are three areas these technical professionals will look for in your solution. If you work in a small- or medium-sized business (SMB), use this guide for app architecture, testing, and deployment considerations.

• Architecture: You'll want to reconsider whether you're using the right development tools for the job now that you have more complete requirements around the user experience, workflow, data, and AI. If you're using a code-generating tool like OpenAI Codex or Claude Code, you'll have more steps to validate the code, configure infrastructure, and automate deployments. Vibe coding tools will also require you to get into the technical weeds. You can skip much of this work by using an AI development platform with built-in hosting, deployment, and compliance certifications. If your organization requires SOC 1 - Type II, SOC 2 - Type II, SOC 3, HIPAA, DFARS, CSA Level 2, FERPA, WCAG, or other compliance, then using do-it-yourself code generator tools and hosting adds many compliance requirements to implement.
• Testing and documenting: After updating your app and workflow with stakeholder feedback, consider how you'll test and document the application, workflow, and AI capabilities. Look for sandbox environments and Application Lifecycle Management (ALM) capabilities, which help you manage separate development, testing, and production environments. You'll also want the platform to support UI previews, automated feature verification, and a way to switch between user role types when testing. Lastly, solutions should help you visualize data structures, troubleshoot workflows, and use AI to simplify implementing changes.
• Publishing and sharing: Publishing requires automated deployment capabilities, plus the option to restore a previous version. Sharing requires steps to configure users and permissions and to consider where email notifications are needed.

The most important step of the checklist

The most challenging part of deploying applications is gaining end-user adoption, stakeholder excitement about the results, and support from technology experts. These are all key steps in driving a successful change management program.

By following this checklist, you've already engaged the primary people needed to go from idea to prototype, application, adoption, and business results. Engaging stakeholders throughout development, testing, and publishing ensures they contribute, provide feedback, and are likely to be enthusiastic supporters.