From AI-Generated Prototypes to Production: 6 Critical Considerations for Business Leaders

The application authenticates users, processes data, and works flawlessly in your hands. Your team tested it. Your early users love it. It works.

Then comes the deployment question: “Will this still work without hiccups if multiple users are using it concurrently?”

The honest answer? You’ve completed roughly 30% of what quality software requires. The remaining 70%—security hardening, error handling, scalability, monitoring, compliance—remains invisible until something breaks.

This isn’t a limitation of AI tools. They’re doing exactly what you asked: make it work quickly. But production systems operate under different constraints: adversaries, scale, failures, regulations, and Murphy’s Law. This article outlines six gaps that separate impressive demos from production-ready systems.

What AI-generated Prototypes Get You (And What It Doesn't)

AI-generated Prototyping—building software through conversational prompts with AI—excels at rapid prototyping. You describe features in natural language, the AI generates working code, and within days you have something functional.

What AI-generated prototyping optimizes for:

• Making features work in the happy path

• Fast iteration on core functionality

• Validating ideas quickly

What AI-generated prototyping systematically skips:

• Security hardening against real-world threats

• Handling failures and edge cases

• Scalability beyond 5-10 users

• Production monitoring and debugging tools

• Regulatory compliance

• Long-term maintainability

This isn't a limitation of AI tools—they're doing exactly what you asked: make it work, make it fast. The problem is assuming "works in demo" equals "ready for production."

The data reveals the magnitude of this gap: AI-generated code produces 1.7x more issues than human-written code, with critical defects up to 1.7x higher and security vulnerabilities rising 1.5-2x. By June 2025, AI-generated code was introducing over 10,000 new security findings per month—a 10x spike in just six months.

1. Security Gaps: When Working Code Isn't Safe Code

AI-generated code that functions correctly isn't necessarily secure. Research from BaxBench found that 62% of solutions produced by even the best-performing AI models are either incorrect or contain security vulnerabilities—and of the code that does work correctly, around half still has security flaws. A prototype can pass every functional test while silently carrying vulnerabilities that only a security-focused review would catch.

Why this matters: "It works" has become the default measure of success in rapid prototyping, but working code and secure code are different standards. If you're evaluating software or a development team, ask: What happens if someone tries to break in? What review process catches vulnerabilities that don't break features?

2. Error Handling: When the Happy Path Ends

Prototypes handle the 95% of cases that work fine. But what happens when a payment processor times out mid-transaction? When an email service goes down? When a user enters unexpected data? That code often doesn't exist—or does the wrong thing.

Why this matters: Half-completed transactions, users charged twice, silent data corruption. If you're evaluating a tool, ask: What happens when things go wrong? How does the system recover?

3. Code Quality: The Hidden Cost of Speed

A survey of 72 studies on AI-generated code identified "Code Style and Standards Issues" as one of eight major categories of bugs—not because it causes crashes, but because it undermines long-term maintainability. The research notes that AI-generated code often lacks readability and consistency, employing non-standard naming conventions and failing to conform to team coding styles. These aren't errors that break functionality—they're patterns that make code harder to understand, modify, and extend.

Why this matters: Code that works today becomes expensive to maintain tomorrow. When multiple developers—or multiple AI sessions—contribute inconsistent solutions to the same codebase, understanding existing code takes longer than writing new code. If you're hiring developers or evaluating a vendor, ask: How is the codebase documented and reviewed? What standards ensure consistency as the project grows?

4. Scalability: The Cliff After Launch

A system that handles 50 users flawlessly can collapse at 500. Missing database indexing. Single servers that can't handle load. Synchronous code that chokes under concurrency. These structural limitations are invisible until traffic spikes.

Why this matters: Launch momentum evaporates when systems grind to a halt. Weeks spent optimising instead of adding features. If you're evaluating software, ask: What's the largest user base this has supported? What happens when demand doubles?

5. Observability: Flying Blind in Production

During development, problems are visible immediately. In production, a user reports "checkout didn't work"—and you have no way to know which checkout, when, or why. No filtering. No tracing. Complete blindness to what's actually happening.

Why this matters: Debugging production issues takes days instead of minutes. Critical failures go unnoticed. You can't identify affected users or lost data. When evaluating software, ask: How do you monitor for problems? How quickly can issues be identified and resolved?

6. Compliance: From "It Works" to "It's Legal"

A working application that collects customer data isn't the same as a compliant one. Where exactly is data stored? Can you prove what was deleted and when? Is data encrypted at rest, or just in transit? Do you have consent logs and data processing agreements?

Why this matters: Singapore's PDPA and EU's GDPR violations can mean hundreds of thousands in fines. Enterprise and government contracts require certifications you can't obtain. Security audits reveal gaps you can't explain. When adopting any software that handles customer data, ask: Where is data stored? How is compliance demonstrated?

Enterprise Reality Check: Scaling These Concerns Across Teams

The challenges above multiply in larger organisations. At a mid-size company (100-500 employees), the gaps become critical:

• Security: Multiple teams access the system. Who can modify what? No role-based access controls (RBAC) means everyone has equal privileges. An intern accidentally deletes the wrong database table. A departing employee retains access for weeks.

• Observability: Different teams own different services. When something breaks, nobody knows where—was it a database issue? API timeout? Frontend bug? No distributed tracing means finger-pointing and slow resolution.

• Compliance & Audit: Legal needs proof that only authorized people accessed customer data. You have no audit logs. Compliance officers can’t sign off on regulations. You can’t pass security reviews for enterprise customers.

• Dependency Management: Your small team knew every AI-generated package. At scale with 50 developers, nobody tracks dependencies anymore. Security vulnerabilities go unpatched for months.

• Scalability: Features your small team built individually now need to handle 10,000+ concurrent users across different time zones. Your single database instance can’t handle it. You need database clustering, load balancing, caching layers, and disaster recovery procedures you never built.

The lesson: What works for a 5-person team and 100 users doesn’t scale to 100-person teams and 100,000 users without intentional architectural decisions made early.

Why This Gap Exists (It's Not Your Fault)

AI coding assistants are optimised for feature development. When you say "build user authentication," they generate login functionality. They don't generate:

• Comprehensive error handling for every failure mode

• Security hardening against injection attacks

• Monitoring instrumentation

• Compliance documentation

• Test suites

• Deployment automation

Because you didn't ask for those. And if you lack production engineering experience, you don't know what to ask for.

This isn't a failing of AI tools—it's a mismatch between "make it work" (what vibecoding does brilliantly) and "make it production-ready" (which requires expertise AI can't infer from casual prompts).

The numbers are stark: 97% of developers have used AI coding tools, yet AI coding assistants don't inherently understand your application's risk model, internal standards, or threat landscape.

The Bottom Line

AI tools have democratized the starting of software projects. But the gap between prototype and production-ready software is where professional engineering expertise becomes essential—not optional.

For teams building software: Faster prototyping doesn't eliminate the need for security reviews, dependency audits, scalability planning, and compliance frameworks. It just gets you to the point where that work begins more quickly.

For teams evaluating software or developers: "It works" and "it's fast" aren't sufficient criteria. Ask about security testing, dependency management, error handling, scalability limits, monitoring capabilities, and compliance documentation.

For everyone adopting new tools: Understand that low-cost or amateur-built software may carry hidden risks that only emerge under real-world conditions—when stakes are highest and recovery is hardest.

The speed of modern development is a gift. But treating a prototype as a finished product is a trap that catches businesses every day.

Red Airship's Approach

We specialise in taking AI-generated prototypes and engineering them for production—with specific expertise in AI-generated code vulnerabilities.

Our Process:

1. AI Code Security Audit: Assess your prototype specifically for AI-generated code vulnerabilities (XSS, SQL injection, cryptographic failures, hallucinated dependencies, missing error handling) and compliance gaps (fixed fee)

2. Production Re-Engineering: Rebuild with security, monitoring, error handling, and scalability built in—preserving functionality while adding production infrastructure and replacing vulnerable AI-generated patterns

3. Compliance & Documentation: Ensure regulatory compliance, implement audit logging, create necessary documentation

4. Ongoing Operations: Continuous monitoring for AI code quality degradation, security patching, performance optimisation

Our Focus: Banking, government, and healthcare organisations in Singapore—sectors where the gaps between AI-generated prototypes and production create unacceptable risk.