Software development has fundamentally changed since the use of AI in coding. Applications that once took weeks to code can now be prototyped in hours. Developers have access to AI-based co-programmers, autonomous coding agents, and no-code platforms that dramatically accelerate software development, but they can also create a false sense of confidence.
AI-assisted development is certainly exciting, but it’s also creating a new challenge: how to ensure AI-written code is safe, can be maintained, and is going to be usable in live scenarios.
This was the central theme of our recent webinar on AI code auditing with our CTO, Toni Kangasniemi, where we shared our lessons from reviewing AI-assisted software projects and discussed how people can adopt AI without sacrificing long-term quality.
Fast development means risky business
AI has removed many of the barriers that once slowed software development. Developers can explore new technology stacks in days instead of months and prototypes can become production systems surprisingly quickly. Even non-developers are increasingly contributing directly to application development using AI tools, which can be both a threat and an oppirtunity.
But speed introduces a different category of risk.
We’ve started seeing projects where:
- architecture emerges accidentally rather than intentionally
- business logic becomes scattered across the application
- generated code is accepted without anyone fully understanding it
- documentation never catches up with implementation.
The challenge isn’t generating software, it’s keeping control of it. Understanding what is where and why, and keeping a handle on version control – all this can break down if you move too fast.
AI doesn’t remove engineering
One other misconception is that AI reduces the need for experienced developers. Our experience suggests the opposite.
The skills that matter most today include:
- architectural thinking
- understanding system boundaries
- security and data governance
- critical review of generated code
- maintaining shared development standards
Prompting is useful, but engineering judgment is indispensable.
One observation from our audits captures this well:
The important question isn’t who knows how to prompt an AI model; it’s who can safely continue developing the system afterwards.
The biggest mistakes happen before the first line of code
Most AI-related problems don’t originate from the AI model itself.
They originate from the lack of agreed processes.
Successful AI-assisted development begins by defining:
- which AI tools are approved
- what data may be shared with AI services
- how generated code is reviewed
- where architectural decisions belong
- documentation standards
- testing expectations
AI behaves much like another developer on the team. Without shared rules, it simply optimises for the task it has been given, not necessarily for the long-term health of the product.
Working software isn’t the same as maintainable software
One pattern appears repeatedly during audits: individual features work well, yet the system as a whole slowly becomes harder to understand.
Business logic ends up split between frontend code, serverless functions, third-party platforms and AI-generated components. Dependencies multiply and ownership becomes unclear.
Eventually teams start hearing familiar phrases:
“It works… but nobody wants to touch it.”
At that point, technical debt has already become organisational debt.
What should an AI code audit actually examine?
Traditional code reviews remain essential, but AI-assisted development introduces additional questions.
An effective audit should ask:
- Can developers explain why architectural decisions were made?
- Is business logic located in the right place?
- Can another developer continue the project without the original author?
- Are authentication and authorisation implemented correctly?
- Are AI-generated components distinguishable from manually written code?
- Are platform dependencies creating unnecessary vendor lock-in?
- Has AI usage been documented where appropriate?
- Are regulatory, licensing and data privacy requirements understood?
These questions determine whether today’s prototype becomes tomorrow’s production platform… or tomorrow’s rewrite.
AI should accelerate development, not technical debt
AI is an extraordinary productivity tool. But productivity isn’t measured by how quickly the first version ships. It’s measured by how confidently the fifth version can still be changed.
The organisations gaining the greatest advantage from AI aren’t necessarily using the newest coding models: they’re building processes that make AI-generated software understandable, testable and maintainable.
That’s ultimately what software engineering has always been about. AI simply makes it more important.
