AI coding tools and building platforms are turbo-boosting software engineering. It is easier and cheaper than ever to turn an idea into a working prototype, even without technical experience. But that does not make software engineers obsolete.

Here’s why...

Creating an app isn't just code

Code is a set of instructions. But turning those instructions into something real users can depend on is something else entirely. 

Software does not just need to function: it needs to be deployed, secure, observable, recoverable and maintainable. The gap between "it runs on my machine" and "a thousand people use it every day" is where most engineering effort actually lives.

AI app builders have made a real attempt to close that gap. A shiny publish button handles the basics, and for a simple project with light dependencies, that is often enough. But it's also where the abstraction starts hiding things that matter.

For example, what happens when transactional emails land in spam because the sending domain hasn’t been authenticated? What about when traffic spikes and the underlying service quietly throttles with no alerting? And if a user in the EU requests data deletion and there is no process, no audit trail and no clear answer about where their data even lives?

These are the kinds of problems experienced engineers think about constantly: backups and recovery, monitoring and error handling, environment separation, dependency management and operational resilience. None of them feel exciting but all of them determine whether your clever software is something you can run a business on.

 

Functionality with AI is easy, non-functional is not

This is the part that catches people out, because it inverts the way we tend to think about effort. The hard bit feels like getting the thing to work. Once it works, surely the rest is just admin?

In reality, functionality is often the cheapest part of building software (even before AI). The expensive parts are non-functional: security, scalability, observability, compliance and maintainability.

That is why mature software teams develop practices like testing environments, automated tests, staged rollouts, infrastructure as code. Someone, somewhere, learned the painful version of why they were necessary.

There is a real and reasonable trade-off between speed and operational maturity, and it is not always wrong to lean towards speed. A weekend prototype does not need a disaster recovery plan. But the further your software moves from prototype to production, the less optional those practices become.

Where this gets interesting is that an engineer who has already internalised these habits is in a uniquely strong position to direct AI tooling well. They know what to ask for, what to check for and what is missing. They can spot the absence of rate limiting, input validation, monitoring or scaling plans, even in a working system.

Less experienced builders may produce similar code with AI, but without the context to see what is missing. And what is missing is usually the part that bites you in month six, not week one.

 

AI can help with the non-functionals, but it is not free yet

It’s worth being clear: AI is genuinely capable of helping with non-functional work too. It can draft privacy policies, propose backup strategies, write monitoring rules, design database structures and help with threat modelling. In the right hands, it is an extraordinary multiplier across the entire delivery lifecycle, not just the parts that produce visible code.

But none of this is automatic. Most AI app builders are optimised for one thing: shipping working software quickly. The question of whether what you’ve shipped is fit to live in the world is simply outside its frame of reference.

Getting useful output from AI in these areas requires intent. Someone has to know to ask and someone also has to know what a good answer looks like. Because AI will produce a confidently-wrong answer with equal fluency. 

That is why experienced engineers remain so valuable. Not because AI cannot help, but because someone must supply the context, judgement and standards to define what good actually looks like within it.

 

Good engineers will make great AI enabled developers

The teams that will get the most out of AI-assisted development are not those treating it as a replacement for traditional engineering, but those who already had good engineering principles and are now expressing them through a faster medium.

Good engineers have always thought beyond code and into security, infrastructure, scalability, observability, testing and maintainability. Those instincts matter even more when an AI is doing the typing, because they separate "code that works in a demo" from "code that works at three in the morning when no one is watching."

Software in the real world is unforgiving in specific, predictable ways. Systems get attacked, scaled, handed between teams and forced to adapt to new requirements over time.

Every checklist and every review gate is a scar from a previous lesson. AI does not remove those lessons; it simply changes how quickly experienced teams can apply them.

 

Does AI-assisted software engineering save time then?

This is the question everyone really wants answered. The honest answer is: yes, but not in the way headlines suggest, and not unconditionally.

Without process - no reviews, no tests, no environments, no consideration of what happens after launch - AI is dramatically faster. You can build something today instead of next week. That is genuinely useful for prototypes and early experimentation.

But that comparison is misleading. It pits AI without process against traditional development with full process. The real question is whether AI-assisted development with proper engineering discipline is faster than traditional development with the same discipline. And there, the answer is more interesting.

In a traditional workflow, engineers design an approach, implement it, write tests, adjust infrastructure, deploy to a test environment and hand it to a colleague for review. Each step adds time, but exists for a reason.

In an AI-assisted workflow, those steps do not disappear. Instead the AI produces drafts, while the human stays in the loop to review, refine and decide.

The time saving is real, but has shifted. The engineer is doing less typing and more judging and that is genuinely faster for most kinds of work - but only if the engineer is equipped to make those judgements quickly. Take the human out of that loop and you do not save time at all, you simply move the cost from build to ‘fix in production’, and the latter is always more expensive with higher consequence.

 

So where does that leave us?

Using AI to build software is a viable and often excellent choice. The speed and leverage it provides is genuinely transformative, especially in the early stages of an idea.

But AI alone only goes so far without engineering expertise sitting alongside it - keeping systems reliable, evolving them safely and avoiding issues that turn a fast launch today into a serious problem later.

The shift is not that engineers are no longer needed, but that they spend less time typing and more time directing, reviewing and safeguarding. The teams that thrive in this next chapter will be those who already understood software craftsmanship before AI, and who now use AI to express it at far greater speed.

 

The "Beyond Working" checklist

If there is one practical takeaway, it is this: before any AI-built tool moves from ‘interesting prototype’ to something real people depend on, there are questions worth asking out loud.

Use them as a conversation starter with whoever is building, whether that is an internal team, an agency or an AI tool. They are not a substitute for engineering expertise, but they help surface the non-functional questions that are often missed.

 

01 / On keeping it running

  • If this goes down at 9AM on a Monday, who finds out, and how?

  • If we lost the database tomorrow, what do we lose and how do we recover?

  • What happens when traffic is ten times higher than we planned for?



02 / On keeping it safe

  • Who can see what data, and how do we know they should be allowed to?

  • If someone with a browser and a bit of curiosity poked at this, what is the worst they could do?

  • Are we storing anything we shouldn’t be, or storing it for longer than we need?



03 / On keeping it legal

  • Which data protection regimes apply to our users, and have we actually checked we’re compliant?

  • If a user asks us to delete their data, can we do it cleanly and prove that we did?

  • Do we have a privacy policy, terms of use and cookie handling that match what the application actually does?



04 / On keeping it evolving

  • Can a new person make a change to this safely, without breaking what already works?

  • Can we test changes before users see them?

  • If the AI tool we built on disappeared tomorrow, could we still maintain the application?



05 / On keeping it honest

  • How do we know the application is doing what we think it is doing?

  • What is our plan when something unexpected goes wrong?

  • Who is accountable for the answers to all of the above?


And if the answers are vague? That is your signal that the parts which determine whether the work survives in the real world have not yet been fully considered. Which is your moment to bring engineering judgement into the room, before it is needed in anger.

 

About Deazy

Deazy enables ambitious organisations to explore and harness AI to drive digital product innovation and operational efficiency, applying our award-winning AI and software delivery expertise to solve complex challenges, accelerate innovation and build resilient digital platforms that scale.

With a uniquely flexible delivery model, we provide rapid access to a diverse pool of 6,000+ experienced nearshore AI, software, and data professionals, managed by highly-experienced and multidisciplinary in-house product and delivery experts who provide the support and resources to guarantee success.

If you’d like to explore how Deazy can support your team’s AI adoption journey or optimise your product delivery capability, please get in touch with us at hello@deazy.com or look us up on LinkedIn.