Building an app with AI: how far can you go alone?

What AI does well today

Building an app with AI or a conversational assistant gives good results for testing an idea before investing further. A clickable mock-up, to show a journey to a co-founder or a potential customer. A demo prototype, to confirm that an idea deserves to be pursued. A small, non-critical internal tool, used by a single person, with no sensitive data or regulatory obligation. In these three cases, AI's speed and low cost are a genuine advantage, and it would be absurd to pay for full development just to test a hypothesis.

These tools also shorten the distance between an idea and the first concrete feedback. Showing a mock-up that reacts to a click often convinces better than a written description, and allows the idea to be adjusted before committing a larger development budget. This is a legitimate and useful use, as long as it stays confined to this stage of the project.

The limits for an application built to last

Moving from a prototype to an application used by real customers or real teams changes the nature of the problem. Security is the first example: quickly generated code often contains flaws in sensitive places, authentication, access rights, validation of data submitted by the user, because the tool optimises for a result that works on screen, not one that withstands an attack. Personal data is a second example: as soon as an application processes data identifying people, obligations apply to what is collected, retained and protected. An AI tool does not know your sector or your specific obligations.

Rights over the code created raise a separate question: depending on the tool used, the terms of service may limit what you are allowed to do with the result, in particular reselling it or distributing it widely. This should be checked in the terms of service of the chosen tool, before building anything on top of it.

Offline use: a special case AI handles poorly on its own

A field app that must work without a guaranteed network connection needs a local database, a synchronisation queue, and an explicit strategy for conflicts between two competing entries. This is a design question, not just a coding one, and general-purpose AI tools rarely get it right on the first attempt. We go into these design choices in our guide on offline field apps.

This is not a limitation specific to AI: it is a genuinely hard subject, which requires explicit decisions, made consciously, rather than default answers. A tool that generates code quickly inevitably picks a default behaviour for these questions, with no way of knowing whether it is the right one for your particular use case.

When an AI-built app is enough, and when it no longer is

Taking over a prototype generated by AI

A prototype built with AI is not wasted time when it later needs to evolve: it is often a good starting point, because it has already validated the idea and shown what the expected journey looks like. The takeover then consists of auditing what can be kept and what must be rewritten, in particular security and data structure, before adding real users. This is the same work as taking over software written by a human developer, with a slightly different risk profile: these codebases often work well on the surface, with duplication and security flaws in sensitive places.

This is precisely what our software takeover and evolution service covers, applied to this particular case: an audit first, then a decision on what is kept and what is rewritten, never a complete rewrite decided by default.

How to decide, without passing judgement

Building an app with AI is neither a miracle solution nor a dead end: it is a tool suited to some uses, and not to others. The question to ask is simple: who will use this application, with what data, and for how long? If the answer is still “just me, to test, no sensitive data”, carry on alone. If the answer includes customers, employees, personal data or use spanning several years, have what you have got audited before going any further.

This decision does not need to be made alone either. A quick audit, before opening your prototype up to real users, costs considerably less than repairing things after a security incident or data loss. It is often the best moment to have what the AI produced reviewed by someone who knows exactly where to look for problems.

This guide describes a practice, not legal advice. The obligations that apply to your data depend on your sector and your activity: check them with your legal adviser or the relevant authority.