Conventional software vs. custom software with AI

AI does not make software obsolete

A traditional system runs explicit instructions: if A happens, calculate B and save C. That precision is a virtue. An AI model interprets patterns and produces a probable output; it can handle language and situations that would be costly to enumerate, but it does not offer the same certainty by default. Putting AI in every step usually lowers reliability.

The right architecture separates responsibilities. The model understands intent, extracts fields or drafts; the software checks that the customer exists, that the slot is free and that the user has permission. The database keeps the state. That way the experience can be flexible without turning critical facts into statistical improvisation.

Where AI changes the interface

In conventional software, a person learns menus, filters and fields. With AI they can state the goal: “show me tomorrow’s bookings that have not paid” or “summarise this client’s changes”. The system turns that intent into permitted queries and shows a result. A conversational interface does not remove structure; it hides it when that helps.

It can also work with documents, emails or notes that do not fit columns well. But every answer should distinguish retrieved facts, interpretation and proposed action. If the user cannot see the source or correct the output, the interface will be convenient but hard to trust.

Signs of a fragile architecture

Be wary if a prompt holds all the business rules, if the model writes straight to production without validation or if there is no record of which tools it used. Also if the only test is a prepared conversation. A model change, a conflicting instruction or a badly labelled document can alter the behaviour.

Security needs limits outside the model: per-user permissions, allowed-action lists, parameter validation, confirmations and logs. Instructions help but do not replace controls. For sensitive data or high-impact decisions, apply human review and minimise the information sent to each provider.

• Critical rules outside the prompt
• Permissions verified by the system
• Visible sources for knowledge answers
• Confirmation before irreversible actions
• Repeatable tests and a change log

How to define a first version

Draw the flow and mark each step as exact or interpretive. Exact steps are built with code, rules and a database. Interpretive ones are candidates for AI. Then limit tools and data to the minimum. A first version can allow search and drafts without executing changes; that produces evidence before permissions are opened.

Measure each layer separately. If the summary is good but the query returns wrong data, it is not a writing problem. If classification is right but the system assigns it wrongly, another model will not fix it. This separation lets you improve without rebuilding everything and avoids blaming “the AI” for integration errors.