AI in the product: core, not decoration

Almost every product now has a button with a sparkle icon labelled "AI". Many of those buttons are forgotten after the first click. The reason is simple: the AI was attached to the product rather than made to work inside it.

That is the difference between a showpiece and a real advantage.

Signs of decorative AI

• You could remove it and nothing would change. If no user would miss it, it is decoration.
• The user has to know what to ask. An empty box saying "ask anything" hands the work back to the user.
• The output needs checking. If reviewing the result takes longer than doing the task by hand, nothing was gained.
• It isn't tied to a problem. It started from the technology, not the need.

Four questions for a real use case

1. Which user task gets shorter?

Good AI is invisible. The user doesn't say "I used AI"; they say "that was quick". Name a specific task that used to take ten minutes and now takes one.

2. Why does this task need AI?

Many problems are solved by a simple rule or a good search. AI is needed where the input is messy: natural language, documents, images, or a judgement with no fixed rule.

3. What happens when it is wrong?

Language models are sometimes wrong with complete confidence. Product design has to live with that:

• In low-risk tasks, an error is tolerable and correctable.
• In high-risk tasks, a person must approve.
• Either way, the user must be able to correct the result.

4. What knowledge does it draw on?

A general model knows nothing about your business. Real value appears when the model is connected to your own knowledge: catalogue, documents, records. Without that, answers are generic and sometimes invented.

Three patterns that work in practice

An assistant in the flow of work. The AI helps exactly where the user gets stuck, not on a separate page — like a sales assistant that answers inside the customer's own chat and completes the purchase.

Turning messy input into structure. The user writes in their own words and the system turns it into structured data. In Maseer, a citizen describes a legal matter in plain language and the system helps with drafting documents and analysing the case.

A personal companion. The AI gives guidance that fits the user's situation. In Rahro, an AI coach talks with the user as part of their daily routine.

In all three, remove the AI and the product is no longer the same product. That is what "core" means.

What it takes behind the scenes

Putting AI at the core brings engineering commitments:

• Controlling behaviour: tone, scope and what it must not say. (persona and tone)
• Measuring quality: a systematic way to know it works. (evaluating quality)
• Managing cost: every answer costs money. (cost control)
• A fallback path: what happens when the model can't. (human handoff)

Without these you have an attractive demo, not a product.

Where to start

1. Pick one specific, frequent user task.
2. Work out why it is hard or slow today.
3. Build the smallest version that does that one task better.
4. Test it with real users, not hand-picked examples.
5. If it works, expand.

It is the same logic as an MVP, with one intelligent component.

The takeaway

AI isn't a feature to add to a list. It is a way to solve problems that used to be unsolvable or too costly. Start from the problem, not the technology.

If you want AI doing real work in your product, see AI products.