The Hardest Part of Building AI Products Is Rarely the AI

The hardest part of building AI products is rarely the AI itself.

It is defining the problem clearly enough that the system can solve it in a way that actually matters.

While working on an AI enabled behavioural planning platform, this became very clear.

The original idea seemed straightforward. Use AI to help people achieve long term goals. The assumption was simple. Better recommendations would lead to better outcomes. Suggest the right actions, generate smarter plans, and provide more intelligent insights.

Technically, the system worked.

But something was missing.

Users did not struggle with setting goals. Most people already knew what they wanted to achieve. The real difficulty was translating those goals into consistent daily behaviour.

Once that became clear, the product direction changed.

Instead of focusing on smarter recommendations, we focused on structure.

Turning goals into daily workflows.

Creating behavioural feedback loops.

Making progress visible and measurable.

Only then did the AI become genuinely useful.

That experience shaped how I think about AI products.

If the problem definition is vague, the model simply optimises the wrong thing.

And when that happens, the technology may improve while the product itself does not.

AI does not create product clarity. It amplifies whatever problem definition you give it.