Mary Fung
essayJune 10, 2026

Business people becoming builders

AI gives non-technical people more power to prototype and test. It does not remove the need to understand what makes a product real.

AI changes what non-technical people can make.

That should not be understated. A business person can now draft an interface, test a workflow, generate a simple app, analyze a dataset, write copy, compare competitors, and create a working demo before asking for technical help.

This is good. It makes ideas less abstract. It gives technical teams something more concrete to react to. It lets business teams test whether a problem is worth solving before they turn it into a project.

But there is a line between prototyping and building.

A prototype asks, "Could this be useful?"

A product has to answer harder questions. Who uses it? What data does it touch? What happens when it fails? Who maintains it? How is access controlled? What is logged? What is reviewed? What happens when more people use it? What is the cost of changing it later?

AI makes the first question easier. It does not make the later questions optional.

This is where surface-level building becomes risky. The interface works. The demo impresses. The workflow looks clean. Underneath, the system may have no real architecture, no security model, no data boundaries, no test strategy, and no owner.

The answer is not to tell business people to stop building. That would waste the new leverage.

The answer is to teach them the shape of real building.

They do not need to become engineers. They do need to understand the difference between a demo and a system. They need to know when data sensitivity matters, when a workflow needs review, when a tool is safe for personal use but not team use, and when an engineering partner should be brought in.

The best business people will become better partners to technical teams because they can show the problem instead of only describing it. They can bring a rough prototype, a clearer use case, a list of assumptions, and a sharper question.

That is useful.

The worst version is different. It is a business team generating fragile tools and asking engineering to bless them after the fact.

AI-enabled business work should make technical collaboration earlier, clearer, and more grounded. It should not make technical judgment an afterthought.

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